This study aimed to determine whether dietary fat intake increased liver cancer risk in Chinese women from a prospective population-based cohort.A total of 72,704 Chinese women were followed up from the time of baseline recruitment (1996–2000) to the end of 2016. Dietary fat intake was calculated using a validated food frequency questionnaire. The Cox regression model was used to assess the hazard ratio (HR) and 95% confidence intervals (CI) for dietary fat intake and liver cancer risk.We identified 252 incident liver cancer cases out of 1,267,845 person-years during the overall follow-up time. Null associations, neither in quartiles nor per standard deviation (SD) increment, were detected between liver cancer risk and dietary total fat, fat subtypes and subtype ratios, and food sources. The HR (95% CI) of the 1-SD increment was 1.03 (0.90–1.17) for total fat, 1.06 (0.93–1.20) for saturated fat, 1.06 (0.93–1.21) for monounsaturated fat, and 1.00 (0.89–1.13) for polyunsaturated fat. Similar null associations were observed in stratification analyses according to body mass index and menopausal status.In our prospective cohort study, no significant association was observed in Chinese women between dietary fat and liver cancer risk, and in stratification and sensitivity analyses. A statistical analysis of incidence rates in China reported that liver cancer represented the seventh most common cancer in women, with 96,000 cases in 20153. Hepatocellular carcinoma (HCC), the major histological type of primary liver cancer, accounted for 75%–85% of total liver cancer cases4. Chronic hepatitis B and C virus (HBV and HCV) infections are the most frequent liver cancer causes, comprising 80% of all HCC cases globally5. Liver cancer also shows a sex-specific disparity in terms of incidence between male and female patients; males are 3 times more likely to develop HCC than females6. Hormones are suggested to be the most important cause for sex discrepancy in HCC carcinogenesis. Manieri et al.7 and Greten8 suggested reduced adiponectin levels were responsible for the increased incidence of HCC in men. Another study suggested that serum interleukin-6 (IL-6) levels can play a critical role in males9. IL-6 has been reported to be associated with adiponectin expression, and adiponectin levels may explain the sex-specific disparity in HCC carcinogenesis.Liver cancer is the ninth most common cancer in women worldwide, with 273,357 new cases in 202010. In recent years, new epidemiological evidence based on 2 large prospective cohorts conducted in the U.S. involving the Nurses’ Health Study (NHS) and the Health Professionals Follow-up Study (HPFS) has supported a change in research on liver cancer risk factors. By considering dietary patterns, these 2 cohort studies have shown that better adherence to the AHEI-2010 diet pattern might decrease the risk of developing HCC11. In terms of food or food groups, increased intakes of whole grains, cereal fiber, and bran were associated with a reduced risk of HCC12. In contrast, processed red meat intake might be associated with a higher risk of HCC, while poultry or fish intake might be associated with a lower risk of HCC13. Regarding specific nutrients, higher intakes of vegetable fat and polyunsaturated fat (PUFA) were found to be associated with lower risks of HCC14. These studies both confirmed that a plant-based low carbohydrate diet and dietary restriction of carbohydrates from refined grains were associated with a lower risk of HCC. In conclusion, exchanging carbohydrates for more plant-based fats and proteins, substituting plant fat and protein for carbohydrates, particularly refined grains, might decrease the risk of HCC15. The above mentioned evidence highlighted the importance of diet nutrients on liver cancer incidence and has stimulated studies of alimentary control and liver cancer risks. The NHS and HPFS also suggested that greater adherence to the type 2 diabetes mellitus (T2DM) diet was associated with a decreased risk of developing HCC among U.S. men and women16.Growing attention has been recently addressed towards the role of nutritional factors in the pathogenesis of common cancers. One explanation is that quantity and quality of nutrients and foods have deep impacts on the pro-inflammatory carcinogenic effects or anti-cancer immune responses18, endometrial cancer19, epithelial ovarian cancer20, and colorectal cancer21, but not for liver cancer. In this population-based prospective cohort study, we determined whether dietary intakes of total fat, fat subtypes, and subtype ratios and food sources increased liver cancer in Chinese women. We also aimed to clarify the effects of dietary fatty acids on liver carcinogenesis in women.Only 3 case-control and 4 cohort studies have been performed to determine the associations between dietary fat and liver cancer risk. These studies were conducted in the U.S., Europe, and Singapore (for Chinese residents), but further details of these associations had not been determined in mainland China. According to the differences in dietary cultures between China and other countries, and variations of eating habits for different sexes, it is worth characterizing the associations between dietary fat and liver cancer risk in Chinese women. Epidemiological evidence comparing diet and cancer incidence risk in women has been reported for breast cancer22. Participants in the SWHS were interviewed by trained interviewers to complete the baseline surveys, including demographic information, occupation, dietary habits, physical activity, smoking, alcohol consumption, and tea consumption, and the personal medical history of chronic diseases and family history of cancer were also recorded. Informed consent was obtained from each recruited individual. In this study, participants who met the following criteria were excluded: 1) diagnosis of cancer in situ during follow-up (n = 135), 2) cancer death with no cancer type or diagnosis date (n = 244), 3) cancer at baseline , 4) loss to follow-up shortly after enrollment (n = 3), 5) a cancer diagnosis that could not be confirmed (n = 67), 6) extreme values for total calorie intake  (n = 121), and 7) participants with missing data for any covariates of interest were also excluded (n = 68). After these exclusions, a total of 72,704 participants were finally included in the study. This study was approved by the Renji Hospital Ethics Committee of Shanghai Jiao Tong University School of Medicine (KY2019-197).The Shanghai Women’s Health Study (SWHS) is a population-based prospective cohort study, which enrolled 74,940 women between 40 and 70 years of age. The study was conducted in urban Shanghai, and the baseline surveys were conducted from 1996 to 200023. A total of 77 food items and food groups were included in the FFQ, which covered approximately 90% of commonly consumed foods in urban Shanghai in 1996. All study participants were interviewed twice to provide names and amounts of food they consumed over the past 24 h. The 24 h dietary recall interview was chosen to obtain detailed information about all food and beverages consumed by each participant in the past 24 h. An unannounced in-person interview was conducted to assess the frequency  and the quantity  of food consumption per time. Daily nutrient intakes were calculated from the FFQs using the nutrient content of each food based on the 2002 Chinese Food Composition Table24.A previously validated semi-quantitative food frequency questionnaire (FFQ) was adopted to evaluate dietary food intake within this cohort and was administered by an in-person interviewTotal fat was calculated from all food items included in the FFQ, except for cooking oils. Due to some limitations, only 4 leading food sources of dietary consumption were presented including soy, vegetable, fruit, and red meat. Because of the low content of dietary fat from vegetables, fruit, and soy, we summed them and presented them as “plant fat”. “Red meat fat” was set as a single group, according to the relatively higher consumption of fat in red meat. Finally, the remaining source of dietary fat was defined as “other fat”. Three food sources of dietary fat were therefore formally presented. Three fat subtypes including saturated fat, monounsaturated fat (MUFA), and polyunsaturated fat (PUFA) were classified. Continuous scales of total fat, fat subtypes, and food sources were graded into quartiles and created with dummy variables. Fat composition ratios including MUFA to saturated fat (M:S), PUFA to saturated fat (P:S), and MUFA and PUFA to saturated fat [(M + P):S] ratios were calculated to determine their associations with liver cancer incidence. We also determined the associations between dietary fat (including cooking oils) and liver cancer risk. However, because the added fat (oils) data were not as accurate as the dietary fat data as comprehensively discussed in the “limitation” section of this manuscript, these results are provided as a supplementary file.25.All cohort members were followed up for cancer occurrence through in-person surveys every 3–4 years. The research outcomes were annually linked with the databases of the Shanghai Cancer Registry, the Shanghai Vital Statistics Registry, and the Shanghai Resident Registry. In total, 5 follow-up surveys on outcomes were performed with response rates of 99.7% (2000–2002), 98.7% (2002–2004), 94.9% (2004–2006), 92.3% (2007–2010), and 91.1% (2012–2017). Person-year (PY) estimation for all cohort members started at baseline  or when a right-censoring event occurred , whichever came first. All liver cancer diagnoses were verified through home visits and rechecked with the medical reports from hospitals. The medical charts were reviewed by clinical and pathology experts. Cancers were coded according to the International Classification of Disease, Ninth Revision (ICD-9). Liver cancer was defined as a primary malignant tumor with an ICD-9 code of 155 defined as “malignant neoplasm of liver and intrahepatic bile ducts”Means and standard deviations (SDs) were calculated for continuous variables, while counts and proportions were presented for categorical variables. The Cox proportional hazard regression model was used to evaluate the association between dietary fat intake and liver cancer incidence by using hazard ratio (HR) and 95% confidence interval (CI). Follow-up time was determined as the underlying time metric. The Schoenfeld residual method was used to check the proportional hazard assumptions for all dietary fat items, but no violations were detected.26. Two Cox regression models were involved in this study. The first model was adjusted by age (continuous) and total calorie intake . The second model was a multivariate model, with the confounding variables including: age (continuous), body mass index 27, education , annual family income , occupation , menopausal status (yes/no), smoking , alcohol consumption , tea drinking , physical activity , total calorie intake , personal medical history of T2DM (yes/no), cholelithiasis (yes/no), chronic hepatitis (yes/no), and family history of liver cancer (yes/no). Trend tests were conducted across the energy-adjusted quartiles of dietary fats.HRs (95% CIs) of both quartile categories and the 1-SD increment of total fat, fat subtypes and subtype ratios, and fat food sources were estimated in the main analyses. The nutrient residual model was used to calculate energy-adjusted total fat and specific fatsvs. ≥ 24 kg/m2) and menopausal status (yes vs. no). The interactive effects of these factors were also determined. As previous evidence has indicated that smoking and alcohol consumption were associated with liver cancer development28, we should have conducted subgroup analyses of these parameters. However, due to the limited proportion of smokers and alcohol consumers in our SWHS , these analyses were not conducted. Three sensitivity analyses were conducted as follows: 1) to eliminate bias due to