Adherence to Culturally Tailored Dietary Guideline and Breast Cancer Risk Reduction: A Cross-Cultural Nested Case-Control Study

Adherence to Culturally Tailored Dietary Guideline and Breast Cancer Risk Reduction: A Cross-Cultural Nested Case-Control Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adherence to Culturally Tailored Dietary Guideline and Breast Cancer Risk Reduction: A Cross-Cultural Nested Case-Control Study Shuwan Yu, Ying Shan, Xinyu Liu, Kai Wei, Zhigang Yu, Fei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6867878/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Diet is a key modifiable risk factor for breast cancer. This study examined the adherence to the Chinese Food Pagoda (CHFP) dietary guidelines in association with breast cancer risk in women from two culturally distinct cohorts in China and the United States. Methods Nested case-control studies were conducted among 133 and 45 breast cancer cases within two cohorts: 46,980 women from the Breast Cancer Study of Chinese Women (BCCS-CW) and 8,655 women from the National Health and Nutrition Examination Survey (NHANES), respectively. Adherence to both the CHFP-2022 and CHFP-2016 dietary guidelines was evaluated using a scoring system based on food group recommendations. The primary outcome was the association between adherence to CHFP guidelines and breast cancer risk, assessed by adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Results Higher CHFP adherence was associated with a 34–36% lower breast cancer risk per 5-point increase of CHFP-2022 score in BCCS-CW (adjusted OR: 0.64, 95%CI: 0.48–0.85) and 27%-30% in NHANES (adjusted OR: 0.70, 95%CI: 0.56–0.88) cohort. Women in the highest quartile of adherence had substantially lower risk than their counterparts in the lowest quartiles in BCCS-CW cohort (OR: 0.50, 95% CI: 0.28–0.89). Risk reductions were consistent across subgroups defined by menopausal status, Body Mass Index, education, income, marital status and the use of estrogen and progesterone, and similar for CHFP-2016 adherence score. Population-specific effects with individual food components were observed, with dairy products associated with lower risk in the NHANES cohort (adjusted OR: 0.78, 95%CI: 0.62–0.98) but higher risk in BCCS-CW cohort (adjusted OR: 1.29, 95%CI: 1.02–1.63). Conclusions This study provides the first evidence that adherence to a culturally adapted dietary framework may be associated with a reduced risk of breast cancer across diverse populations. Further research is warranted to confirm causality and explore population-specific dietary influences. Breast cancer Dietary guidelines Cross-cultural comparison Cancer prevention Figures Figure 1 Figure 2 Figure 3 INTRODUCTION According to data from the World Health Organization (WHO), breast cancer accounts for 31% of all female cancers, and is projected to increase by nearly 40% by 2050. Breast cancer risk differs across regions and calls on cost-effective prevention policies [ 1 , 2 ]. Despite advancements in diagnostic and therapeutic strategies, the incidence of breast cancer has been steadily rising since the mid-2000s, with an average annual growth rate of 0.5% [ 3 ]. This trend is particularly evident in developing countries, where rapid urbanization and lifestyle changes exacerbate modifiable risk factors [ 3 ]. Within this framework, lifestyle medicine has emerged as a critical discipline, emphasizing the role of daily behaviors in cancer prevention and overall health maintenance [ 4 , 5 ]. Among modifiable factors, dietary quality has received increasing attention due to its central role in cancer development and progression, including breast cancer [ 6 – 8 ]. While numerous epidemiological studies have examined the relationship between individual foods or nutrients (e.g., grains, alcohol, fruits, vegetables, meat, and soy products) and breast cancer risk [ 8 – 10 ], the complex interactions among dietary components challenge the reductionist approach of focusing on isolated nutrients [ 11 ]. Furthermore, the dietary patterns of different regions are inherently complex, limiting the theoretical and practical value of studying single food items [ 12 ]. Therefore, recent research has shifted toward examining broader dietary patterns and adherence to established dietary guidelines. Therefore, adopting a holistic evaluation of dietary quality offers the potential to simultaneously address multiple carcinogenic pathways, reinforcing its role as a cornerstone in cancer prevention strategies [ 2 ]. The 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Third Expert Report evaluated evidence linking cancer with dietary quality indices, but current evidence remains limited and inconclusive. Contemporary research has focused on dietary patterns such as the Healthy Eating Index (HEI) [ 13 , 14 ], Alternate Healthy Eating Index (AHEI) [ 14 , 15 ], Mediterranean Diet Score (MDS) [ 15 , 16 ], and Dietary Inflammation Index (DII) [ 17 , 18 ], all of which have demonstrated predictive value for cancer outcomes [ 19 , 20 ]. However, existing evidence suggests that dietary risk factors and protective effects are often influenced by cultural and regional dietary patterns, limiting the universal applicability of these patterns [ 21 ]. For instance, HEI, AHEI, and DII were originally developed for Western populations and may not accurately reflect dietary habits in other regions [ 22 – 24 ]. Similarly, the MDS includes key components such as olive oil, seafood, and cheese that may be less accessible, culturally uncommon, or cost-prohibitive in non-Mediterranean settings [ 25 , 26 ]. These cultural and geographical disparities highlight the need for population-specific dietary guidelines to improve relevance and effectiveness. The Chinese Food Guide Pagoda (CHFP), developed by the Chinese Nutrition Society and the Ministry of Health, serves as a culturally tailored and scientifically grounded dietary guideline that reflects traditional dietary customs while incorporating modern nutritional principles. Updated every six years, the latest versions include CHFP-2016 and CHFP-2022 [ 27 – 29 ]. Previous studies suggest that adherence to CHFP recommendations is associated with improved survival and reduced recurrence and mortality among breast cancer survivors [ 30 ]. However, evidence on its protective effect against primary breast cancer incidence remains limited. Additionally, whether such a dietary pattern can be generalized and applied to other populations with different cultural, behavioral, and environmental contexts remains unclear [ 31 , 32 ]. To address these gaps, this study investigates the association between dietary guideline adherence and breast cancer risk using data from the Breast Cancer Cohort Study of Chinese Women (BCCS-CW) and the U.S. National Health and Nutrition Examination Survey (NHANES). By leveraging data from two culturally distinct populations, this study aims to evaluate the cross-cultural generalizability of dietary adherence as a preventive factor against breast cancer. Findings from this research may inform the development of culturally adaptive dietary interventions and contribute to global strategies for breast cancer prevention. METHODS Study Population This study utilized data from the Breast Cancer Cohort Study (BCCS-CW) and the National Health and Nutrition Examination Survey (NHANES) to conduct matched and weighted case-control analyses. To ensure comparability between the two cohorts, we excluded individuals who (1) were under 20 or over 80 years old, (2) had extremely abnormal total energy intake (less than 600 or more than 3,500 kcal/day), (3) had missing required variable information, or (4) were diagnosed during pregnancy. The detailed data selection process is illustrated in Figure 1 . The BCCS-CW, initiated in 2008, is a large, community-based cohort encompassing 15 districts and counties across Shandong, Hebei, and Jiangsu provinces in China. The study employs a multi-stage follow-up design, incorporating community-targeted surveillance, as well as annual data linkage to collect breast cancer-related outcomes. The most recent in-person follow-up occurred between 2019 and 2020, during which dietary intake data were gathered using a standardized Food Frequency Questionnaire (FFQ). Detailed information regarding the BCCS-CW methodology has been previously published [33]. NHANES is a population-based, cross-sectional survey designed to assess the health and nutritional status of the U.S. population. Approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, NHANES employs a complex, stratified, multi-stage sampling design to obtain a representative sample of the U.S. population [34, 35]. The NHANES data is publicly available at https://www.cdc.gov/nchs/nhanes/. In this nested case-control study, newly diagnosed breast cancer cases were identified through biopsy and/or surgical pathology, which are considered the gold standard for diagnosis in the BCCS-CW, and by ICD-10 code matching in NHANES. To minimize potential confounding variables and enhance the accuracy of the findings, in the BCCS-CW study, breast cancer-free women were matched to newly diagnosed cases in a 1:3 ratio based on age at diagnosis, timing of the FFQ survey, and geographic region [36]. The NHANES study employed a stratified, multistage probability sampling design, with sample weights applied to account for sampling bias and ensure national representativeness. Therefore, no variable imputation was performed in the NHANES dataset in order to preserve the integrity of the weighted analyses [37-39]. Generation of Dietary Recommendation Adherence Scores Adherence scores were calculated for each participant based on the CHFP-2016 and CHFP-2022 guidelines. Both versions encompass similar food categories—including dairy products, beans, meat, poultry, eggs, aquatic products, vegetables, fruits, grains, alcohol, salt, oil, water, and sugar—with slight variations in the recommended intake levels. Food intake was calculated using the formula: daily intake = consumption frequency × amount per occasion. It was subsequently standardized to daily units. For each food category, recommended intake levels were established. Participants meeting or exceeding the recommendations received the highest score, while those with intake levels below the thresholds received the lowest. Intermediate scores were proportionally assigned based on the degree of deviation from the recommended amounts. In the BCCS-CW cohort, due to the unavailability of data on daily intake of water, sugar, oil, and salt, adherence scores for CHFP-2016 and CHFP-2022 were calculated based on the remaining nine food components. The total adherence scores ranged from 0 (lowest adherence) to 40 (highest adherence; Table 1 , Supplementary Table 1 ). For the NHANES cohort, adherence scores for CHFP-2016 and CHFP-2022 were calculated based on total nutrient intakes from the first day of data collection (DR1TOT), obtained through in-person interviews at mobile examination centers. This analysis included 11 food items (with increased emphasis on sugar and oil compared to BCCS-CW), with total scores ranging from 0 (lowest adherence) to 50 (highest adherence) ( Supplementary Tables 2 and 3 ). Before generating adherence scores in the BCCS-CW study, dietary variables with missing values in more than two-thirds of all participants were excluded from the final analysis. For the remaining variables with missing data, multiple imputation by chained equations (MICE) was performed. Consistency across imputations was assessed using Cronbach’s alpha, and the distribution of the imputed data closely resembled that of the original dataset. Consequently, the mean of these five imputations was utilized to fill in the missing data [40-42]. Table 1. Chinese Food Guide Pagoda 2022 components and adherence scores in the BCCS-CW Components a Recommended amount of intake Standard for Maximum Standard for Minimum Maximum point CHFP-2022 score b OR (95%CI) c P Dairy products 300-500 g/d > 500 g/d 0 g/d 5 0.42±0.80 1.29(1.02-1.63) 0.03 Beans 25-35 g/d > 35 g/d 0 g/d 5 0.05±0.09 0.48(0.05-5.00) 0.54 Meat poultry 40-75 g/d 112.5 g/d 4 3.25±1.46 0.58(0.19-1.74) 0.33 Egg 300-350 g/w 525 g/w 3 2.44±0.80 0.89(0.79-1.12) 0.12 Aquatic products 40-75 g/d > 75 g/d 0 g/d 3 0.42±0.68 0.90(0.63-1.28) 0.56 Vegetables 300-500 g/d > 500 g/d 0 g/d 5 2.31±1.42 0.90(0.77-1.06) 0.20 Fruits 200-350 g/d > 300 g/d 0 g/d 5 2.88±1.78 0.79(0.69-0.90) 400 g/d 0 g/d 5 3.35±1.45 0.75(0.64-0.89) <0.001 Alcohol < 15 g/d 22.5 g/d 5 4.99±0.26 0.69(0.38-0.26) 0.23 Total c 40 20.11±4.07 0.91(0.86-0.97) 0.002 Abbreviations: BC, Breast Cancer; CHFP-2022, Chinese Food Guide Pagoda 2022; OR, Odds Ratio; CI, Confidence Interval; SD, Standard Deviation. a Components, maximum/minimum points and standards were based on Chinese Food Pagoda 2016(CHFP-2016). nine items were included in this study, except daily intake of water, oil, salt and sugar, due to the lack of data. b Intakes between minimum and maximum levels were scored proportionately following the formulas: (actual intake amount / intake amount for maximum point) × maximum point, for components with recommendation of lower limit of intake amount; (intake amount for minimum point - actual intake amount) / (intake amount for minimum point - intake amount for maximum point) × maximum point, for components with recommendation of upper limit of intake amount. Data were shown as mean ± SD. c Conditional logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, BMI, marital status, education level, income level, family history of cancer, whether have taken hormones, progesterone, or other female hormones. Variable Collection Common covariates across both cohorts included age, menopausal status, use of estrogen and progesterone, marital status (married or living with partner, never married, separated or otherwise), Body Mass Index (BMI) (< 25 kg/m², ≥ 25 kg/m²), educational level, and annual income level. Due to differences in educational systems and income measures between Chinese and American residents, we describe educational levels and annual income separately for each cohort. In the BCCS-CW cohort, educational levels are categorized as ≤ 6 years, 7-9 years, 10-12 years, and > 12 years; annual income levels are categorized as < 12,000 RMB, 12,000-36,000 RMB, 36,000-60,000 RMB, and ≥ 60,000 RMB. In the NHANES cohort, educational levels are categorized as 12 years; annual income levels are categorized as < 25,000 USD, 25,000-55,000 USD, 55,000-75,000 USD, and ≥ 75,000 USD. Additionally, given the different data compositions of the two cohorts, we extracted relevant variables separately to account for potential confounding effects. These variables included family history of cancer and hormone use in the BCCS-CW cohort, and ethnicity (White, Black, Hispanic, Other), MET (weekly MET accumulation), total daily caloric intake, and chronic diseases (diabetes, dyslipidemia, and cardiovascular disease) in the NHANES cohort ( Supplementary Methods ). These variables were included based on the assumption that patients with these conditions may have altered dietary habits and that these conditions could be associated with higher risks of breast cancer. Statistical Analysis Participants were classified into interquartile groups based on the distribution of adherence scores for each dietary recommendation. Baseline characteristics of the different groups were compared using t-tests and Mann–Whitney U tests for numerical variables, and chi-squared (χ²) tests for categorical variables. Conditional logistic regression models and logistic regression models were performed for the BCCS-CW and NHANES cohorts, respectively, to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between dietary adherence and breast cancer risk. The models were adjusted as follows: (1) Model 1: Adjusted for age at survey and menstrual status (i.e., whether menstruation had ceased for over one year). For the NHANES data, daily energy intake was also adjusted. (2) Model 2: Further adjusted for socio-economic factors, including BMI, marital status, education level, and income level. Ethnicity was additionally included in the NHANES analysis. (3) Model 3: Further adjusted for chronic diseases potentially associated with dietary habits and breast cancer risk. In the BCCS-CW cohort, these included family history of cancer and hormone use (e.g., progesterone and other female hormones). In the NHANES cohort, the adjustments included hypertension, diabetes, cardiovascular disease, dyslipidemia, and metabolic equivalent of task (MET). These adjustment variables were selected to influence breast cancer risk or to be related to dietary habits or overall diet quality, thus serving as potential confounders of the diet-breast cancer risk correlation. Therefore, it is important to adjust for these covariates when performing statistical analyses to minimize confounding bias and to better isolate the independent effects of diet quality on breast cancer risk. ORs and 95% CIs for each 5-point increment in adherence scores were calculated by treating the scores as continuous variables. Subgroup analyses were performed to assess whether the associations between CHFP scores and breast cancer risk differed by menopausal status, marital status, education level, income level, and BMI. Interactions of adherence scores with potential confounding factors were also tested. All statistical analyses were conducted using SPSS 25.0 and R 4.1 software. A P -value of <0.05 was considered statistically significant. RESULTS Characteristics of Study Populations Following the inclusion criteria, the BCCS-CW cohort comprised a total of 46,980 eligible female participants, including 133 newly diagnosed breast cancer cases. In the NHANES cohort, 8,655 eligible females were included in this analysis, of whom 45 were newly diagnosed with breast cancer, based on data from the 2005–2016 period. Dietary weights (wtdrd1) were applied to the NHANES dataset to reflect a population size of 71,370,868, ensuring its representativeness of the general U.S. population. Baseline characteristics of cases and controls were generally comparable across both the BCCS-CW and NHANES cohorts. However, in the NHANES dataset, women with breast cancer were significantly older (60.83 vs. 46.72 years, P < 0.0001), more likely to be post-menopausal (87.70% vs. 43.8%, P < 0.0001), and had a higher prevalence of underlying cardiovascular diseases (18.67% vs. 5.38%, P < 0.0001). In the BCCS-CW cohort, compared to breast cancer free participants, women with breast cancer demonstrated significantly lower adherence scores for both CHFP-2016 (20.26 vs. 21.22, P = 0.02) and CHFP-2022 (20.22 vs. 21.32, P = 0.01). Similarly, in the NHANES cohort, breast cancer patients had lower CHFP-2016 scores (30.52 vs. 31.65, P = 0.04) and CHFP-2022 scores (30.04 vs. 31.05, P = 0.08). Detailed baseline characteristics are presented in Table 2 ( see Additional file 1) Dietary Adherence and Breast Cancer Risk BCCS-CW Cohort After adjusting for potential confounders, each 5-point increase in adherence scores to CHFP-2016 (OR: 0.69, 95% CI: 0.52-0.91) and CHFP-2022 (OR: 0.66, 95% CI: 0.51-0.87) was associated with a 31% to 34% reduction in breast cancer risk, respectively (Model 1). These associations persisted in further-adjusted analyses, including demographic and socioeconomic factors (Model 2) and covariates of family history and hormone usage (Model 3) for both CHFP-2016 (OR: 0.69, 95% CI: 0.52-0.91) and CHFP-2022 (OR: 0.64, 95% CI: 0.48-0.85; Figure 2 ). Compared to women in the lowest quartile of CHFP-2022 adherence scores, participants in the highest quartile displayed a significant reduction in breast cancer risk, with fully adjusted odds ratios (Model 3) of 0.50 (95% CI: 0.28-0.89). A significant linear trend ( P trend < 0.05) was observed across quartiles of CHFP-2022 scores in the BCCS-CW cohort ( Table 3 ). Table 3. ORs (95% CI) for breast cancer by quartile of dietary recommendation adherence scores in BCCS-CW and NHANES. Dietary recommendations Coherent Odds ratio (95% CI) by quartile Quartile1 g Quartile 2 h Quartile 3 Quartile 4 P trend CHFP-2016 scores BCCS-CW Crude OR 1.00 (Ref.) 1.00(0.59-1.70) 0.73(0.42-1.26) 0.64(0.36-1.12) 0.08 Model 1 a 1.00 (Ref.) 1.00(0.59-1.71) 0.73(0.41-1.26) 0.64(0.36-1.12) 0.08 Model 2 b 1.00 (Ref.) 1.06(0.62-1.81) 0.69(0.39-1.22) 0.63(0.35-1.11) 0.06 Model 3 c 1.00 (Ref.) 1.09(0.63-1.88) 0.72(0.41-1.27) 0.64(0.36-1.14) 0.08 NHANES Crude OR 1.00 (Ref.) 1.23(0.49-3.10) 1.24(0.42-3.65) 0.68(0.26-1.80) 0.47 Model 1 d 1.00 (Ref.) 0.97(0.36-2.61) 0.91(0.28-2.95) 0.43(0.15-1.19) 0.10 Model 2 e 1.00 (Ref.) 0.95(0.37-2.47 0.85(0.25-2.83) 0.37(0.13-1.02) 0.06 Model 3 f 1.00 (Ref.) 0.97(0.39-2.41) 0.84(0.26-2.80) 0.37(0.13-1.01) 0.05 CHFP-2022 scores BCCS-CW Crude OR 1.00 (Ref.) 0.69(0.40-1.18) 0.64(0.37-1.09) 0.51(0.29-0.89) 0.002 Model 1 a 1.00 (Ref.) 0.69(0.40-1.18) 0.63(0.36-1.09) 0.51(0.29-0.88) 0.002 Model 2 b 1.00 (Ref.) 0.71(0.41-1.21) 0.62(0.35-1.08) 0.49(0.28-0.87) 0.001 Model 3 c 1.00 (Ref.) 0.73(0.42-1.25) 0.63(0.36-1.10) 0.50(0.28-0.89) 0.002 NHANES Crude OR 1.00 (Ref.) 1.21(0.48-3.05) 1.25(0.43-3.62) 0.68(0.26-1.79) 0.49 Model 1 d 1.00 (Ref.) 0.97(0.36-2.60) 0.90(0.29-2.84) 0.44(0.16-1.21) 0.11 Model 2 e 1.00 (Ref.) 0.95(0.37-2.47) 0.84(0.26-2.73) 0.38(0.14-1.05) 0.06 Model 3 f 1.00 (Ref.) 0.96(0.38-2.41) 0.83(0.26-2.69) 0.37(0.14-1.02) 0.06 Abbreviations: CHFP-2016, Chinese Food Guide Pagoda 2016; CHFP-2022, Chinese Food Guide Pagoda 2022; OR, Odds Ratio; CI, Confidence Interval. a Conditional logistic regression model was adjusted for age at survey, whether stopped menstruating for over a year, b Conditional logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, BMI, marital status, education level, income level. c Conditional logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, BMI, marital status, education level, income level, family history of cancer，whether have taken hormones, progesterone, or other female hormones. d Logistic regression model was adjusted for age at survey, whether stopped menstruating for over a year and energy. e Logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, energy, BMI, ethnic, marital status, education level, income level. f Logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, energy, BMI, ethnic, marital status, education level, income level, diabetes, cardiovascular disease, dyslipidemia, MET, whether have taken hormones, progesterone, or other female hormones. g Q1-Q4: Chinese Food Pagoda 2016 and Chinese Food Pagoda 2022 quartiles of scores h OR (95%CI) NHANES Cohort In the NHANES cohort, a similar protective pattern was observed, with each 5-point increase in adherence scores to CHFP-2016 (OR: 0.69, 95% CI: 0.55-0.86) and CHFP-2022 (OR: 0.70, 95% CI: 0.56-0.88) associated with 30% and 31% reductions in breast cancer-specific risk, respectively (Model 3; Figure 2 ). No linear trend in quartiles founded in NHANES. However, protection trend from Q1-Q4 can be seen in NHANES) cohort ( Table 3 ). Details of baseline characteristics by adherence quartiles for BCCS-CW and NHANES cohorts are presented in Supplementary Tables 4 - 7 . Stratified and Subgroup Analyses We observed little evidence of effect modification by most demographic factors and chronic diseases, as shown in Figure 3 and Supplementary Tables 8 and 9 . However, when stratified by racial/ethnic group, we found that higher CHFP adherence score was associated with lower breast cancer risk among White women (adjusted OR: 0.93, 95% CI: 0.89-0.98 for CHFP-2016; adjusted OR: 0.94, 95% CI: 0.90-0.98 for CHFP-2022), but higher risk among Spanish population (adjusted OR: 1.23, 95% CI: 1.45-1.31 for CHFP-2016; adjusted OR: 1.17, 95% CI: 1.12-1.22 for CHFP-2022). Individual Dietary Components Analyses of specific dietary components revealed divergent effects of dairy consumption between the two cohorts, with an inverse association observed in NHANES (OR: 0.78, 95% CI: 0.62-0.98) and a positive association in BCCS-CW (OR: 1.29, 95% CI: 1.02-1.63) ( Table 1 , Supplementary Tables 1-3 ). DISCUSSION Our study provides compelling evidence that higher adherence to the Chinese Food Pagoda (CHFP) dietary guidelines is significantly associated with a reduced risk of breast cancer among both Chinese and American women. These findings highlight the value of evidence-based dietary frameworks, such as the CHFP, not only in preventing chronic diseases but also in mitigating cancer risk across populations with diverse geographic, cultural, and lifestyle contexts. By extending the application of such guidelines beyond general health promotion, targeted dietary interventions grounded in culturally