possible reverse causation in cohort studies, we excluded participants whose follow-up time was less than 2 years; 2) we excluded participants who had T2DM at baseline; 3) the nutrient density method26 was adopted to re-analyze the associations between all dietary fats and liver cancer risks.Prior exploratory subgroup analyses were conducted to evaluate the associations between total fat, fat subtypes and subtype ratios, and food sources and liver cancer in participants who had different characteristics such as BMI . All Table 1).A total of 252 new cases of female liver cancer were identified during over 1.26 million person-years of the follow-up period, from baseline to the end of 2016, with an average follow-up time of 17.44 years for each person. The cumulative incidence density and rate of liver cancer were 0.20/1,000 person-years and 3.47/1,000 participants during the follow-up time, respectively. Participants who were older and menopausal, and had a higher BMI, medical history of hepatitis, cholelithiasis, and T2DM, and family history of liver cancer were more likely to develop liver cancer. A decreased probability to develop liver cancer was detected in women who had consumed tea. Imbalanced discrepancies of education background, occupation, and family income were presented in non-cases and liver cancer cases. After categorizing participants according to the quartiles of dietary fat intake, a statistical significance was observed in all factors, except for family history of liver cancer (vs. lowest quartile of HR (95% CI) was 1.11 (0.79–1.57) for total fat, 1.25 (0.87–1.79) for saturated fat, 1.08 (0.75–1.55) for MUFA, and 1.02 (0.73–1.45) for PUFA after adjusting for multiple confounders, and the corresponding P-trend values were 0.611, 0.246, 0.788, and 0.816, respectively. Taking into consideration the M:S, P:S, and (M + P):S ratios, no significance was observed in both models; the highest vs. lowest quartiles of HR (95% CI) were 0.88 (0.62–1.25) for the M:S ratio, 0.94 (0.66–1.34) for P:S ratio, and 1.15 (0.80–1.66) for the (M + P):S ratio after adjusting by multivariate. Furthermore, null associations were detected in relation to the food sources of dietary fat. The highest vs. lowest quartile of HR (95% CI) were 1.04 (0.73–1.50) for plant fat, 0.83 (0.58–1.19) for red meat fat, and 1.16 (0.80–1.69) for other fat with P-trend values of 0.480, 0.615, and 0.442, respectively (Table 2). Null associations were detected in relation to cooking oil (Supplementary Table S1).Null associations were found in dietary intakes of total fat, specific fat subtypes, food sources, and liver cancer risks in both regression models. The highest vs. lowest quartile of HR (95% CI) was 0.54 (0.31–0.95) for the P:S ratio in overweight women and the second vs. lowest quartile was 0.40 (0.17–0.94) for red meat fat. No significant association was found between the stratified factors (Table 3).Exploratory subgroup analyses were conducted according to BMI and menopausal status in recruited women. After stratifying by both factors, null associations were detected in all fat items, despite the second vs. lowest quartile of HRs (95% CIs) were 0.60 (0.39–0.92) for the P:S ratio and 0.65 (0.44–0.96) for the red meat fat, with corresponding P-trend values of 0.827 and 0.732, respectively. Null associations were found after excluding participants with T2DM at baseline recruitment. After applying the density-energy method to adjust for dietary fat intake, we found null associations for all dietary fats except for the (M + P):S ratio; the second vs. lowest quartile of HR (95% CI) was 1.62 (1.10–2.37) with a P-trend value of 0.436 (Table 4).Finally, 3 strategies of sensitivity analyses were conducted. We found that after excluding participants who had less than 2 years of follow-up, similar results were observed except for the P:S ratio and red meat fat. The second In this prospective population-based cohort study, we assessed the associations between dietary fats and liver cancer incidence in Chinese women. Among 72,704 participants with 1,267,845 person-years of follow-up, 252 females were newly diagnosed with liver cancer. No significant association between dietary fats and liver cancer risks was detected, and even in the stratification and sensitivity analyses, similar results were observed.14 that performed a stratification analysis by sex, null associations were found between total fat and HCC risk, with decreased risks (ranging from 0.82–0.73) across quartiles in women in the NHS, whereas the risks (ranging from 0.97–1.23) were higher in our study. Taking the risk of 1-SD increment into consideration, the HR (95% CI) was 0.92 (0.72–1.19) for total fat in the NHS14 but 1.03 (0.90–1.17) in our SWHS. Discrepancies in risks were also observed when comparing the NHS14 and SWHS; the highest vs. lowest quartile of HRs were 0.73 vs. 1.11 for total fat, 0.56 vs. 1.25 for saturated fat, 0.71 vs. 1.08 for MUFA, and 0.59 vs. 1.02 for PUFA . In the European Prospective Investigation into Cancer and Nutrition cohort study (EPIC), the highest vs. lowest quartile of HRs (95% CI) were 0.74, 1.19, 0.53, and 0.86 for total fat, saturated fat, MUFA, and PUFA , respectively29. In the National Institute of Health-AARP (NIH-AARP) Diet and Health Study, the HR of 5% increase in energy was statistically significant with 1.10 in total fat, and 1.38 for saturated fat; and not significant with 1.18 for MUFA and 1.06 for PUFA in the whole population30. In the Singapore Chinese Health cohort, the highest vs. lowest quartiles of HRs were 1.26 for total fat, 1.40 for saturated fat, and 0.90 for MUFA, with all HRs being insignificant31. All the above cohort studies were conducted for both sexes and not specifically in women. The possible explanations for these combined analyses might be related to the similar risk factor exposures between men and women, as well as the limited number of incident cases in women.To date, studies on the associations between dietary fat and liver cancer risks are very limited, particularly in women. Similar to the results reported in the US cohort study14; whereas, it was far less in our SWHS (data not shown). In addition, the discrepancies between our study results and the EPIC and NIH-AARP may be attributed to sex differences. As indicated by the Shanghai Men’s Health Study32, dietary fat was associated with a higher risk of liver cancer in Chinese men. Furthermore, this inconsistency may partly be explained by different dietary habits. As shown in a recent review33, the main food sources of MUFA were meat and meat products in the NIH-AARP, and olive oil and nuts (~ 20%) in the EPIC study, and fats from olive oil and nuts were associated with lower risks of HCC. Comparing our results with those from other cohort studies, the Singapore Chinese Health Study reported the most similar conclusions, except for the HR for MUFA. Because all analyses from the EPIC, NIH-AARP, and Singapore Chinese Health Study were performed using both sexes, it might also imply that sex and ethnicity had some effects on the associations between dietary fat and liver carcinogenesis.Considering the disparities of results in women from the NHS and our SWHS, the causes may be attributed to food sources of dietary fat. In the NHS, vegetable fat was the second most common food source and had the potential ability to decrease HCC risk14. As presented in the clinical trial of the Dietary Intervention Study in Children34, the P:S ratio was inversely associated with breast density, which could serve as a strong risk factor for breast cancer in women. The n-3 and n-6 are the main types of PUFAs. A report from epidemiological evidence and an animal model suggested that increasing n-3 PUFAs and decreasing n-6 PUFAs and saturated fat intakes had potential effects on colon cancer treatment35. All the above studies showed the importance of fat composition ratios in cancer incidence and prevention. However, it also implied that not all PUFAs were good for health, and further detailed analyses should be conducted.Taking into account fat subtype ratios, the null significance was detected in associations between M:S, P:S, and (M + P):S ratios and liver cancer risk in the NHS or our SWHS. After stratifying using the BMI, a null association was observed in our SWHS, which was similar to the result that was obtained in the whole analyses of all participants from the NHS (women cohort) and the HPFS (men cohort)via the lingual and gastric enzymes, and after crude emulsion with the contribution of gastric peristalsis, fine lipid droplets are formed, then they enter into the duodenum, undergo the effects of bile acid and pancreatic enzymes, and then the chemical and physical structures are changed to help digestion36. Shen et al.37 studied the influences of dietary fat composition (high fat vs. low fat) on intestinal microbiota and showed that the subsequent fermentation products affected liver function. In C57BL/6J mice, a high-fat diet could induce the progression of non-alcoholic fatty liver disease (NAFLD) to non-alcoholic steatohepatitis and subsequently to HCC38. The n-3 and n-6 PUFAs are the main types of PUFA, and different effects for liver diseases have been previously reported. As reported by the NHS and HPFS studies, high dietary intake of PUFAs was associated with a reduced risk of HCC in U.S. adults14. Another report suggested that the n-3 PUFAs seemed to reduce HCC risk, even among subjects with HBV/HCV infection39. However, n-6 PUFAs promoted inflammatory responses, leading to the increased production of inflammatory cytokines by liver Kupffer cells and the activation of NF-κB, thus aggravating liver inflammation and fibrosis40. Collectively, specific fat compositions may have different effects on hepatocellular carcinogenesis, which should be explored in-depth in future studies.Dietary fat digestion starts in the oral cavity by lingual lipase exposure. The digestion continues in the stomach This is the first cohort study that evaluated the associations between dietary fat and liver cancer risk in Chinese women. The advantages of this study were related to the large-scale, population-based, and prospective cohort study design. This study represented a prospective cohort study with the largest number of liver cancer incident cases in women that had currently been reported. In addition, multiple confounding factors were included in this study, such as personal medical histories of hepatitis, cholelithiasis, T2DM, and family history of liver cancer, which could be potential causes of liver cancer. Meanwhile, this study presented some additional limitations as well. First, no dietary alterations during follow-up were considered, and baseline FFQ surveys were adopted for the analyses. In addition, the measurement bias of dietary consumption may have led to biased results. Furthermore, as the main source of dietary fat, oil consumption was not exactly calculated. For the shortage of early FFQ design, monthly family but not daily individual oil consumption data were collected. Considering that the oil fat data were crude and not as accurate as the dietary fat data, we included the combined results of dietary fat and oil fat in the supplementary material to support our conclusions. Another limitation of our study was the lack of recording HBV or HCV infection, which were the major causes of HCC. However, as one of the main outcomes of HBV or HCV infection, the medical history of chronic hepatitis has been calibrated in the multivariate model, which could, to some extent, reduce this bias.In summary, our results showed no significant association between total dietary fat, specific fat subtypes, and food sources, and liver cancer risk in Chinese women. Due to the limited number of related studies, more epidemiological evidence should be obtained to further investigate the role of dietary fat intake in liver carcinogenesis in women.Click here for additional data file.


The epidemiologic evidence from observational studies on breast cancer risk and phthalates, endocrine disrupting chemicals, has been inconsistent. In the only previous study based on pre-diagnostic urinary phthalates and risk of breast cancer, results were null in mostly white women.molar) and adjusted for creatinine and potential confounders.We examined the association between pre-diagnostic urinary phthalates and breast cancer in a nested case-control study within the Multiethnic Cohort (MEC) study, presenting the first data from five major racial/ethnic groups in the USA. We measured 10 phthalate metabolites and phthalic acid, using a sensitive liquid chromatography mass spectrometry assay on 1032 women with breast cancer  and 1030 matched controls. Conditional logistic regression was used to examine risk with individual metabolites and ratios of primary  to secondary ; MEOHP, mono(2-ethyl-5-oxohexy)) metabolites of di-2-ethylhexyl phthalate (DEHP), a widely used plasticizer. In addition, we investigated risk associations with high (∑HMWP) and low molecular weight (∑LMWP) phthalates, as well as total phthalates which included high and low molecular weight phthalates with phthalic acid (∑LMHMPA) or without phthalic acid in molar ratios (∑LMHMPtrend = 0.05). Risk among Native Hawaiian women increased with exposures to eight of ten individual phthalates and total phthalates . In analysis by hormone receptor (HR) status, exposure above the median of ∑LMWP was associated with an increased risk of HR-positive breast cancer  while above the median exposure to phthalic acid was associated with an increased risk of HR-negative breast cancer .Among all women, breast cancer risk was higher for those in tertile 2 and tertile 3 of primary to secondary metabolites of DEHP (MEHP/(MEHHP + MEOHP)) in comparison to those in tertile 1; the respective odds ratios were 1.32 (95% CI 1.04–1.68) and 1.26 (95% CI 0.96–1.66) (Further investigations of suggestive associations of elevated breast cancer risk with higher ratios of primary to secondary metabolites of DEHP, and differences in risk patterns by race/ethnicity and HR status are warranted. Phthalates are industrial chemicals that are present in numerous consumer products and solvents, as additives, and plasticizers –3, and hPhthalates have been implicated to influence developmental and reproductive processes and exert carcinogenic effects , 8–10. HTo further investigate the role of pre-diagnostic urinary phthalates and breast cancer risk, we conducted a nested case-control in the Multiethnic Cohort (MEC) study  that incThe MEC is a large prospective cohort that included 96,810 men and 118,441 women aged 45–75 years from five different racial/ethnic groups  living in Hawaii and California (primarily from Los Angeles County) at enrollment between 1993 and 1996 . Particin = 1032) were diagnosed with incident breast cancer from 2001 through 2014 after urine collection; 22% were in situ and 78% were invasive cancers. The mean time between urine collection and breast cancer diagnosis was 5.5 years (standard deviation (SD) = 3.3). For each case, we selected one control, who was alive and free of breast cancer at the age of breast cancer diagnosis, and individually matched controls to cases on area , birth year (± 1 year), race and ethnicity , urine type , date (± 1 year) of urine collection, hours of fasting , and time of blood draw (± 2 h). As described previously, the overnight urine collection started between 5:00 pm and 9:00 pm (depending on participant) and included all urine passed during the night and the first morning urine sample covering a period of 12 h [In 2001–2006, a prospective biorepository was established by collecting pre-diagnostic urine and blood specimens from 67,594 MEC cohort members . A short of 12 h . ControlPhthalate metabolites  and phthalic acid measurements were conducted at the University of Hawaii Cancer Center Analytical Biochemistry Shared Resource. Dr. Adrian Franke supervised the day-to-day activities and quality assurance and quality control of phthalate measurements using state-of-the-art sensitive isotope-dilution orbitrap-based high-resolution accurate-mass liquid chromatography mass spectrometry (LCMS) assay after enzymatic hydrolysis and liquid-liquid extraction , 20. In tertiles) and 95% confidence intervals (CIs) were the primary statistics of interest, and inference was based on the Wald test. We found no evidence of a nonlinear relationship  between phthalates and risk using restricted cubic splines (data not shown). Therefore, log-transformed phthalate variables were used as trend variables to test for dose-response relationships. Models were adjusted for potential confounders that were not matching factors  via indicator variables for tertiles of propensity scores for exposure to phthalates [ρ1 + 2 × ρ2 + 2 × ρ3, where ρi is the model-based probability of exposure to tertile i. Heterogeneity of the associations by race/ethnicity was assessed by a global test of the interaction terms between race and the phthalate trend variable. We repeated subgroup analyses for hormone receptor-positive  and hormone receptor-negative  cancer, as well as BMI and use of hormone therapy at urine collection. In addition, waist-hip ratio (WHR) was obtained in a follow-up questionnaire and was available for 333 cases prior to diagnosis and 946 control women; we explored risk association results by WHR. We also conducted a sensitivity analysis by restricting to invasive cases only (n = 798) and by lag time between time of urine collection and breast cancer diagnosis (≤ 5 years versus > 5 years), as well as excluding 187 cases that were diagnosed within 2 years of urine collection to minimize the potential effect of pre-diagnostic breast cancer on phthalate levels. Associations with P < 0.05 were considered statistically significant and with 0.05 ≤ P < 0.10 were considered suggestive. The correlations among phthalate metabolites and phthalic acid were examined using Spearman’s rho.We conducted conditional logistic regression, with the matched sets as strata (1028 pairs and 2 triplets), and modeled phthalate variables as tertiles using selected cutoff points based on the distribution among all controls. Odds ratios . We divided the concentration of each metabolite by its molecular weight to obtain the molar equivalent  and then summed the concentrations to get total μm/L of metabolites per creatinine unit.The short-branched phthalates, DMP and DEP, are excreted in urine as the unconjugated monoesters, MMP and MEP, respectively. The longer-branched phthalates such as DEHP are hydrolyzed first to MEHP and subsequently metabolized to MEHHP, MEOHP, MECPP, and other oxidative metabolites , 27, 28 2), but were otherwise comparable.The majority of participants (86%) were from Hawaii, who donated overnight  or first morning urines , whereas only first morning samples  were collected in Los Angeles County (Table P < 0.05) whereas the DEHP metabolites were more strongly correlated (rho’s were 0.5 to ~ 0.8). The correlations between phthalic acid and the 10 phthalates ranged from 0.15 to 0.44. . In contrast, risk in all women was positively associated with high ratios of MEHP/MEOHP (Ptrend = 0.04), MEHP/(MEOHP+ MEHHP) (Ptrend = 0.052), and high MEHP% (Ptrend = 0.092); the range of ORs associated with exposure in the upper tertile was 1.18 to 1.26 compared to those in the lowest tertile of exposure. These patterns were apparent among white women (Table Ptrend = 0.05), MEHP (Ptrend = 0.01), MEHHP (Ptrend = 0.09), MECPP (Ptrend = 0.06), and phthalic acid (Ptrend = 0.03); ORs ranged from 1.94 to 2.73 for Native Hawaiians in the upper tertile of phthalate exposures compared to those in the lower tertile. In contrast, the individual metabolites were not associated with risk in African Americans and Latinos combined or in Japanese Americans. Among whites, below unity ORs were found for MBzP (Ptrend = 0.05) and for all three DEHP secondary metabolites, with strongest inverse trends for MEOHP (Ptrend = 0.06).The risk of breast cancer in all women combined tended to decrease with increasing exposure to MBzP , ∑LMHMmolar (Ptrend = 0.03), ∑LMWP (Ptrend = 0.10), and ∑HMWP (Ptrend = 0.10). Native Hawaiian women in the upper two tertiles of ∑LMHMPA exposure showed a significant 2.4- to 2.6-fold higher risk than their counterparts in the lowest tertile of exposure.Breast cancer risk in all women combined was not associated with ∑DEHP and ∑HMWP but exposure to ∑LMWP, ∑LMHMPA, and ∑LMHMn = 694) and HR− (n = 96) invasive breast cancers in all women combined , but this positive association was also observed for HR− breast cancer  (Pheterogeneity = 0.95). In contrast, the risk of HR− breast cancers was increased in association with phthalic acid exposure  but not HR+ breast cancer  (Pheterogeneity = 0.08). Above the median exposure to ∑LMHMPA was also associated with HR− breast cancer ; this increased risk was observed in whites, Native Hawaiians, and African Americans and Latinos combined, but it was particularly prominent among Native Hawaiian women   but not among those with low (< 0.854) WHR  (Pheterogeneity = 0.01).Phthalate-breast cancer associations did not differ by years of follow-up after urine collection, or between all (invasive and in situ combined) versus invasive breast cancers only (data not shown). There were also no suggestive differences in breast cancer associations with phthalate exposures by use of hormone therapy or BMI at urine collection (data not shown). However, in the subgroup of women with data on WHR, breast cancer risks increased among those with high ≥ 0.854) WHR in association with exposure to MEHP%, ratio variables of MEHP to secondary metabolites of DEHP, phthalate acid, and ∑LMHMPA , highlight the challenges in studying these ubiquitous chemicals in relation to breast cancer risk. The suggestive differences in associations between HR+ and HR− breast cancer and among those with high WHR warrant confirmation in future studies.The most consistent findings in all women combined were the inverse associations with MBzP, the increased risks associated with high MEHP%, and high ratios of DEHP metabolites, namely MEHP to MEOHP and MEHHP. The significant inverse association between MBzP exposure and risk is consistent with results observed in Mexico  and amonOur findings on the individual DEHP metabolites and ratios of primary to secondary DEHP metabolites suggest potential racial/ethnic differences in exposure and/or metabolism. Exposures to all four DEHP metabolites were inversely associated with risk in whites but were positively associated with risk in Native Hawaiians; the results in whites are supportive of the null/inverse associations reported previously in whites , 14, whimolar appeared to be stronger for HR− than HR+ breast cancers but this was based on only 96 HR− breast cancers. Although these risk associations were particularly strong among Native Hawaiians, we are cautious in our interpretation because of the very wide confidence intervals (Supplementary Table n = 96) and combined previous studies on urinary phthalates .molar) while the associations were less consistent in the other racial/ethnic groups. We are not aware of previous studies on exposures of phthalates among Native Hawaiians and Japanese Americans in the USA, but the divergent risk patterns may be due to differences in their use of phthalate containing products as suggested by racial/ethnic differences in profiles of individual urinary phthalates  to secondary DEHP metabolites  emphasize the need of further investigation of not only the individual metabolites, but also ratios of the metabolites as well as summation of these metabolites. While additional studies with pre-diagnostic samples are needed to fill our gaps in understanding the effects of these ubiquitous chemicals, continued efforts to reduce exposure burden to phthalates are needed.Additional file 1: Supplement Table 1. Molecular weight of phthalate metabolites and phthalic acid and samples with lower limit of detection (LLOD), and geometric mean (95%CI) of phthalate metabolites and summary variables of phthalates (μg/g creatinine) by study area and by race/ethnicity. Supplementary Table 2. Pairwise Spearman’s correlations between individual phthalate biomarkers and summation variables among controls. Supplementary Table 3. Associations of risk (OR and 95% CI) a of hormone receptor positive (HR+) and HR negative (HR-) invasive breast cancers with summary phthalate exposures by race/ethnicity. Supplemental Table 4. Distribution of covariates by tertiles of total phthalate exposure (∑LMHMPA).