relevant recommendations may offer broad potential as effective cancer prevention strategies. In our study, after adjusting for potential confounding factors, each 5-point increase in the CHFP score was associated with a 36% (CHFP-2022) and 31% (CHFP-2016) reduction in breast cancer risk in the BCCS-CW cohort. These findings build upon previous research demonstrating the protective effects of healthy dietary patterns, such as those assessed by indices including the Healthy Eating Index (HEI), Alternative Healthy Eating Index (AHEI), Dietary Inflammatory Index (DII), and Mediterranean Diet Score (MDS) [14, 43, 44]. While these indices have shown strong associations with reduced cancer risk, their applicability is often limited by geographic and cultural constraints. For instance, the HEI and AHEI are tailored to dietary habits in the U.S. [45], while the MDS emphasizes components such as olive oil and seafood, which may be less accessible or affordable in other regions [46, 47]. Although the DII offers mechanistic insights, its complexity hampers public understanding and adoption [48]. In contrast, the CHFP—rooted in cultural relevance and broad dietary guidance—demonstrated consistent protective associations in both Chinese and American populations, indicating that culturally tailored dietary frameworks may offer broader applicability than universal indices. The protective effect of CHFP may stem from its emphasis on whole grains and plant-based proteins, which modulate key pathways in breast carcinogenesis, including estrogen metabolism and inflammatory responses. This cross-cultural relevance highlights the value of culturally informed dietary frameworks in cancer prevention research. Despite the overall positive association between dietary adherence and breast cancer risk, our study also indicated differences in adherence patterns between the two cohorts, with NHANES participants generally exhibiting better adherence scores compared to those in the BCCS-CW cohort. This distinction may be attributed to variations in socioeconomic factors, public awareness, and access to dietary resources. The generally higher adherence scores observed in the American cohort may reflect the longstanding implementation of nutrition education campaigns, government-supported dietary programs, and specific public health interventions that promote adherence to dietary recommendations [49-51]. In contrast, while dietary patterns in China have improved in recent decades [52], intake of some key food groups, such as dairy products, soybeans, and aquatic products, remains below recommended levels. For instance, the Diet Quality Divergence Index for China has shown that while there is an upward trend in the consumption of fruits and vegetables, the intake of other essential food groups, such as dairy and protein sources, remains suboptimal [31, 53]. This gap highlights the need for targeted public health interventions to promote balanced dietary patterns that meet nutritional guidelines among Chinese women. Interestingly, in addition to demonstrating the general protective effect of CHFP adherence, our study revealed population-specific associations with individual food components. For instance, dairy consumption was associated with a reduced risk of breast cancer in the NHANES cohort, whereas an increased risk was observed in the BCCS-CW cohort. This divergence reflects ongoing controversy surrounding the role of dairy products in breast cancer etiology [54, 55], with studies reporting conflicting findings. Some research suggests that moderate dairy intake may confer health benefits, potentially due to bioactive components such as calcium, vitamin D, and peptides, which have been shown to inhibit the proliferation, migration, and invasion of breast cancer cells [10, 56, 57]. Conversely, concerns remain about residual sex steroid hormones in milk, which may disrupt hormonal balance and elevate cancer risk [55]. Moreover, the impact of dairy appears to differ by type and population subgroup: fermented dairy may offer protection in postmenopausal women, while high-fat dairy could increase risk, and low-fat dairy may be protective among premenopausal women [58-60]. These findings underscore the complexity of diet-disease relationships and highlight the need to consider regional dietary habits, food processing methods, and socio-environmental factors when interpreting epidemiological associations. Although the CHFP demonstrated a protective effect in both White and Asian populations in our study, this effect appeared attenuated in the Spanish population, potentially due to limited sample size, divergent dietary behaviors, or genetic susceptibility. Further research is warranted to explore ethnic and regional disparities in dietary risk factors for cancer. Additionally, we observed that the protective effect of CHFP adherence was more pronounced in participants without dyslipidemia, likely attributable to an overall healthier lifestyle in this subgroup. However, dyslipidemia could also act as a biological or behavioral confounder, diminishing the diet’s protective effect on breast cancer risk [61]. Given emerging evidence on the role of lipids in carcinogenesis [62], further mechanistic studies are needed to clarify the interactions among dyslipidemia, dietary intake, and breast cancer development. One of the key strengths of our study lies in the comprehensive assessment of adherence to dietary guidelines across both Chinese and American populations, utilizing two large, culturally diverse cohorts. This cross-national design enhances the robustness and generalizability of our findings. Notably, the CHFP demonstrated strong cross-cultural applicability, further supporting its potential as a globally adaptable dietary framework. In addition, the use of a nested case-control design, combined with rigorous matching and inverse probability weighting methods, helped to reduce potential confounding and selection biases. However, several limitations should be acknowledged. First, despite the strengths of the nested case-control design in minimizing recall bias, its retrospective nature limits the ability to infer a definitive causal relationship between CHFP adherence and breast cancer risk. Second, although the food frequency questionnaires (FFQs) used were previously validated and administered via in-person interviews, recall bias and measurement error may still have affected the accuracy of dietary intake estimates and adherence scores. Future research employing more objective dietary assessment methods, such as 24-hour dietary recalls or nutritional biomarkers, is warranted to improve measurement precision. Third, our findings are limited to Chinese and American populations, and further validation in other ethnic and geographic groups is needed to ensure broader applicability. Finally, although we adjusted for key sociodemographic and lifestyle variables, unmeasured confounding factors—particularly those related to cultural practices, regional food availability, and habitual dietary patterns—may still influence the observed associations. Future studies should further explore the interplay between cultural, environmental, and genetic factors in shaping diet–cancer relationships. CONCLUSION In conclusion, our study demonstrates that adherence to the CHFP guideline is significantly associated with a reduced risk of breast cancer across populations with substantial cultural differences. Notably, the consistency of the observed protective effects across distinct cohorts underscores the potential for CHFP-based dietary recommendations to be intercultural adapted and applied internationally. Further research is warranted to elucidate the influence of cultural, geographic, and habitual factors in the relationship between diet and cancer risk. Abbreviations BC: Breast Cancer, CHFP-2016: Chinese Food Guide Pagoda 2016, CHFP-2022: Chinese Food Guide Pagoda 2022, BCCS-CW: the Breast Cancer Study of Chinese Women, NHANES: the National Health and Nutrition Examination Survey. Declarations Data Availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Conflict of Interest The authors declare that they have no competing interests. Funding This work was funded by the National Natural Science Foundation of China (Grant No. 82003526), National Key Research and Development Program of China (2016YF0901301), and the Research Grant of the Second Hospital of Shandong University. Authors' contributions FW conceptualized the study and designed the experiments. SY, YS, XL, and KW performed the methodology. SY conducted the data analysis. FW and SY drafted the original manuscript. FW and ZY supervised the study. All authors read and approved the final manuscript. Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant No. 82003526), National Key Research and Development Program of China (2016YF0901301), and the Research Grant of the Second Hospital of Shandong University. Fei Wang is supported by the Taishan Scholarship of Shandong Province, the Qilu Scholar-in-Training Award of Shandong University, and the Young Elite Sponsorship Program of Shandong Provincial Medical Association. Zhigang Yu is supported by the Taishan Scholarship of Shandong Province. References DeSantis C, Siegel R, Bandi P, Jemal A. Breast cancer statistics, 2011. CA-CANCER J CLIN. [Journal Article; Review]. 2011 2011-11-1;61(6):409-18. Diet, Nutrition, Physical Activity and Cancer : a Global Perspective. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA-CANCER J CLIN. [Journal Article]. 2023 2023-1-1;73(1):17-48. Sharman R, Harris Z, Ernst B, Mussallem D, Larsen A, Gowin K. Lifestyle Factors and Cancer: A Narrative Review. Mayo Clin Proc Innov Qual Outcomes. [Journal Article]. 2024 2024-4-1;8(2):166-83. Parkinson MD, Stout R, Dysinger W. Lifestyle Medicine: Prevention, Treatment, and Reversal of Disease. MED CLIN N AM. [Journal Article; Review]. 2023 2023-11-1;107(6):1109-20. Minich DM, Bland JS. Personalized lifestyle medicine: relevance for nutrition and lifestyle recommendations. ScientificWorldJournal. [Journal Article]. 2013 2013-1-20;2013:129841. Xiao YL, Gong Y, Qi YJ, Shao ZM, Jiang YZ. Effects of dietary intervention on human diseases: molecular mechanisms and therapeutic potential. SIGNAL TRANSDUCT TAR. [Journal Article; Review]. 2024 2024-3-11;9(1):59. Anderson JJ, Darwis N, Mackay DF, Celis-Morales CA, Lyall DM, Sattar N, et al. Red and processed meat consumption and breast cancer: UK Biobank cohort study and meta-analysis. EUR J CANCER. [Journal Article; Meta-Analysis; Research Support, Non-U.S. Gov't; Systematic Review]. 2018 2018-2-1;90:73-82. Papadimitriou N, Markozannes G, Kanellopoulou A, Critselis E, Alhardan S, Karafousia V, et al. An umbrella review of the evidence associating diet and cancer risk at 11 anatomical sites. NAT COMMUN. [Journal Article; Meta-Analysis; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Systematic Review]. 2021 2021-7-28;12(1):4579. Fraser GE, Jaceldo-Siegl K, Orlich M, Mashchak A, Sirirat R, Knutsen S. Dairy, soy, and risk of breast cancer: those confounded milks. INT J EPIDEMIOL. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't]. 2020 2020-10-1;49(5):1526-37. Dietary patterns and cancer risk. Wang X, Chen L, Cao R, Ma R, Liu Y, Zhao Q, et al. Associations of 10 dietary habits with breast cancer: a Mendelian randomization study. FRONT NUTR. [Journal Article]. 2023 2023-1-20;10:1215220. Porciello G, Coluccia S, Vitale S, Palumbo E, Luongo A, Grimaldi M, et al. Baseline Association between Healthy Eating Index-2015 and Health-Related Quality of Life in Breast Cancer Patients Enrolled in a Randomized Trial. CANCERS. [Journal Article]. 2024 2024-7-18;16(14). Dela CR, Park SY, Shvetsov YB, Boushey CJ, Monroe KR, Le Marchand L, et al. Diet Quality and Breast Cancer Incidence in the Multiethnic Cohort. EUR J CLIN NUTR. [Journal Article; Research Support, N.I.H., Extramural]. 2020 2020-12-1;74(12):1743-7. Petimar J, Park YM, Smith-Warner SA, Fung TT, Sandler DP. Dietary index scores and invasive breast cancer risk among women with a family history of breast cancer. AM J CLIN NUTR. [Journal Article; Research Support, N.I.H., Extramural; Research Support, N.I.H., Intramural; Research Support, Non-U.S. Gov't]. 2019 2019-5-1;109(5):1393-401. Ergas IJ, Cespedes FE, Bradshaw PT, Roh JM, Kwan ML, Cadenhead J, et al. Diet Quality and Breast Cancer Recurrence and Survival: The Pathways Study. JNCI CANCER SPECT. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't]. 2021 2021-4-1;5(2). Hariharan R, Odjidja EN, Scott D, Shivappa N, Hébert JR, Hodge A, et al. The dietary inflammatory index, obesity, type 2 diabetes, and cardiovascular risk factors and diseases. OBES REV. [Journal Article; Research Support, Non-U.S. Gov't; Review]. 2022 2022-1-1;23(1):e13349. Fowler ME, Akinyemiju TF. Meta-analysis of the association between dietary inflammatory index (DII) and cancer outcomes. INT J CANCER. [Journal Article; Meta-Analysis; Review; Systematic Review]. 2017 2017-12-1;141(11):2215-27. Key TJ, Bradbury KE, Perez-Cornago A, Sinha R, Tsilidis KK, Tsugane S. Diet, nutrition, and cancer risk: what do we know and what is the way forward? BMJ-BRIT MED J. [Journal Article; Research Support, Non-U.S. Gov't]. 2020 2020-3-5;368:m511. Chlebowski RT, Aragaki AK, Anderson GL, Pan K, Neuhouser ML, Manson JE, et al. Dietary Modification and Breast Cancer Mortality: Long-Term Follow-Up of the Women's Health Initiative Randomized Trial. J CLIN ONCOL. [Journal Article; Multicenter Study; Randomized Controlled Trial; Research Support, N.I.H., Extramural]. 2020 2020-5-1;38(13):1419-28. Biruete A, Leal-Escobar G, Espinosa-Cuevas Á, Mojica L, Kistler BM. Dieta de la Milpa: A Culturally-Concordant Plant-Based Dietary Pattern for Hispanic/Latine People with Chronic Kidney Disease. NUTRIENTS. [Journal Article; Review]. 2024 2024-2-20;16(5). Pourmasoumi M, Karimbeiki R, Vosoughi N, Feizi A, Ghiasvand R, Barak F, et al. Healthy Eating Index/Alternative Healthy Eating Index and Breast Cancer Mortality and Survival: A Systematic Review and Meta-analysis. ASIA-PAC J ONCOL NUR. [Journal Article]. 2016 2016-7-1;3(3):297-305. Hayati Z, Jafarabadi MA, Pirouzpanah S. Dietary inflammatory index and breast cancer risk: an updated meta-analysis of observational studies. EUR J CLIN NUTR. [Journal Article; Meta-Analysis; Review]. 2022 2022-8-1;76(8):1073-87. Steck SE, Guinter M, Zheng J, Thomson CA. Index-based dietary patterns and colorectal cancer risk: a systematic review. ADV NUTR. [Journal Article; Research Support, N.I.H., Extramural; Review; Systematic Review]. 2015 2015-11-1;6(6):763-73. Castro-Espin C, Bonet C, Crous-Bou M, Nadal-Zaragoza N, Tjønneland A, Mellemkjær L, et al. Association of Mediterranean diet with survival after breast cancer diagnosis in women from nine European countries: results from the EPIC cohort study. BMC MED. [Journal Article; Research Support, Non-U.S. Gov't]. 2023 2023-6-26;21(1):225. Bordoni A. Insight into the Sustainability of the Mediterranean Diet: The Water Footprint of the Recommended Italian Diet. NUTRIENTS. [Journal Article]. 2023 2023-5-5;15(9). Wang SS, Lay S, Yu HN, Shen SR. Dietary Guidelines for Chinese Residents (2016): comments and comparisons. J ZHEJIANG UNIV-SC B. [Journal Article]. 2016 2016-9-1;17(9):649-56. Chinese Food Guide Pagoda(2016). Chinese Food Guide Pagoda(2022). Wang F, Cai H, Gu K, Shi L, Yu D, Zhang M, et al. Adherence to Dietary Recommendations among Long-Term Breast Cancer Survivors and Cancer Outcome Associations. CANCER EPIDEM BIOMAR. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.]. 2020 2020-2-1;29(2):386-95. Yu D, Zhang X, Xiang YB, Yang G, Li H, Gao YT, et al. Adherence to dietary guidelines and mortality: a report from prospective cohort studies of 134,000 Chinese adults in urban Shanghai. AM J CLIN NUTR. [Journal Article; Meta-Analysis; Research Support, N.I.H., Extramural; Review]. 2014 2014-8-1;100(2):693-700. Nguyen S, Li H, Yu D, Gao J, Gao Y, Tran H, et al. Adherence to dietary recommendations and colorectal cancer risk: results from two prospective cohort studies. INT J EPIDEMIOL. [Journal Article; Research Support, N.I.H., Extramural]. 2020 2020-2-1;49(1):270-80. Bao H, Liu L, Cong S, Wang F, Yu L, Fang L, et al. Breast Cancer Awareness and Association with Frequency of Screening Among Women - China, 2020. CHINA CDC WEEKLY. [Journal Article]. 2023 2023-4-14;5(15):327-32. Paulose-Ram R, Graber JE, Woodwell D, Ahluwalia N. The National Health and Nutrition Examination Survey (NHANES), 2021-2022: Adapting Data Collection in a COVID-19 Environment. AM J PUBLIC HEALTH. [Journal Article]. 2021 2021-12-1;111(12):2149-56. National Health and Nutrition Examination Survey. Artru F, Sacleux SC, Ursic-Bedoya J, Ntandja WL, Lutu A, L'Hermite S, et al. Long-term outcome following liver transplantation of patients with ACLF grade 3. J HEPATOL. [Journal Article; Multicenter Study]. 2025 2025-1-1;82(1):62-71. Liu F, Ran Q, Zhang H, Chen J. The Systemic Immune-Inflammation Index and the Risk of Parkinson's Disease in the U.S.: A Cross-Sectional Study. J CLIN MED. [Journal Article]. 2025 2025-1-10;14(2). Pan S, Chen S. Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health. INT J ENV RES PUB HE. [Journal Article; Research Support, N.I.H., Extramural]. 2023 2023-1-14;20(2). Abellana SR, Farran CA. The identification, impact and management of missing values and outlier data in nutritional epidemiology. NUTR HOSP. [Journal Article; Review]. 2015 2015-2-26;31 Suppl 3:189-95. Junaid KP, Kiran T, Gupta M, Kishore K, Siwatch S. How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations. POPUL HEALTH METR. [Journal Article]. 2025 2025-2-1;23(1):2. Holcomb LA, Maldonado L, Nietert PJ, Hayes MA, Witcraft SM, Newman RB, et al. Examining the role of social determinants of health in maternal mental health screening and treatment engagement during the perinatal period. BIOL SEX DIFFER. [Journal Article]. 2025 2025-2-12;16(1):11. Junaid KP, Kiran T, Gupta M, Kishore K, Siwatch S. How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations. POPUL HEALTH METR. [Journal Article]. 2025 2025-2-1;23(1):2. Kord-Varkaneh H, Salehi-Sahlabadi A, Zarezade M, Rahmani J, Tan SC, Hekmatdoost A, et al. Association between Healthy Eating Index-2015 and Breast Cancer Risk: A Case-Control Study. Asian Pac J Cancer Prev. [Journal Article]. 2020 2020-5-1;21(5):1363-7. Long M, Fan X, Wang M, Liu X, Fu C, Huang J, et al. Plasma metabolomic signature of a proinflammatory diet in relation to breast cancer risk: a prospective cohort study. AM J CLIN NUTR. [Journal Article]. 2025 2025-1-11. Petersen KS, Kris-Etherton PM. Diet Quality Assessment and the Relationship between Diet Quality and Cardiovascular Disease Risk. NUTRIENTS. [Journal Article; Review]. 2021 2021-11-28;13(12). Wang Y, Chen GC, Wang Z, Luo K, Zhang Y, Li Y, et al. Dietary Acculturation Is Associated With Altered Gut Microbiome, Circulating Metabolites, and Cardiovascular Disease Risk in US Hispanics and Latinos: Results From HCHS/SOL. CIRCULATION. [Journal Article]. 2024 2024-7-16;150(3):215-29. Castro-Espin C, Bonet C, Crous-Bou M, Nadal-Zaragoza N, Tjønneland A, Mellemkjær L, et al. Association of Mediterranean diet with survival after breast cancer diagnosis in women from nine European countries: results from the EPIC cohort study. BMC MED. [Journal Article; Research Support, Non-U.S. Gov't]. 2023 2023-6-26;21(1):225. Marx W, Veronese N, Kelly JT, Smith L, Hockey M, Collins S, et al. The Dietary Inflammatory Index and Human Health: An Umbrella Review of Meta-Analyses of Observational Studies. ADV NUTR. [Journal Article; Research Support, Non-U.S. Gov't; Systematic Review]. 2021 2021-10-1;12(5):1681-90. Mirowsky J, Ross CE. Education, Health, and the Default American Lifestyle. J HEALTH SOC BEHAV. [Journal Article]. 2015 2015-9-1;56(3):297-306. Aspry KE, Van Horn L, Carson J, Wylie-Rosett J, Kushner RF, Lichtenstein AH, et al. Medical Nutrition Education, Training, and Competencies to Advance Guideline-Based Diet Counseling by Physicians: A Science Advisory From the American Heart Association. CIRCULATION. [Journal Article; Review]. 2018 2018-6-5;137(23):e821-41. Mozaffarian D, Aspry KE, Garfield K, Kris-Etherton P, Seligman H, Velarde GP, et al. "Food Is Medicine" Strategies for Nutrition Security and Cardiometabolic Health Equity: JACC State-of-the-Art Review. J AM COLL CARDIOL. [Journal Article; Review]. 2024 2024-2-27;83(8):843-64. Lian Y, Gu L, Yang L, Wang L, Li H. The Reasonableness and Spatial Differences of the Food Consumption Structure of Urban and Rural Residents in China, 2015-2021. FOODS. [Journal Article]. 2023 2023-5-15;12(10). Zhang J, Wang H, Du W, Su C, Jia X, Huang F, et al. [Food intake among adult residents in 10 provinces (autonomous regions) of China in 2022-2023]. Wei Sheng Yan Jiu. [English Abstract; Journal Article]. 2024 2024-11-1;53(6):870-3. Scialo TE, Pace CM, Abrams DI. The Dairy and Cancer Controversy: Milking the Evidence. CURR ONCOL REP. [Journal Article; Review]. 2024 2024-3-1;26(3):191-9. Gao P, Li C, Gong Q, Liu L, Qin R, Liu J. Sex steroid hormone residues in milk and their potential risks for breast and prostate cancer. FRONT NUTR. [Journal Article; Review]. 2024 2024-1-20;11:1390379. Zhang D, Yuan Y, Xiong J, Zeng Q, Gan Y, Jiang K, et al. Anti-breast cancer effects of dairy protein active peptides, dairy products, and dairy protein-based nanoparticles. FRONT PHARMACOL. [Journal Article; Review]. 2024 2024-1-20;15:1486264. He Y, Tao Q, Zhou F, Si Y, Fu R, Xu B, et al. The relationship between dairy products intake and breast cancer incidence: a meta-analysis of observational studies. BMC CANCER. [Journal Article; Meta-Analysis]. 2021 2021-10-15;21(1):1109. Ronco AL, De Stéfani E, Dáttoli R. Dairy foods and risk of breast cancer: a case-control study in Montevideo, Uruguay. EUR J CANCER PREV. [Comparative Study; Evaluation Study; Journal Article]. 2002 2002-10-1;11(5):457-63. Moorman PG, Terry PD. Consumption of dairy products and the risk of breast cancer: a review of the literature. AM J CLIN NUTR. [Journal Article; Review]. 2004 2004-7-1;80(1):5-14. Kroenke CH, Kwan ML, Sweeney C, Castillo A, Caan BJ. High- and low-fat dairy intake, recurrence, and mortality after breast cancer diagnosis. JNCI-J NATL CANCER I. [Journal Article; Research Support, N.I.H., Extramural]. 2013 2013-5-1;105(9):616-23. Nelson ER, Wardell SE, Jasper JS, Park S, Suchindran S, Howe MK, et al. 27-Hydroxycholesterol links hypercholesterolemia and breast cancer pathophysiology. SCIENCE. [Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.]. 2013 2013-11-29;342(6162):1094-8. Liu W, Chakraborty B, Safi R, Kazmin D, Chang CY, McDonnell DP. Dysregulated cholesterol homeostasis results in resistance to ferroptosis increasing tumorigenicity and metastasis in cancer. NAT COMMUN. [Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.]. 2021 2021-8-24;12(1):5103. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additional file 1 File format:. Additional file 1.xls Ttle of data: Table 2. Characteristics of participants in BCCS-CW and NHANES Description: This table presents the baseline characteristics of cases and controls in both the BCCS-CW and NHANES cohorts . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6867878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483126860,"identity":"e4ce4ff0-822a-4fc9-98b4-1e420cbd74bd","order_by":0,"name":"Shuwan Yu","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shuwan","middleName":"","lastName":"Yu","suffix":""},{"id":483126861,"identity":"3625610e-83f2-448e-9b2c-dc3b4ece4375","order_by":1,"name":"Ying Shan","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Shan","suffix":""},{"id":483126862,"identity":"00069797-c0fd-4ab0-b107-57c18759bd1e","order_by":2,"name":"Xinyu Liu","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Liu","suffix":""},{"id":483126863,"identity":"703421c1-ecf7-4c0b-a13e-c6cb8b8d5ec3","order_by":3,"name":"Kai Wei","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Wei","suffix":""},{"id":483126864,"identity":"63a5eaee-70e8-4911-9379-cc08d53019b2","order_by":4,"name":"Zhigang Yu","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Yu","suffix":""},{"id":483126865,"identity":"08f36850-3a14-409f-a293-0f50e6a025e4","order_by":5,"name":"Fei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3NsQrCMBCA4ZSCWQJd4yC+QkEQQWhfJaGgS3URxKHDgdDRrr6GCJ0rB7pUXDO4dHF2clOM0rnNKJgfjtxwHyHEZvvBKDjwXTxCfP247YQVNemCOakXvzAmdA0uS67B/oI5J6uxBHoumgk7aHK8RbmaLDkppxLYXDSSkEtw4w5GQ8WG3ElRAmd+8y/9SpMXRoOs1ORlQrgD7izFwCexJmBCmAR8blBwNVmMxHE6SFncQuipqrYPDL0Md+qejHsZLZvJp0KPhM8m9HRa7+tC00ObzWb7w94RP0Pm9MlwmgAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Fei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-11 04:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6867878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6867878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86661864,"identity":"6ee4a5d4-64df-4be3-8a7e-6b47f4ce99c1","added_by":"auto","created_at":"2025-07-14 10:40:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of study design. \u003c/strong\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea \u003c/sup\u003eThis figure shows the inclusion and exclusion criteria for the populations in the two cohorts used in this study.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u003cem\u003eb \u003c/em\u003e\u003c/sup\u003eBased on the method of information collection of the NHANES, we categorized the data sources into \"Interview\" and \"Examination\". A total of 21,209 females were selected who attended the mobile exam center and answered interview questions, i.e., their IDs are recorded in the database.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec \u003c/sup\u003eThe FPED (Food Patterns Equivalents Database), developed by the USDA, standardizes food items reported in NHANES into predefined food groups and converts food consumption data into specific nutrient intakes.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Breast cancer-free women were matched to newly diagnosed cases in a 1:3 ratio based on age at diagnosis, timing of the FFQ survey, and geographic region.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee \u003c/sup\u003eFor NHANES data weighting, we used the dietary weight wtdrd1. Given the study design involves six cycles, each case's weight was divided by 6.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6867878/v1/c8b5391b7c88b2fb1698d282.png"},{"id":86663251,"identity":"d8ead57d-71b4-4faf-af26-bf663032d108","added_by":"auto","created_at":"2025-07-14 10:48:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eORs (95% CI) for breast cancer events by 5-point increase of dietary recommendation adherence scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: CHFP-2016, Chinese Food Guide Pagoda 2016; CHFP-2022, Chinese Food Guide Pagoda 2022; OR, Odds Ratio; CI, Confidence Interval.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e In BCCS-CW: Conditional logistic regression model was adjusted for age at survey, whether stopped menstruating for over a year (Model 1), Conditional logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, BMI, marital status, education level, income level (Model 2). Conditional logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, BMI, marital status, education level, income level, family history of cancer，whether have taken hormones, progesterone, or other female hormones (Model 3).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Logistic regression model was adjusted for age at survey, whether stopped menstruating for over a year and energy (Model 1); Logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, energy, BMI, ethnic, marital status, education level, income level (Model 2); Logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, energy, BMI, ethnic, marital status, education level, income level, diabetes, cardiovascular disease, dyslipidemia, MET, whether have taken hormones, progesterone, or other female hormones (Model 3).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6867878/v1/ea2ce332e1c75459c6f26e1e.png"},{"id":86661862,"identity":"8e48e7c3-bff0-44c2-b7ee-ed696435474b","added_by":"auto","created_at":"2025-07-14 10:40:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":189750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStratified Analysis of CHFP-2022 Adherence and Breast Cancer Risk by Ethnicity and Menopausal Status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: BC, Breast Cancer；CHFP-2016, Chinese Food Guide Pagoda 2016: CHFP-2022, Chinese Food Guide Pagoda 2022; OR, Odds Ratio; CI: Confidence Interval.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, energy, BMI, ethnic, marital status, education level, income level, diabetes, cardiovascular disease, dyslipidemia, MET, whether have taken hormones, progesterone, or other female hormones.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6867878/v1/e62847ae6c00923e89d1ce89.png"},{"id":86665587,"identity":"dcb7305d-83b7-4455-a72a-7c5f365ebb79","added_by":"auto","created_at":"2025-07-14 11:04:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1499031,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6867878/v1/eba3569f-7f98-4c53-9693-22d1eed533b8.pdf"},{"id":86663252,"identity":"935d09f8-74ca-4012-b34c-b9e668b616a0","added_by":"auto","created_at":"2025-07-14 10:48:16","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17074,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1\u003c/p\u003e\n\u003cp\u003eFile format:. Additional file 1.xls\u003c/p\u003e\n\u003cp\u003eTtle of data: Table 2. Characteristics of participants in BCCS-CW and NHANES\u003c/p\u003e\n\u003cp\u003eDescription: This table presents the baseline characteristics of cases and controls in both the BCCS-CW and NHANES cohorts\u003cstrong\u003e .\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6867878/v1/be90c7fdf82f387899434601.xlsx"},{"id":86664921,"identity":"c0024bde-1f85-41d0-9e79-f8cd01336a4b","added_by":"auto","created_at":"2025-07-14 10:56:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":89826,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiguresandtables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6867878/v1/2be1b6028eb090f62aeecbb5.docx"},{"id":86663259,"identity":"fdfcd497-c0bc-4439-93de-9716b5ed516f","added_by":"auto","created_at":"2025-07-14 10:48:16","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18997,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-6867878/v1/65989f86e8cd7b1903020b56.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adherence to Culturally Tailored Dietary Guideline and Breast Cancer Risk Reduction: A Cross-Cultural Nested Case-Control Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAccording to data from the World Health Organization (WHO), breast cancer accounts for 31% of all female cancers, and is projected to increase by nearly 40% by 2050. Breast cancer risk differs across regions and calls on cost-effective prevention policies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite advancements in diagnostic and therapeutic strategies, the incidence of breast cancer has been steadily rising since the mid-2000s, with an average annual growth rate of 0.5% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This trend is particularly evident in developing countries, where rapid urbanization and lifestyle changes exacerbate modifiable risk factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Within this framework, lifestyle medicine has emerged as a critical discipline, emphasizing the role of daily behaviors in cancer prevention and overall health maintenance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among modifiable factors, dietary quality has received increasing attention due to its central role in cancer development and progression, including breast cancer [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While numerous epidemiological studies have examined the relationship between individual foods or nutrients (e.g., grains, alcohol, fruits, vegetables, meat, and soy products) and breast cancer risk [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], the complex interactions among dietary components challenge the reductionist approach of focusing on isolated nutrients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, the dietary patterns of different regions are inherently complex, limiting the theoretical and practical value of studying single food items [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, recent research has shifted toward examining broader dietary patterns and adherence to established dietary guidelines. Therefore, adopting a holistic evaluation of dietary quality offers the potential to simultaneously address multiple carcinogenic pathways, reinforcing its role as a cornerstone in cancer prevention strategies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Third Expert Report evaluated evidence linking cancer with dietary quality indices, but current evidence remains limited and inconclusive. Contemporary research has focused on dietary patterns such as the Healthy Eating Index (HEI) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], Alternate Healthy Eating Index (AHEI) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], Mediterranean Diet Score (MDS) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and Dietary Inflammation Index (DII) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], all of which have demonstrated predictive value for cancer outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, existing evidence suggests that dietary risk factors and protective effects are often influenced by cultural and regional dietary patterns, limiting the universal applicability of these patterns [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For instance, HEI, AHEI, and DII were originally developed for Western populations and may not accurately reflect dietary habits in other regions [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, the MDS includes key components such as olive oil, seafood, and cheese that may be less accessible, culturally uncommon, or cost-prohibitive in non-Mediterranean settings [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These cultural and geographical disparities highlight the need for population-specific dietary guidelines to improve relevance and effectiveness.\u003c/p\u003e\u003cp\u003e The Chinese Food Guide Pagoda (CHFP), developed by the Chinese Nutrition Society and the Ministry of Health, serves as a culturally tailored and scientifically grounded dietary guideline that reflects traditional dietary customs while incorporating modern nutritional principles. Updated every six years, the latest versions include CHFP-2016 and CHFP-2022 [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Previous studies suggest that adherence to CHFP recommendations is associated with improved survival and reduced recurrence and mortality among breast cancer survivors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, evidence on its protective effect against primary breast cancer incidence remains limited. Additionally, whether such a dietary pattern can be generalized and applied to other populations with different cultural, behavioral, and environmental contexts remains unclear [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e To address these gaps, this study investigates the association between dietary guideline adherence and breast cancer risk using data from the Breast Cancer Cohort Study of Chinese Women (BCCS-CW) and the U.S. National Health and Nutrition Examination Survey (NHANES). By leveraging data from two culturally distinct populations, this study aims to evaluate the cross-cultural generalizability of dietary adherence as a preventive factor against breast cancer. Findings from this research may inform the development of culturally adaptive dietary interventions and contribute to global strategies for breast cancer prevention.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the Breast Cancer Cohort Study (BCCS-CW) and the National Health and Nutrition Examination Survey (NHANES) to conduct matched and weighted case-control analyses. To ensure comparability between the two cohorts, we excluded individuals who (1) were under 20 or over 80 years old, (2) had extremely abnormal total energy intake (less than 600 or more than 3,500 kcal/day), (3) had missing required variable information, or (4) were diagnosed during pregnancy. The detailed data selection process is illustrated in\u003cstrong\u003e\u0026nbsp;Figure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe BCCS-CW, initiated in 2008, is a large, community-based cohort encompassing 15 districts and counties across Shandong, Hebei, and Jiangsu provinces in China. The study employs a multi-stage follow-up design, incorporating community-targeted surveillance, as well as annual data linkage to collect breast cancer-related outcomes. The most recent in-person follow-up occurred between 2019 and 2020, during which dietary intake data were gathered using a standardized Food Frequency Questionnaire (FFQ). Detailed information regarding the BCCS-CW methodology has been previously published\u0026nbsp;[33].