How well do breast cancer risk prediction models that incorporate polygenic risk scores (PRSs) and nongenetic risk factors perform for Asian women?In this diagnostic study of 126 894 women, a PRS including 111 genetic variants was developed and tested using data from a prospective cohort study. The PRS was significantly associated with breast cancer risk, and adding 7 nongenetic risk factors improved the model’s accuracy.These findings support the utility of prediction models in identifying Asian women with high risk of breast cancer. This diagnostic study develops and tests breast cancer risk prediction models for Asian women, incorporating polygenic risk scores (PRSs) and nongenetic risk factors. Polygenic risk scores (PRSs) have shown promise in breast cancer risk prediction; however, limited studies have been conducted among Asian women.To develop breast cancer risk prediction models for Asian women incorporating PRSs and nongenetic risk factors.This diagnostic study included women of Asian ancestry from the Asia Breast Cancer Consortium. PRSs were developed using data from genomewide association studies (GWASs) of breast cancer conducted among 123 041 women with Asian ancestry (including 18 650 women with breast cancer) using 3 approaches: (1) reported PRS for women with European ancestry; (2) breast cancer–associated single-nucleotide variations (SNVs) identified by fine-mapping of GWAS-identified risk loci; and (3) genomewide risk prediction algorithms. A nongenetic risk score (NGRS) was built, including 7 well-established nongenetic risk factors, using data of 416 case participants and 1558 control participants from a prospective cohort study. PRSs were initially validated in an independent data set including 1426 case participants and 1323 control participants and further evaluated, along with the NGRS, in the second data set including 368 case participants and 736 control participants nested within a prospective cohort study.Logistic regression was used to examine associations of risk scores with breast cancer risk to estimate odds ratios (ORs) with 95% CIs and area under the receiver operating characteristic curve (AUC).A total of 126 894 women of Asian ancestry were included; 20 444 (16.1%) had breast cancer. The mean (SD) age ranged from 49.1 (10.8) to 54.4 (10.4) years for case participants and 50.6 (9.5) to 54.0 (7.4) years for control participants among studies that provided demographic characteristics. In the prospective cohort, a PRS with 111 SNVs developed using the fine-mapping approach (PRS111) showed a prediction performance comparable with a genomewide PRS that included more than 855 000 SNVs. The OR per SD increase of PRS111 score was 1.67 , with an AUC of 0.639 . The NGRS had a limited predictive ability . Compared with the average risk group (40th-60th percentile), women in the top 5% of PRS111 and NGRS were at a 3.84-fold  and 2.10-fold  higher risk of breast cancer, respectively. The prediction model including both PRS111 and NGRS achieved the highest prediction accuracy .In this study, PRSs derived using breast cancer risk–associated SNVs had similar predictive performance in Asian and European women. Including nongenetic risk factors in models further improved prediction accuracy. These findings support the utility of these models in developing personalized screening and prevention strategies. Hence, a cost-efficient, population-specific breast cancer screening strategy for Asian women is needed.Breast cancer is the most commonly diagnosed malignant neoplasm among women worldwide.10 However, most breast cancer risk loci were identified in GWASs conducted in European descendants.11 Multiple studies have attempted to aggregate the associations of risk SNVs identified by GWAS as polygenic risk scores (PRSs) to stratify women into different breast cancer risk groups.16 Most PRSs for breast cancer were established specifically in women of European ancestry, and among them, a 313-SNV PRS showed the highest predictive ability, with an area under the receiver operating characteristic curve (AUC) of 0.630 to 0.642.12 To our knowledge, few studies of breast cancer PRSs for Asian women have been conducted, and limited prediction accuracy was observed.21 A recent study showed that the 313-SNV PRS performed better than PRSs derived from Asian data.21 However, in that study, the Asian-specific PRSs were derived using a limited number of SNVs.In 2006, we established the Asia Breast Cancer Consortium (ABCC) to identify genetic risk variants, including single nucleotide variants (SNVs), associated with breast cancer risk through genomewide association studies (GWASs). Approximately 50 risk loci were identified using Asian data alone or in combination with data from European descendants.22 Several studies have explored the potential of incorporating PRSs and nongenetic risk factors in improving predictive accuracy.25 Among them, a recent study among European women revealed that the 313-SNV PRS was more predictive than a model including 16 nongenetic risk factors, and the best risk stratification performance was achieved when PRS and nongenetic factors were combined.22 However, to our knowledge, similar studies have rarely been carried out among Asian women. In the present study, we aimed to develop and validate PRSs for Asian women using data from the largest GWAS of breast cancer conducted among Asian women that we are aware of and to further evaluate the performance of risk prediction models, including both PRSs and nongenetic risk factors.Nongenetic factors are also associated with breast cancer risk.10 We divided these data sets to a training set (18 650 case participants and 104 391 control participants) for PRS derivation and a validation set (1426 case participants and 1323 control participants) for prediction performance evaluation  reporting guideline.In this diagnostic study, the PRS development data sets included GWAS data of 20 076 women with breast cancer and 105 714 control participants of Asian ancestry from the ABCC . Detaile10 and in the eMethods in the Detailed descriptions of genetic data are provided in our recent publicationk and βk represent the allelic dosage and corresponding weight of SNV k, and n is the number of SNVs used.We applied 3 approaches to develop PRSs, as described briefly here and in detail in the eMethods in the 12 Most recently, this PRS was updated by adding 17 novel breast cancer–susceptibility SNVs.11 Of these 330 SNVs, 263 could be found in our validation set and prospective test set, and 3 PRSs  were derived using weights of these SNVs from data from women with European ancestry included in the Breast Cancer Association Consortium (BCAC-European),11 data from women with Asian ancestry in our training set  version 1.93.2.29 Within each locus, a COJO threshold of P < 1 × 10−5 was used to identify independently associated SNVs and reestimate weights of their association with breast cancer risk. Some loci were ineligible for fine-mapping because no SNVs within them had an association with breast cancer risk at P < 10−5 in our training set. Based on fine-mapping, 3 PRSs were derived using (1) all SNVs selected from fine-mapping; (2) SNVs selected by fine-mapping and showing consistent association directions with P < .05 in the BCAC-European data; and (3) SNVs in the second PRS and lead SNVs from loci that were ineligible for fine-mapping but showed P < .05 in our training set , LDpred2 (version 1.4.4), and PRS-CSx  were used to derive PRSs using data from the training set. Details of these algorithms can be obtained elsewhere.17 Data from 1974 women (416 case participants and 1558 control participants) from the SWHS, but independent from those in the prospective test set, were used to estimate the weights of the association of these 7 nongenetic factors and the interaction term with breast cancer risk , waist-to-hip ratio (WHR), benign breast disease, age at menarche, age at first live birth, and family history of breast cancer. The interaction between BMI and menopause status was included in the model, as BMI shows a different association with breast cancer risk by menopausal status.12 an integrated risk prediction model (IRPM) was built including a PRS and the NGRS to predict breast cancer risk . Logistic regression was used to evaluate ORs and 95% CIs per SD increase in these risk scores. Prediction performance was measured by AUCs and 95% CIs using the R function pROC:roc.33For the PRS showing the highest prediction accuracy in our prospective test set and the reported European PRS,12 A total of 10 207 Chinese women (5087 case participants and 5120 control participants) from the Shanghai Breast Cancer Genetic Study and BCAC-Asians  with breast cancer and 106 450 control participants, were included in this study . The ABCIn general, all PRSs had a slightly higher mean value among case participants than among control participants, while the SD value was similar between both groups, in both the case-control validation set and the prospective test set  compared with PRS263-meta (60 [37-83] vs 44 [20-71])  .Among the most predictive PRS derived by each genomewide risk prediction algorithm, the PRS derived from PRS-CSx was the best-performing PRS in both the validation and the prospective test set, with AUCs of 0.613  and 0.642 , respectively . The OR In the training set for NGRS, compared with control participants, case participants, on average, had a younger age at menarche, older age at first live birth, and higher BMI  and an AUC of 0.565  . IncorpoAmong the 10 207 Chinese women from the ABCC data sets, a dose-response association of breast cancer risk with percentiles of PRS111 and PRS263-meta was observed A and B. Leveraging large GWAS data sets of women with Asian ancestry, we developed PRSs for breast cancer risk using 3 approaches and validated their prediction performance in an independent prospective test cohort. PRS111, derived using the fine-mapping approach, was the best-performing PRS in this study . The prediction model incorporating PRS111 and 7 nongenetic risk factors achieved a further improved prediction accuracy .21 The 313-SNV PRS was significantly more predictive  than any of the 5 Asian-specific PRSs .21 However, because most of the breast cancer risk variants were identified in GWAS conducted among women with European ancestry, the Asian-specific PRSs were derived using a limited number of SNVs (from 5 to 51) in that study. In the present study, the most predictive PRS based on these 313 SNVs, PRS263-meta, underperformed the PRS111, which was derived entirely using Asian data. The prediction ability of PRS111 in Asian women  is almost equivalent with that of the 313-SNV PRS in women with European ancestry .12 The prediction ability of our PRS111 is also comparable with that derived using genomewide data in a recent study conducted among Asian women .34A recent study compared the predictive ability of 5 Asian-specific PRSs with that of the 313-SNV European PRS in Asian women.38 Generally, including nongenetic risk factors could lead to improved prediction accuracy, although the magnitude of improvement is relatively small. In a recent analysis using data from a prospective cohort of Dutch women, the 313-SNV European PRS was found to have an AUC of 0.636.37 Incorporating this PRS with 9 nongenetic risk factors improved the AUC to 0.653,37 similar to the level achieved in our study. In 2010, we built an Asian-specific prediction model incorporating a 12-SNV PRS and 5 nongenetic risk factors, which showed an AUC of 0.629 among Chinese women.17 In the present study, IRPM111, the prediction model including PRS111 and the NGRS, outperformed both PRS111 and the NGRS in predicting breast cancer risk.Most studies of prediction models incorporating PRS and nongenetic risk factors were carried out among European descendants.The strengths of this study include the use of large GWAS data sets as the training set to improve the accuracy of estimating weights of breast cancer–associated SNVs for PRS construction. We performed fine-mapping analyses to identify additional breast cancer risk SNVs specifically for Asian women. Because most of the breast cancer–associated SNVs were identified in populations with European ancestry and there are differences in genetic architectures between Asian and European populations, we believe that this approach is necessary to construct a PRS that is more appropriate for Asian women. In addition, we demonstrated the ability of PRS-CSx in developing more predictive PRSs than other genomewide prediction algorithms. Finally, the availability of both genetic and nongenetic risk factors data made it possible to establish and validate prediction models incorporating PRSs and nongenetic risk factors.This study has limitations. The sample size of our prospective test set was relatively small, which led to relatively wide 95% CIs for ORs and AUCs. Although the PRSs used for relative risk estimation were externally validated, there might still be some potential for overfitting in risk estimation. We found that all PRSs had better prediction performance in our prospective test set than in our case-control validation set. Reasons for this observation are unclear. Case and control participants were from 2 different studies in the case-control validation set, which could reduce the comparability between the case and control groups. However, the patterns of associations uncovered in both validation tests were similar. Additionally, including additional nongenetic factors, such as mammographic density, could improve model performance; however, such data were not available in the present study.Using data from the largest GWAS conducted in Asian women, we demonstrated that PRSs derived using breast cancer–associated risk SNVs show similar performance in predicting breast cancer risk in Asian and European descendants. Including known nongenetic risk factors in the models could further improve the accuracy of risk prediction. Our study provides strong support for the utility of risk prediction models in developing personalized screening and prevention strategies.


In contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet.A large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004–2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women’s Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy.E/O) ratios of 1.01 , 0.94–1.09) and 0.94 , respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634  and 0.585  in the CKB and the SWHS, respectively.During a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (Based only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals’ awareness and aid risk-stratified screening and prevention strategies.The online version contains supplementary material available at 10.1186/s13058-021-01439-2. Breast cancer is the most common and rapidly increasing female malignancy in China . CompareFollowing the pioneering model derived by Gail et al. in 1989 , multiplIn this study, we used data from a large nationwide prospective cohort, the China Kadoorie Biobank (CKB), as well as national age- and residence -specific invasive breast cancer incidence rates and non-breast cancer mortality rates to develop a risk prediction model considering competing risk, and used data from another large prospective cohort, the Shanghai Women’s Health Study (SWHS), to independently validated the model.Data from the CKB, a large-scale prospective study, was used to derive the relative risk (RR) model . The stuIncident cases of invasive breast cancer and mortality were identified chiefly through the linkage with the national health insurance claim database and disease registries, supplemented with local residential records and annual active confirmation. The International Classification of Diseases, 10th Revision was used to code all breast cancer (C50) by trained staff who were blinded to baseline information. We excluded women who had missing data for any reproductive factors or who provided implausible data on age at menarche or age at first live birth. We further excluded women who reported previous histories of cancer at baseline or had missing data for body mass index (BMI), leaving 300,824 women in the analysis.Independent data from the SWHS was used to externally validate the derived model based on CKB data . In brieAt baseline, all information involved in the current analysis was collected through in-person interviews and anthropometric measures following standard protocol. Incident breast cancer cases (ICD-9 code 174) were identified by a combination of active re-surveys every 2 to 4 years and annual linkage with the Shanghai Cancer Registry and the Shanghai death certificate registry. The cancer diagnosis was verified through home visits and reviews of medical charts obtained from the hospitals where the patients were diagnosed. Applying the same exclusion criteria as the CKB data, 73,203 SWHS participants were included.Participants were considered at risk from the enrollment to the diagnose of invasive breast cancer, death, loss to follow-up, or Dec 31, 2016, whichever came first. Cox proportional hazards model was used to estimate the hazard ratios as the metric of relative risk (RR) for each variable in the model, with age as the timescale, and stratified jointly by 10 study sites and age at enrollment in a 5-year interval .P < 0.05 together in the model. Variable selection was repeated using stepwise backward elimination, which yielded the same result. The variables were converted to ordinal variables if their RRs were proportional to levels and no evidence of nonlinearity was detected using fractional polynomials. All first-order interactions were tested one by one using the likelihood ratio test comparing models with and without the interaction term. For all variables in the final model, the lowest risk category was regarded as the reference group, to facilitate population attributable risk (PAR) computation.We initially considered the following variables to construct the model: education, tobacco smoking, alcohol drinking, total physical activity, consumption of soybean, BMI, height, first-degree family history of overall cancer, menopausal status, number of live birth, age at menarche, total duration of breastfeeding, and usage of contraceptives. Because we did not collect information on family history of breast cancer, we used the family history of overall cancer as a surrogate. The continuous variables were converted to categorical variables to reduce overfitting. Cutoffs of BMI were chosen according to the well-established criteria for Chinese . And, thGiven the higher incidence rate of breast cancer in urban areas than that in rural areas, we also tempted to build residence -specific models, i.e., variable selection and predictors coefficients were separately done in urban and rural datasets. Interestingly, we found that the relative risks were similar between urban and rural areas, and there was no significant interaction between area and risk factors  is the age-specific hazards of developing breast cancer and h2(t) is the age-specific hazards for competing causes at age t. We can estimate h1 = h10(t)RR(x) as the product of age-, residence-specific baseline hazards h10(t) and relative risks RR(x) from the relative risk model described above. RR(x) are age-constant for all risk factors x except for BMI, which has two different RR for < 50 and ≥ 50 years old.where h10(t), by multiplying age-specific incidence rates in 2014 from the National Central Cancer Registry of China (NCCR) , 0.94 [0.90–0.98]; SWHS: 0.67 [0.63–0.71]) and for most subgroups defined by the predicted risk deciles  the risk of women in the urban area in the SWHS. To have a good model generalization, we have applied China’s national age and residence  rates in the absolute risk calculation, instead of regional rates like previous studies in China –15. Ther31–2.51) , 8. ThisIn the external validation, we found a moderate AUC of 0.585, which was better than or equivalent to those of the AABCS model , 25. MatThe development of the CKB risk prediction model has several public health implications. First, our model, with the moderate discriminating ability and good calibration, can facilitate allocation of preventive resources under monetary and medical constraints and aid risk-based screening strategies . China’sOur study has several strengths. We used data from the largest nationwide prospective cohort study in China to develop the relative risk model, augmented with China national incidence and mortality rates, and validated in another large prospective cohort study. These methods ensure our model to be robust and potentially generalizable to both rural and urban areas in China. Also, all predictors in the model are non-invasive, easy to measure at low cost, which makes the model easily applicable to the general population. We plan to develop an online risk calculator to promote its use.. The history of benign breast diseases was not collected