\u003c/p\u003e\n\u003cp\u003eNHANES is a population-based, cross-sectional survey designed to assess the health and nutritional status of the U.S. population. Approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, NHANES employs a complex, stratified, multi-stage sampling design to obtain a representative sample of the U.S. population [34, 35]. The NHANES data is publicly available at https://www.cdc.gov/nchs/nhanes/. In this nested case-control study, newly diagnosed breast cancer cases were identified through biopsy and/or surgical pathology, which are considered the gold standard for diagnosis in the BCCS-CW, and by ICD-10 code matching in NHANES. To minimize potential confounding variables and enhance the accuracy of the findings, in the BCCS-CW study, breast cancer-free women were matched to newly diagnosed cases in a 1:3 ratio based on age at diagnosis, timing of the FFQ survey, and geographic region [36]. The NHANES study employed a stratified, multistage probability sampling design, with sample weights applied to account for sampling bias and ensure national representativeness. Therefore, no variable imputation was performed in the NHANES dataset in order to preserve the integrity of the weighted analyses [37-39].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneration of Dietary Recommendation Adherence Scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdherence scores were calculated for each participant based on the CHFP-2016 and CHFP-2022 guidelines. Both versions encompass similar food categories\u0026mdash;including dairy products, beans, meat, poultry, eggs, aquatic products, vegetables, fruits, grains, alcohol, salt, oil, water, and sugar\u0026mdash;with slight variations in the recommended intake levels. Food intake was calculated using the formula: daily intake = consumption frequency \u0026times; amount per occasion. It was subsequently standardized to daily units. For each food category, recommended intake levels were established. Participants meeting or exceeding the recommendations received the highest score, while those with intake levels below the thresholds received the lowest. Intermediate scores were proportionally assigned based on the degree of deviation from the recommended amounts.\u003c/p\u003e\n\u003cp\u003eIn the BCCS-CW cohort, due to the unavailability of data on daily intake of water, sugar, oil, and salt, adherence scores for CHFP-2016 and CHFP-2022 were calculated based on the remaining nine food components. The total adherence scores ranged from 0 (lowest adherence) to 40 (highest adherence; \u003cstrong\u003eTable 1\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;Supplementary Table 1\u003c/strong\u003e). For the NHANES cohort, adherence scores for CHFP-2016 and CHFP-2022 were calculated based on total nutrient intakes from the first day of data collection (DR1TOT), obtained through in-person interviews at mobile examination centers. This analysis included 11 food items (with increased emphasis on sugar and oil compared to BCCS-CW), with total scores ranging from 0 (lowest adherence) to 50 (highest adherence) (\u003cstrong\u003eSupplementary Tables 2 and 3\u003c/strong\u003e). Before generating adherence scores in the BCCS-CW study, dietary variables with missing values in more than two-thirds of all participants were excluded from the final analysis. For the remaining variables with missing data, multiple imputation by chained equations (MICE) was performed. Consistency across imputations was assessed using Cronbach\u0026rsquo;s alpha, and the distribution of the imputed data closely resembled that of the original dataset. Consequently, the mean of these five imputations was utilized to fill in the missing data [40-42].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Chinese Food Guide Pagoda 2022 components and adherence scores in the BCCS-CW\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponents \u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecommended amount of intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard for Maximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard for Minimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum point\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHFP-2022 score \u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI) \u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eDairy products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e300-500 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026gt; 500 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.29(1.02-1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eBeans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e25-35 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026gt; 35 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.48(0.05-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eMeat poultry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e40-75 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt; 75 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026gt; 112.5 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.25\u0026plusmn;1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.58(0.19-1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eEgg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e300-350 g/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt; 350 g/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026gt; 525 g/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.44\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.89(0.79-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAquatic products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e40-75 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026gt; 75 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.90(0.63-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eVegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e300-500 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026gt; 500 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.31\u0026plusmn;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.90(0.77-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eFruits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e200-350 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026gt; 300 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.88\u0026plusmn;1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.79(0.69-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eGrains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e250-400 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026gt; 400 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.35\u0026plusmn;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.75(0.64-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt; 15 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt; 15 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026gt; 22.5 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.99\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.69(0.38-0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eTotal \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e20.11\u0026plusmn;4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.91(0.86-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BC, Breast Cancer; CHFP-2022, Chinese Food Guide Pagoda 2022; OR, Odds Ratio; CI, Confidence Interval; SD, Standard Deviation.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Components, maximum/minimum points and standards were based on Chinese Food Pagoda 2016(CHFP-2016). nine items were included in this study, except daily intake of water, oil, salt and sugar, due to the lack of data.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Intakes between minimum and maximum levels were scored proportionately following the formulas: (actual intake amount / intake amount for maximum point) \u0026times; maximum point, for components with recommendation of lower limit of intake amount; (intake amount for minimum point - actual intake amount) / (intake amount for minimum point - intake amount for maximum point) \u0026times; maximum point, for components with recommendation of upper limit of intake amount. Data were shown as mean \u0026plusmn; SD.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Conditional logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, BMI, marital status, education level, income level, family history of cancer, whether have taken hormones, progesterone, or other female hormones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCommon covariates across both cohorts included age, menopausal status, use of estrogen and progesterone, marital status (married or living with partner, never married, separated or otherwise), Body Mass Index (BMI) (\u0026lt; 25 kg/m\u0026sup2;, \u0026ge; 25 kg/m\u0026sup2;), educational level, and annual income level. Due to differences in educational systems and income measures between Chinese and American residents, we describe educational levels and annual income separately for each cohort. In the BCCS-CW cohort, educational levels are categorized as \u0026le; 6 years, 7-9 years, 10-12 years, and \u0026gt; 12 years; annual income levels are categorized as \u0026lt; 12,000 RMB, 12,000-36,000 RMB, 36,000-60,000 RMB, and \u0026ge; 60,000 RMB. In the NHANES cohort, educational levels are categorized as \u0026lt; 9 years, 9-12 years, and \u0026gt; 12 years; annual income levels are categorized as \u0026lt; 25,000 USD, 25,000-55,000 USD, 55,000-75,000 USD, and \u0026ge; 75,000 USD.\u003c/p\u003e\n\u003cp\u003eAdditionally, given the different data compositions of the two cohorts, we extracted relevant variables separately to account for potential confounding effects. These variables included family history of cancer and hormone use in the BCCS-CW cohort, and ethnicity (White, Black, Hispanic, Other), MET (weekly MET accumulation), total daily caloric intake, and chronic diseases (diabetes, dyslipidemia, and cardiovascular disease) in the NHANES cohort (\u003cstrong\u003eSupplementary Methods\u003c/strong\u003e). These variables were included based on the assumption that patients with these conditions may have altered dietary habits and that these conditions could be associated with higher risks of breast cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were classified into interquartile groups based on the distribution of adherence scores for each dietary recommendation. Baseline characteristics of the different groups were compared using t-tests and Mann\u0026ndash;Whitney U tests for numerical variables, and chi-squared (\u0026chi;\u0026sup2;) tests for categorical variables.\u003c/p\u003e\n\u003cp\u003eConditional logistic regression models and logistic regression models were performed for the BCCS-CW and NHANES cohorts, respectively, to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between dietary adherence and breast cancer risk. The models were adjusted as follows: (1) Model 1: Adjusted for age at survey and menstrual status (i.e., whether menstruation had ceased for over one year). For the NHANES data, daily energy intake was also adjusted. (2) Model 2: Further adjusted for socio-economic factors, including BMI, marital status, education level, and income level. Ethnicity was additionally included in the NHANES analysis. (3) Model 3: Further adjusted for chronic diseases potentially associated with dietary habits and breast cancer risk. In the BCCS-CW cohort, these included family history of cancer and hormone use (e.g., progesterone and other female hormones). In the NHANES cohort, the adjustments included hypertension, diabetes, cardiovascular disease, dyslipidemia, and metabolic equivalent of task (MET). These adjustment variables were selected to influence breast cancer risk or to be related to dietary habits or overall diet quality, thus serving as potential confounders of the diet-breast cancer risk correlation. Therefore, it is important to adjust for these covariates when performing statistical analyses to minimize confounding bias and to better isolate the independent effects of diet quality on breast cancer risk.