in the CKB and we think it might not be reliably collected in the general Chinese population. Second, cumulative evidence showed heterogeneous associations of epidemiological factors with estrogen receptor (ER)-specific breast cancer though some factors are common for both ER-positive and ER-negative breast cancers [However, one must be aware of limitations of our study. First, several established risk factors were not included in the model. Although several studies included alcohol –31, the  cancers , 33. We  cancers , our modIn summary, we have developed and validated a breast cancer risk prediction model that only relies on non-laboratory predictors. The model has a good calibration and a moderate discriminating capacity. The model may serve as a useful tool to raise individuals’ awareness and to identify women who may benefit from breast cancer screening in China. To improve the model discriminating accuracy, further studies can add genetic and epigenetic predictors for breast cancer, as well as mammographic density. Validation of our model in other regions of China, especially rural areas, is also desirable to evaluate the robustness of the CKB model.Additional file 1. Show comparison of Age-adjusted RR (95% CI) for breast cancer among women in urban and rural areas of China Kadoorie Biobank.Additional file 2. Show age- and residence-specific breast cancer incidence rates and mortality rates of non-breast cancer per 100,000 person-years by data sources.Additional file 3. Show age- and site-adjusted RR (95% CI) from the derivation subcohort and the whole China Kadoorie Biobank.Additional file 4. Show expected and observed number of breast cancer in the test subcohort of China Kadoorie Biobank and Shanghai Women’s Health Study using the corresponding local rates.Additional file 5. Show performance of the breast cancer prediction model across different predicted risk cutoffs in the China Kadoorie Biobank.Additional file 6. Show performance of the breast cancer prediction model across different predicted risk cutoffs in the Shanghai Women's Health Study.Additional file 7. Show validation of the Asian America Breast Cancer Study model for predicting individual breast cancer risk in China Kadoorie Biobank and Shanghai Women's Health Study.


Postmenopausal breast cancer is the most common obesity-related cancer death among women in the U.S. Insulin resistance, which worsens in the setting of obesity, is associated with higher breast cancer incidence and mortality. Maladaptive eating patterns driving insulin resistance represent a key modifiable risk factor for breast cancer. Emerging evidence suggests that time-restricted feeding paradigms (TRF) improve cancer-related metabolic risk factors; however, more flexible approaches could be more feasible and effective. In this exploratory, secondary analysis, we identified participants following a low-glucose eating pattern (LGEP), defined as consuming energy when glucose levels are at or below average fasting levels, as an alternative to TRF. Results show that following an LGEP regimen for at least 40% of reported eating events improves insulin resistance (HOMA-IR) and other cancer-related serum biomarkers. The magnitude of serum biomarkers changes observed here has previously been shown to favorably modulate benign breast tissue in women with overweight and obesity who are at risk for postmenopausal breast cancer. By comparison, the observed effects of LGEP were similar to results from previously published TRF studies in similar populations. These preliminary findings support further testing of LGEP as an alternative to TRF and a postmenopausal breast cancer prevention strategy. However, results should be interpreted with caution, given the exploratory nature of analyses. High obesity rates among women in the United States and worldwide are leading to a continued rise in obesity-related cancers, most notably postmenopausal breast cancer , which iIn a seminal Phase II feasibility study of a 6-month intensive lifestyle intervention conducted in postmenopausal overweight and obese women at increased risk for breast cancer, Fabian et al. demonstrated that weight losses of at least 10% effectively reduced serum biomarkers, including insulin resistance (HOMA-IR), at a magnitude that favorably modulated benign breast tissue biomarkers ,13. WhilIntermittent fasting paradigms have become increasingly popular among researchers and health-conscious individuals. These eating paradigms aim to align meal-timing with circadian rhythms. Restricting the consumption of energy intake to a daily timespan of 4–10 h  enhances synchronization between the central circadian clock (synchronized by light) and peripheral circadian clocks (entrained by nutrient intake) ,20. DesyEating when pre-prandial  glucose levels are low (“low-glucose eating pattern”) is an evidence-based strategy to improve maladaptive eating patterns. Research shows that eating without physiological hunger is a modifiable health risk behavior associated with excessive weight gain and increased metabolic risk ,32. Cons2) [The modification of glucose eating patterns by GGE is feasible ,36 and h2) . Insulin2) ,41. GGE 2) , suggestThe key aspect of the GGE intervention is eating when glucose is low, defined as under one’s personalized glucose threshold. The goal of the current study is to explore the impact of low- vs. high-glucose eating patterns on changes in body weight and the selected serum biomarkers of breast cancer risk after 16 weeks and compare these results with those reported in recent TRF studies in similar populations of older women and with the intensive lifestyle intervention conducted by Fabian et al. in postmenopausal overweight and obese women at increased risk for breast cancer. The findings of the current study are intended to support further testing of GGE to promote a low-glucose eating pattern as a strategy to reduce breast cancer risk in postmenopausal women. Therefore, this exploratory, secondary analysis aims to examine the potential effect of a low-glucose eating pattern on postmenopausal breast cancer risk.Project Take Charge  was a 16N = 50) were predominantly White, non-Hispanic older women who were well-educated; lived in the Houston, Texas, metropolitan area; and had a BMI > 27 kg/m2. This study was approved by the Institutional Review Board and registered at clinicaltrials.gov (NCT03546972). Women provided informed consent prior to initiating the study.As part of a randomized feasibility study, GGE was added to a 16-week version of the DPP intervention that targeted women at risk of postmenopausal breast cancer  . ParticiThe Project Take Charge protocols were fully described elsewhere . BrieflyN = 19 women.The women enrolled in Project Take Charge additionally provided blinded CGM data using Dexcom G5  at week 0 (baseline), week 8, and week 16 (post-intervention) for up to 10 days at a time. From the collected CGM data, the mean amplitude of glycemic excursions (MAGE) was calculated using EasyGV  as a meaWomen were categorized into eating patterns based on week 16 dietary and CGM data. Those who consumed at least 40% of their recorded meals when their pre-prandial glucose levels were below their personalized threshold will be referred to as following a “low-glucose eating pattern (LGEP)”; whereas those who ate less than 40% of their meals when pre-prandial glucose was below their threshold will be referred to as following a “high-glucose eating pattern (HGEP)”. The threshold of 40% eating events was chosen to define the groups post hoc to maximize between-group differences in reductions of HOMA-IR at 16-weeks. LGEP and HGEP were quantified at all three time points using blinded CGM was calculated as the percentage of reported eating occasions where a participant’s glucose was equal to or less than their computed, personalized threshold .p-values or other tests of significance were not reported due to the secondary and exploratory nature of this analysis. TRF studies were identified using MedLine. Only those articles with predominantly older women at or near an age consistent with the onset of menopause (>45 years) were considered.Outcomes between the original intervention groups were similar. Specifically, the intervention groups (DPP-only vs. DPP + GGE) had comparable changes from baseline to 16 weeks in weight (−5.0 kg vs. −4.9 kg) and HOMA-IR (−0.3 vs. −0.4). Therefore, for this analysis, the data from the DPP-only and DPP + GGE groups were combined. The 16-week changes in weight and serum biomarkers were compared between women in the LGEP and HGEP groups using SPSS version 28. The means, standard deviations, medians, and ranges are reported here and compared to findings from Fabian et al.  and publN = 19) were predominantly older, White, non-Hispanic, college-educated women, with a BMI in the obese range at baseline. Out of the 19 women with CGM and dietary data, eight (42%) were identified as following a low-glucose eating pattern at post-intervention (week 16) and categorized in the LGEP group. Women in the LGEP were comparable demographically to those in the HGEP group with modestly greater BMI (ater BMI .At baseline (prior to starting the intervention), nearly 70% of reported eating events occurred when glucose was above fasting levels (“HGEP”). By week 8 in the LGEP group, the majority of reported eating events occurred when glucose levels were below the personalized thresholds (approximately 60%). This change in the LGEP group was maintained at week 16. In the HGEP, fewer reported eating events occurred when glucose levels were below personalized thresholds from baseline to mid-intervention to post-intervention .Women in the LGEP group experienced notable improvements in adiponectin, HOMA-IR, fasting insulin, and glycemic variability  . These cWhen results of this study are compared to those from published TRF interventions in similar populations, it suggests that LGEP induces nominally larger average weight loss and reductions in HOMA-IR and fasting insulin that is not explained by changes in energy intake . The comThis study supports the potential efficacy of a low-glucose eating pattern (LGEP) to improve metabolic and cancer risk biomarkers, including insulin resistance, in older women. Importantly, these data support a viable alternative to TRF for improving health outcomes. Furthermore, the positive metabolic effects of an LGEP might be achieved without eating all meals under the personalized glucose threshold, further supporting the flexibility of LGEP and the robust effects of LGEP in relation to metabolic health. Specifically, following LGEP at ≥40% of eating events is associated with significant improvements in weight and serum markers of cancer risk over time. These findings are similar to previously reported findings of the GGE intervention, where modest protocol adherence was associated with clinically relevant weight loss and improvements in eating behavior . HoweverComparing our results to those from TRF studies suggests that LGEP could be as effective or more effective at reducing the risk of postmenopausal breast cancer. We hypothesize that GGE could effectively teach women to follow LGEP to achieve these outcomes. The results