\u003c/p\u003e\n\u003cp\u003eORs and 95% CIs for each 5-point increment in adherence scores were calculated by treating the scores as continuous variables. Subgroup analyses were performed to assess whether the associations between CHFP scores and breast cancer risk differed by menopausal status, marital status, education level, income level, and BMI. Interactions of adherence scores with potential confounding factors were also tested.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using SPSS 25.0 and R 4.1 software. A \u003cem\u003eP\u003c/em\u003e-value of \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of Study Populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the inclusion criteria, the BCCS-CW cohort comprised a total of 46,980 eligible female participants, including 133 newly diagnosed breast cancer cases. In the NHANES cohort, 8,655 eligible females were included in this analysis, of whom 45 were newly diagnosed with breast cancer, based on data from the 2005\u0026ndash;2016 period. Dietary weights (wtdrd1) were applied to the NHANES dataset to reflect a population size of 71,370,868, ensuring its representativeness of the general U.S. population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of cases and controls were generally comparable across both the BCCS-CW and NHANES cohorts. However, in the NHANES dataset, women with breast cancer were significantly older (60.83 vs. 46.72 years, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001), more likely to be post-menopausal (87.70% vs. 43.8%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001), and had a higher prevalence of underlying cardiovascular diseases (18.67% vs. 5.38%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). In the BCCS-CW cohort, compared to breast cancer free participants, women with breast cancer demonstrated significantly lower adherence scores for both CHFP-2016 (20.26 vs. 21.22, \u003cem\u003eP\u003c/em\u003e = 0.02) and CHFP-2022 (20.22 vs. 21.32,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e = 0.01). Similarly, in the NHANES cohort, breast cancer patients had lower CHFP-2016 scores (30.52 vs. 31.65, \u003cem\u003eP\u003c/em\u003e = 0.04) and CHFP-2022 scores (30.04 vs. 31.05, \u003cem\u003eP\u003c/em\u003e = 0.08). Detailed baseline characteristics are presented in \u003cstrong\u003eTable 2\u003c/strong\u003e (\u003cstrong\u003esee Additional file 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary Adherence and Breast Cancer Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBCCS-CW Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter adjusting for potential confounders, each 5-point increase in adherence scores to CHFP-2016 (OR: 0.69, 95% CI: 0.52-0.91) and CHFP-2022 (OR: 0.66, 95% CI: 0.51-0.87) was associated with a 31% to 34% reduction in breast cancer risk, respectively (Model 1). These associations persisted in further-adjusted analyses, including demographic and socioeconomic factors (Model 2) and covariates of family history and hormone usage (Model 3) for both CHFP-2016 (OR: 0.69, 95% CI: 0.52-0.91) and CHFP-2022 (OR: 0.64, 95% CI: 0.48-0.85; \u003cstrong\u003eFigure 2\u003c/strong\u003e). Compared to women in the lowest quartile of CHFP-2022 adherence scores, participants in the highest quartile displayed a significant reduction in breast cancer risk, with fully adjusted odds ratios (Model 3) of 0.50 (95% CI: 0.28-0.89). A significant linear trend (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e \u0026lt; 0.05) was observed across quartiles of CHFP-2022 scores in the BCCS-CW cohort (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. ORs (95% CI) for breast cancer by quartile of dietary recommendation adherence scores\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ein BCCS-CW and NHANES.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDietary recommendations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoherent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 427px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio (95% CI) by quartile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile1\u003csup\u003eg\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 2 \u003csup\u003eh\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eCHFP-2016 scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 75px;\"\u003e\n \u003cp\u003eBCCS-CW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eCrude OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.00(0.59-1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.73(0.42-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.64(0.36-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.00(0.59-1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.73(0.41-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.64(0.36-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.06(0.62-1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.69(0.39-1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.63(0.35-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.09(0.63-1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.72(0.41-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.64(0.36-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 685px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 75px;\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eCrude OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.23(0.49-3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.24(0.42-3.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.68(0.26-1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 1\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.97(0.36-2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.91(0.28-2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.43(0.15-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 2\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.95(0.37-2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.85(0.25-2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.37(0.13-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 3\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.97(0.39-2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.84(0.26-2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.37(0.13-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 685px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eCHFP-2022 scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 75px;\"\u003e\n \u003cp\u003eBCCS-CW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eCrude OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.69(0.40-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.64(0.37-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.51(0.29-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.69(0.40-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.63(0.36-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.51(0.29-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.71(0.41-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.62(0.35-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.49(0.28-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.73(0.42-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.63(0.36-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.50(0.28-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 685px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 75px;\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eCrude OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.21(0.48-3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.25(0.43-3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.68(0.26-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 1\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.97(0.36-2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.90(0.29-2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.44(0.16-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 2\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.95(0.37-2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.84(0.26-2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.38(0.14-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eModel 3\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.96(0.38-2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.83(0.26-2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.37(0.14-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: CHFP-2016, Chinese Food Guide Pagoda 2016; CHFP-2022, Chinese Food Guide Pagoda 2022; OR, Odds Ratio; CI, Confidence Interval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Conditional logistic regression model was adjusted for age at survey, whether stopped menstruating for over a year,\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Conditional logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, BMI, marital status, education level, income level.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Conditional logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, BMI, marital status, education level, income level, family history of cancer，whether have taken hormones, progesterone, or other female hormones.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Logistic regression model was adjusted for age at survey, whether stopped menstruating for over a year and energy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003e Logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, energy, BMI, ethnic, marital status, education level, income level.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ef\u003c/sup\u003e Logistic regression model was additionally adjusted for age at survey, whether stopped menstruating for over a year, energy, BMI, ethnic, marital status, education level, income level, diabetes, cardiovascular disease, dyslipidemia, MET, whether have taken hormones, progesterone, or other female hormones.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eg\u003c/sup\u003e Q1-Q4: Chinese Food Pagoda 2016 and Chinese Food Pagoda 2022 quartiles of scores\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eh\u003c/sup\u003e OR (95%CI)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNHANES Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the NHANES cohort, a similar protective pattern was observed, with each 5-point increase in adherence scores to CHFP-2016 (OR: 0.69, 95% CI: 0.55-0.86) and CHFP-2022 (OR: 0.70, 95% CI: 0.56-0.88) associated with 30% and 31% reductions in breast cancer-specific risk, respectively (Model 3;\u003cstrong\u003e\u0026nbsp;Figure 2\u003c/strong\u003e). No linear trend in quartiles founded in NHANES. However, protection trend from Q1-Q4 can be seen in NHANES) cohort (\u003cstrong\u003eTable 3\u003c/strong\u003e). Details of baseline characteristics by adherence quartiles for BCCS-CW and NHANES cohorts are presented in \u003cstrong\u003eSupplementary Tables 4 - 7\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStratified and Subgroup Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe observed little evidence of effect modification by most demographic factors and chronic diseases, as shown in \u003cstrong\u003eFigure 3\u003c/strong\u003e and \u003cstrong\u003eSupplementary Tables 8 and 9\u003c/strong\u003e. However, when stratified by racial/ethnic group, we found that higher CHFP adherence score was associated with lower breast cancer risk among White women (adjusted OR: 0.93, 95% CI: 0.89-0.98 for CHFP-2016; adjusted OR: 0.94, 95% CI: 0.90-0.98 for CHFP-2022), but higher risk among Spanish population (adjusted OR: 1.23, 95% CI: 1.45-1.31 for CHFP-2016; adjusted OR: 1.17, 95% CI: 1.12-1.22 for CHFP-2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndividual Dietary Components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses of specific dietary components revealed divergent effects of dairy consumption between the two cohorts, with an inverse association observed in NHANES (OR: 0.78, 95% CI: 0.62-0.98) and a positive association in BCCS-CW (OR: 1.29, 95% CI: 1.02-1.63) (\u003cstrong\u003eTable 1\u003c/strong\u003e, \u003cstrong\u003eSupplementary Tables 1-3\u003c/strong\u003e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study provides compelling evidence that higher adherence to the Chinese Food Pagoda (CHFP) dietary guidelines is significantly associated with a reduced risk of breast cancer among both Chinese and American women. These findings highlight the value of evidence-based dietary frameworks, such as the CHFP, not only in preventing chronic diseases but also in mitigating cancer risk across populations with diverse geographic, cultural, and lifestyle contexts. By extending the application of such guidelines beyond general health promotion, targeted dietary interventions grounded in culturally relevant recommendations may offer broad potential as effective cancer prevention strategies.