shown here suggest it is worthwhile to conduct a clinical trial aimed at comparing the effects of these interventions on biomarkers of postmenopausal breast cancer. Key features of such a trial should include adherence for a range of population groups and durability of effects after the intervention has ceased. A previous pilot study showed that GGE is acceptable from a patient perspective and outlined adherence barriers and enablers . FurtherOf note, this and the related Project Take Charge studies to exemplify the benefit of using biological feedback (here glucose levels) to motivate and support effective behavior change . While systematic reviews demonstrated the utility of glucose monitoring in obesity research , limitedStrengths of this study include objective quantification of LGEP through passive glucose monitoring and the range of biomarkers tested. However, this analysis is limited by our small, homogeneous sample, which limits the generalizability of our findings. Our findings are most appropriate for hypothesis driving rather than hypothesis testing, and results should be interpreted with caution given the exploratory nature of analyses. Furthermore, the TRF studies were of shorter duration than the current study, which could have implications on comparing the magnitudes of observed effects. It is also unclear why women in Project Take Charge, who were randomized to the DPP-only intervention, changed to their LGEP without additionally receiving the GGE intervention. Future DPP intervention research could test the robustness of these findings. While other clinical trials have tested GGE as a standalone intervention ,36, follThis exploratory analysis of the impact of LGEP on weight and metabolic markers offers direction for the next steps in testing GGE as an intervention to prevent postmenopausal breast cancer. The adherence goal of 40% offers a feasible target for future GGE interventions and potential for health benefits, most critically a reduction in risk of postmenopausal breast cancer.


Is population screening for breast cancer risk associated with increased use of mammography among medically underserved women from racial and ethnic minority groups?In this cohort study including 188 women, providing individualized breast cancer risk estimates as a standard component of annual preventive health care was associated with improved use of mammography among women at high risk. This group’s rate of annual mammography increased from 37% during usual care to 51% following risk assessment.The findings of this study suggest that providing individualized breast cancer risk estimates as a standard component of preventive health care may reduce racial inequities in breast cancer screening and ultimately mitigate disparities in breast cancer mortality. Black women bear a disproportionate burden of breast cancer mortality in the US, in part due to inequities in the use of mammography. Population screening for breast cancer risk in primary care is a promising strategy for mitigating breast cancer disparities, but it is unknown whether this strategy would be associated with increased mammography rates in underserved women of racial and ethnic minority groups.To examine whether providing individualized breast cancer risk estimates is associated with an increase in the rate of screening mammography.A cohort study was conducted in women receiving individualized risk estimates as part of routine primary health care at federally qualified health centers in medically underserved communities in Chicago, Illinois. The study was conducted from November 5, 2013, to December 19, 2014, with data acquisition completed on March 5, 2017; data analysis was performed from December 30, 2020, to February 2, 2021. A total of 347 women aged 25 to 69 years without a personal history of breast cancer presenting for an annual visit with their primary care clinician were enrolled.Breast cancer risk estimates were obtained with validated risk assessment tools as a standard component of the clinic check-in process. One of 4 women at average risk and all women at high risk were invited to participate in the study.The primary outcome was the mammography rate during 18 months of usual care compared with the rate during 18 months after implementation of risk assessment.Of the 347 women enrolled, 188 were age-eligible for mammography and were included in the analysis ; 70 women (37.2%) were Hispanic, 114 (60.6%) were non-Hispanic African American, and 4 (2.1%) were from other racial and ethnic groups (4 non-Hispanic White women). Ninety-eight women (52.1%) had an average risk of developing breast cancer and 90 (47.9%) were at high risk. Overall, there was a nonsignificant increase in the mammography rate, from 38.8% during usual care to 48.9% following implementation of risk assessment . In preplanned subgroup analysis, the mammography rate among women at high risk was significantly higher after vs before risk assessment .In this study, providing individualized breast cancer risk estimates as a component of primary health care in federally qualified health centers was associated with increased use of mammography among women of racial and ethnic minority groups who were at high risk. Implementation of this approach in underserved communities could promote equity in the use of mammography and reduce racial disparities in breast cancer mortality. This strategy warrants further investigation. This cohort study assesses mammography use in a sample of women from participating federally qualified health centers to examine whether providing individualized breast cancer risk estimates is associated with increased uptake of cancer control measures. Racial disparities in breast cancer mortality emerged as routine screening mammography became the standard of care, and more than 3 decades later the breast cancer mortality rate for Black women remains 40% higher compared with non-Hispanic White women.2 Racial stratification in the US produces inequities in health care and health outcomes through multilevel social determinants of health.3 Black women in the US historically have had lower rates of screening mammography,4 which leads to a higher proportion of late-stage diagnoses and lower survival rates.5 Data have shown a narrowing of the racial gap from 1987 to 2015, but lower screening rates persist among women of racial and ethnic minority groups with socioeconomic disadvantage.6Widespread implementation of screening mammography contributed to the decrease in breast cancer mortality in the US over the past 30 years,8 A key domain in the health beliefs model is perceived risk.9 This theoretical framework posits that a woman will be more likely to engage in breast cancer screening programs if she believes that she is susceptible to breast cancer. Earlier work reported that Black women overall were significantly less likely to perceive their breast cancer risk accurately10 and that Black women with low income who were nonadherent to screening recommendations were less likely to perceive themselves as susceptible to breast cancer.11 These data suggest that providing underserved Black women with individualized breast cancer risk estimates may promote uptake of mammography.Models of health behavior change, such as the health beliefs model, have been used to identify factors associated with the uptake of mammography in women of underserved racial and ethnic minority groups.12 Women at high risk are candidates for a variety of enhanced cancer control measures.17 Formal cancer genetic risk assessment is a relatively time-intensive process and requires specialized expertise.12 However, it is possible to perform rapid screening for breast cancer risk (breast cancer risk assessment [BCRA]) in the primary care setting with validated instruments to identify women at high risk who are eligible for referral for genetic counseling and genetic testing for hereditary breast cancer syndromes based on their family history18 and are candidates for enhanced breast cancer screening with breast magnetic resonance imaging20 and pharmacologic risk reduction.21 Generating individualized breast cancer risk estimates for asymptomatic women in the primary care setting also provides an opportunity to ensure that the women who benefit most from mammography  are engaged in screening programs. To our knowledge, incorporating BCRA into preventive health care to identify high-risk individuals has not been tested as a strategy to increase mammography use among women of underserved racial and ethnic minority groups. We conducted a study to examine the feasibility of implementing BCRA as a standard component of the annual well visit at federally qualified health centers (FQHCs) in underserved racial and ethnic minority communities.22 Herein, we report an analysis of mammography use in a sample of women from participating FQHCs who enrolled in a prospective cohort study to examine whether providing individualized breast cancer risk estimates is associated with increased uptake of cancer control measures.Cancer genetic risk assessment involves in-depth evaluation with genetic counseling and germline genetic testing of cancer susceptibility genes, and empirical risk model prediction when a pathogenic genetic variant is not identified.22 A prospective cohort study was embedded within the risk assessment feasibility study by selecting a sample of women from the entire group who received individualized risk estimates to participate in the prospective study; 1 of 4 randomly selected women at average risk and all of those at high risk were invited to participate in the prospective cohort study immediately after completion of risk assessment. Study recruiters were aware of each woman’s risk status, but participants were not aware of their individualized risk estimate at the time they were invited to participate in this study. Self-identified race and ethnicity of study participants was categorized as Asian or Pacific Islander, Hispanic, multiracial, non-Hispanic African American, non-Hispanic White, or other/do not know/refused. Study participation was restricted to women who could complete the enrollment interview in either English or Spanish. Recruitment occurred from November 5, 2013, to December 19, 2014, with data acquisition completed on March 5, 2017; data analysis was performed from December 30, 2020, to February 2, 2021. This report focuses on adherence to standard screening with mammography; therefore, the analysis reported herein includes only the subgroup of study participants who were aged 40 years or older at the time of enrollment and so were eligible for mammography according to the participating clinics’ guidelines. Younger women with increased risk were followed up for other outcomes  that were reported previously.22 All study participants were considered medically underserved because they receive regular care at an FQHC, and a key criterion for designation as an FQHC is that a community clinic provides primary care services in a medically underserved area.23From November 5, 2013, through December 19, 2014, we tested the feasibility of implementing BCRA as a standard component of annual well visits and new-patient visits at 2 outpatient clinic sites of an FQHC in Chicago, Illinois.STROBE) reporting guideline for cohort studies.24The study was approved by the institutional review board at the University of Illinois at Chicago, and all participants provided written informed consent; participants received financial compensation. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology  or the modified Gail model . Women who did not meet any of these criteria were classified as average risk. The software tool generates the result from each assessment instrument along with clinical decision support that includes recommendations for chemoprevention, enhanced screening with breast magnetic resonance imaging, and genetic counseling referral based on the result of the assessments and national practice guidelines. Primary care clinicians received a hard copy of BCRA results along with clinical decision support at the time of the participant encounter, and participants received the result of BCRA directly from their primary care clinician. The study protocol did not include any decision aids or educational materials for study participants. Patient education was nonstandardized and was left to the discretion of each treating clinician.Breast cancer risk assessment was performed by nonlicensed clinic staff for all women aged 25 to 69 years who did not have a personal history of breast cancer on presentation for an annual well visit or new-patient visit with their primary care clinician. Clinic staff members entered risk factor data for the assessment using tablet computers, based on patient self-report of family history and other breast cancer risk factors. Breast cancer risk assessment was performed with a custom software application developed by study investigators that collects family cancer history and nonfamilial breast cancer risk factors and integrates several validated instruments to generate a comprehensive assessment of breast cancer risk  collected demographic information and measures of breast cancer beliefs. The survey instrument is described in eTable 2 in the P < .20 in unadjusted models. Generalized estimating equation models specified equal within-group correlations (exchangeable correlation structure) among repeated measures. The prespecified analysis plan included subgroup analyses stratified by risk level. All P values were 2-sided and considered significant at P < .05. Analyses were performed with Stata, version 16 (StataCorp LLC).Characteristics of high-risk participants were compared with those of average-risk participants using logistic regression models weighted to account for unequal probability of recruitment. Generalized estimating equations were used to compare the rate of mammography during 18 months of usual care before BCRA with the rate during 18 months following BCRA and assess factors associated with mammography use. This approach accounts for undersampling of average-risk participants and for correlation between pre- and post-BCRA data. Unadjusted bivariate analyses with generalized estimating equations were performed by combining all events (performance of a screening mammogram) occurring during the 18 months of usual care before BCRA with all events occurring over 18 months after participants underwent BCRA. A multivariable model was estimated that included factors associated with mammography at a level of A total of 267 women at average risk and 224 women at high risk of breast cancer were invited to participate in the prospective cohort study, and 186 women (69.7%) with an average risk and 161 women (71.9%) with high risk completed study enrollment. This analysis included 98 (52.7%) average-risk and 90 (55.9%) high-risk participants who were older than 40 years at the time of study enrollment  (P = .02). There was a nonsignificant numeric increase among average-risk participants .There was a nonsignificant increase in the overall rate of mammography use, from 38.8% during usual care to 48.9% after BCRA (P = .12) . The rat22; 100% of 1269 women who were eligible for BCRA completed the assessment while they waited to see their primary care clinician. This strategy for conducting risk assessment involved minimal change to clinic workflow overall, requiring only 2 to 3 extra minutes of staff time on average to input family history and nonfamilial risk factor data and generate the assessment report.22 The process did not require any changes in physician workflow, nor did it require scheduling longer appointment slots for annual well visits or new-patient visits.This prospective cohort study found that incorporating BCRA into routine preventive health care was associated with improved use of screening mammography among women of underserved racial and ethnic minority groups who had a high risk of breast cancer. A previous report described the feasibility of a technology-enabled approach to population screening for breast cancer risk28 Somewhat unexpectedly, perceived risk was not associated with mammography use in the entire cohort or in either of the risk-stratified subgroups. Kim and colleagues27 reported similar findings for a racially diverse cohort from San Francisco. Qualitative research with African American women with hereditary breast cancer risk found that decisions to engage in cancer risk management behaviors are associated with 3 accumulated layers of risk information: perceived risk at the foundational layer, general information about managing breast cancer risk at the middle layer, and specific information about risk-management options at the most proximal layer.29 African American women experience distinct dynamics at each of these layers compared with non-Hispanic White women. The authors noted that information access is associated with clinician access.29 Consistent with that finding, we observed a numeric increase in mammography adherence among women who ever discussed breast cancer risk with a clinician in the entire cohort and in both risk-level subgroups, although the increase did not reach statistical significance. These data suggest that coupling individualized risk estimates with tailored educational material and decision aids may stimulate discussion with clinicians and may be more useful in motivating engagement in breast cancer screening than simply delivering risk estimates, as done in this study.Less than half of all women at high risk (45.6%) correctly perceived their risk status, compared with more than 70% of women at average risk (71.4%). This difference is consistent with other reports in the literature suggesting that approximately half of African American women accurately perceive their breast cancer risk.One potential unintended consequence of widespread implementation of BCRA in asymptomatic women is the possibility that the information would provide a false sense of security for women at average risk and reduce mammography use in that subgroup; however, we did not observe a signal of that adverse outcome. Although the study was underpowered for the average-risk subgroup, we observed a nonsignificant numeric increase in mammography uptake in that group as well as in the high-risk group. This increase suggests that providing individualized risk estimates might be associated with improved mammography use in women of underserved racial and ethnic minority groups regardless of risk status, although that hypothesis will need to be tested in an adequately powered study.30 The present study did not incorporate any interventions designed specifically to facilitate mammography adherence . Despite this lack of interventions, the improvement in mammography adherence rates associated with BCRA in our study compares favorably with interventions designed specifically for that purpose.30 A multilevel approach that identifies the high-risk population with BCRA in primary care settings would allow other interventions that improve adherence to mammography  to be targeted to the subgroup of women who benefit the most from screening. A strategy that combines BCRA with navigation of high-risk individuals may be more useful than either intervention alone for addressing breast cancer disparities, and this approach is supported by a cost-effectiveness analysis.31 In addition, this multilevel approach could be tested as a strategy to increase use of enhanced cancer control measures, such as genetic counseling and breast magnetic resonance imaging screening. More research is needed to assess whether this strategy would mitigate racial disparities in breast cancer mortality.A large body of research reports strategies for improving mammography adherence in women of racial and ethnic minority groups, including patient reminders and educational/motivational interventions, and patient navigation.15 To our knowledge, this is the first study to observe improved uptake of a clinical procedure  following systematic BCRA in women without a personal history of breast cancer. The present study was not designed to detect change in a clinical outcome . More work is needed with studies that have longer follow-up and are adequately powered to detect changes in relevant clinical outcomes. However, this study provides data supporting the feasibility of research designed to assess the clinical outcomes associated with BCRA in women of underserved racial and ethnic minority groups.The US Preventive Services Task Force recently noted that the benefits of screening for hereditary breast cancer risk have not been directly evaluated by research.This study has limitations. The investigation was conducted in urban FQHCs with African American and Hispanic women. The results need to be confirmed in other settings and with women from other racial and ethnic groups. In addition, all women in this study had either public or private health insurance. The result may have been different in a population that includes women without health insurance. We did not measure health literacy or numeracy and we did not provide standardized educational material or decision aids, which could have confounded the results. It is unknown whether the benefit will be maintained over the long term and whether similar improvements would be seen in women with higher baseline screening rates. This study did not examine whether the association between BCRA and mammography use in women at high risk was owing to a change in primary care clinician recommendations or patient adherence. Differences in baseline characteristics between the high-risk and average-risk subgroups could contribute to the observed differential association with BCRA. The cohort design prevented us from drawing conclusions on causality, and the study was underpowered to detect differences in the average-risk subgroup.This study suggests that an individualized approach to breast cancer screening enabled by implementation of BCRA in FQHCs is associated with increased use of screening mammography among women of medically underserved racial and ethnic minority groups who have an increased risk of developing breast cancer. This approach warrants further study as a strategy to reduce racial disparities in breast cancer mortality, although further research should combine BCRA with other interventions that may improve mammography adherence to maximize the benefit.