\u003c/p\u003e\n\u003cp\u003eIn our study, after adjusting for potential confounding factors, each 5-point increase in the CHFP score was associated with a 36% (CHFP-2022) and 31% (CHFP-2016) reduction in breast cancer risk in the BCCS-CW cohort. These findings build upon previous research demonstrating the protective effects of healthy dietary patterns, such as those assessed by indices including the Healthy Eating Index (HEI), Alternative Healthy Eating Index (AHEI), Dietary Inflammatory Index (DII), and Mediterranean Diet Score (MDS) [14, 43, 44]. While these indices have shown strong associations with reduced cancer risk, their applicability is often limited by geographic and cultural constraints. For instance, the HEI and AHEI are tailored to dietary habits in the U.S. [45], while the MDS emphasizes components such as olive oil and seafood, which may be less accessible or affordable in other regions [46, 47]. Although the DII offers mechanistic insights, its complexity hampers public understanding and adoption [48]. In contrast, the CHFP—rooted in cultural relevance and broad dietary guidance—demonstrated consistent protective associations in both Chinese and American populations, indicating that culturally tailored dietary frameworks may offer broader applicability than universal indices. The protective effect of CHFP may stem from its emphasis on whole grains and plant-based proteins, which modulate key pathways in breast carcinogenesis, including estrogen metabolism and inflammatory responses. This cross-cultural relevance highlights the value of culturally informed dietary frameworks in cancer prevention research. \u003c/p\u003e\n\u003cp\u003eDespite the overall positive association between dietary adherence and breast cancer risk, our study also indicated differences in adherence patterns between the two cohorts, with NHANES participants generally exhibiting better adherence scores compared to those in the BCCS-CW cohort. This distinction may be attributed to variations in socioeconomic factors, public awareness, and access to dietary resources. The generally higher adherence scores observed in the American cohort may reflect the longstanding implementation of nutrition education campaigns, government-supported dietary programs, and specific public health interventions that promote adherence to dietary recommendations [49-51]. In contrast, while dietary patterns in China have improved in recent decades [52], intake of some key food groups, such as dairy products, soybeans, and aquatic products, remains below recommended levels. For instance, the Diet Quality Divergence Index for China has shown that while there is an upward trend in the consumption of fruits and vegetables, the intake of other essential food groups, such as dairy and protein sources, remains suboptimal [31, 53]. This gap highlights the need for targeted public health interventions to promote balanced dietary patterns that meet nutritional guidelines among Chinese women.\u003c/p\u003e\n\u003cp\u003eInterestingly, in addition to demonstrating the general protective effect of CHFP adherence, our study revealed population-specific associations with individual food components. For instance, dairy consumption was associated with a reduced risk of breast cancer in the NHANES cohort, whereas an increased risk was observed in the BCCS-CW cohort. This divergence reflects ongoing controversy surrounding the role of dairy products in breast cancer etiology [54, 55], with studies reporting conflicting findings. Some research suggests that moderate dairy intake may confer health benefits, potentially due to bioactive components such as calcium, vitamin D, and peptides, which have been shown to inhibit the proliferation, migration, and invasion of breast cancer cells [10, 56, 57]. Conversely, concerns remain about residual sex steroid hormones in milk, which may disrupt hormonal balance and elevate cancer risk [55]. Moreover, the impact of dairy appears to differ by type and population subgroup: fermented dairy may offer protection in postmenopausal women, while high-fat dairy could increase risk, and low-fat dairy may be protective among premenopausal women [58-60]. These findings underscore the complexity of diet-disease relationships and highlight the need to consider regional dietary habits, food processing methods, and socio-environmental factors when interpreting epidemiological associations. Although the CHFP demonstrated a protective effect in both White and Asian populations in our study, this effect appeared attenuated in the Spanish population, potentially due to limited sample size, divergent dietary behaviors, or genetic susceptibility. Further research is warranted to explore ethnic and regional disparities in dietary risk factors for cancer. Additionally, we observed that the protective effect of CHFP adherence was more pronounced in participants without dyslipidemia, likely attributable to an overall healthier lifestyle in this subgroup. However, dyslipidemia could also act as a biological or behavioral confounder, diminishing the diet’s protective effect on breast cancer risk [61]. Given emerging evidence on the role of lipids in carcinogenesis [62], further mechanistic studies are needed to clarify the interactions among dyslipidemia, dietary intake, and breast cancer development.\u003c/p\u003e\n\u003cp\u003eOne of the key strengths of our study lies in the comprehensive assessment of adherence to dietary guidelines across both Chinese and American populations, utilizing two large, culturally diverse cohorts. This cross-national design enhances the robustness and generalizability of our findings. Notably, the CHFP demonstrated strong cross-cultural applicability, further supporting its potential as a globally adaptable dietary framework. In addition, the use of a nested case-control design, combined with rigorous matching and inverse probability weighting methods, helped to reduce potential confounding and selection biases. However, several limitations should be acknowledged. First, despite the strengths of the nested case-control design in minimizing recall bias, its retrospective nature limits the ability to infer a definitive causal relationship between CHFP adherence and breast cancer risk. Second, although the food frequency questionnaires (FFQs) used were previously validated and administered via in-person interviews, recall bias and measurement error may still have affected the accuracy of dietary intake estimates and adherence scores. Future research employing more objective dietary assessment methods, such as 24-hour dietary recalls or nutritional biomarkers, is warranted to improve measurement precision. Third, our findings are limited to Chinese and American populations, and further validation in other ethnic and geographic groups is needed to ensure broader applicability. Finally, although we adjusted for key sociodemographic and lifestyle variables, unmeasured confounding factors—particularly those related to cultural practices, regional food availability, and habitual dietary patterns—may still influence the observed associations. Future studies should further explore the interplay between cultural, environmental, and genetic factors in shaping diet–cancer relationships.\u003c/p\u003e\n\n"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, our study demonstrates that adherence to the CHFP guideline is significantly associated with a reduced risk of breast cancer across populations with substantial cultural differences. Notably, the consistency of the observed protective effects across distinct cohorts underscores the potential for CHFP-based dietary recommendations to be intercultural adapted and applied internationally. Further research is warranted to elucidate the influence of cultural, geographic, and habitual factors in the relationship between diet and cancer risk. \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBC: Breast Cancer,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHFP-2016: Chinese Food Guide Pagoda 2016,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHFP-2022: Chinese Food Guide Pagoda 2022,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBCCS-CW: the Breast Cancer Study of Chinese Women,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNHANES: the National Health and Nutrition Examination Survey.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the National Natural Science Foundation of China (Grant No. 82003526), National Key Research and Development Program of China (2016YF0901301), and the Research Grant of the Second Hospital of Shandong University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFW conceptualized the study and designed the experiments. SY, YS, XL, and KW performed the methodology. SY conducted the data analysis. FW and SY drafted the original manuscript. FW and ZY supervised the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 82003526), National Key Research and Development Program of China (2016YF0901301), and the Research Grant of the Second Hospital of Shandong University. Fei Wang is supported by the Taishan Scholarship of Shandong Province, the Qilu Scholar-in-Training Award of Shandong University, and the Young Elite Sponsorship Program of Shandong Provincial Medical Association. Zhigang Yu is supported by the Taishan Scholarship of Shandong Province.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDeSantis C, Siegel R, Bandi P, Jemal A. Breast cancer statistics, 2011. CA-CANCER J CLIN. [Journal Article; Review]. 2011 2011-11-1;61(6):409-18.\u003c/li\u003e\n\u003cli\u003eDiet, Nutrition, Physical Activity and Cancer : a Global Perspective.\u003c/li\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA-CANCER J CLIN. [Journal Article]. 2023 2023-1-1;73(1):17-48.\u003c/li\u003e\n\u003cli\u003eSharman R, Harris Z, Ernst B, Mussallem D, Larsen A, Gowin K. Lifestyle Factors and Cancer: A Narrative Review. Mayo Clin Proc Innov Qual Outcomes. [Journal Article]. 2024 2024-4-1;8(2):166-83.\u003c/li\u003e\n\u003cli\u003eParkinson MD, Stout R, Dysinger W. Lifestyle Medicine: Prevention, Treatment, and Reversal of Disease. MED CLIN N AM. [Journal Article; Review]. 2023 2023-11-1;107(6):1109-20.\u003c/li\u003e\n\u003cli\u003eMinich DM, Bland JS. Personalized lifestyle medicine: relevance for nutrition and lifestyle recommendations. ScientificWorldJournal. [Journal Article]. 2013 2013-1-20;2013:129841.\u003c/li\u003e\n\u003cli\u003eXiao YL, Gong Y, Qi YJ, Shao ZM, Jiang YZ. Effects of dietary intervention on human diseases: molecular mechanisms and therapeutic potential. SIGNAL TRANSDUCT TAR. [Journal Article; Review]. 2024 2024-3-11;9(1):59.\u003c/li\u003e\n\u003cli\u003eAnderson JJ, Darwis N, Mackay DF, Celis-Morales CA, Lyall DM, Sattar N, et al. Red and processed meat consumption and breast cancer: UK Biobank cohort study and meta-analysis. EUR J CANCER. [Journal Article; Meta-Analysis; Research Support, Non-U.S. Gov\u0026apos;t; Systematic Review]. 2018 2018-2-1;90:73-82.\u003c/li\u003e\n\u003cli\u003ePapadimitriou N, Markozannes G, Kanellopoulou A, Critselis E, Alhardan S, Karafousia V, et al. An umbrella review of the evidence associating diet and cancer risk at 11 anatomical sites. NAT COMMUN. [Journal Article; Meta-Analysis; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov\u0026apos;t; Systematic Review]. 2021 2021-7-28;12(1):4579.\u003c/li\u003e\n\u003cli\u003eFraser GE, Jaceldo-Siegl K, Orlich M, Mashchak A, Sirirat R, Knutsen S. Dairy, soy, and risk of breast cancer: those confounded milks. INT J EPIDEMIOL. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov\u0026apos;t]. 2020 2020-10-1;49(5):1526-37.\u003c/li\u003e\n\u003cli\u003eDietary patterns and cancer risk.\u003c/li\u003e\n\u003cli\u003eWang X, Chen L, Cao R, Ma R, Liu Y, Zhao Q, et al. Associations of 10 dietary habits with breast cancer: a Mendelian randomization study. FRONT NUTR. [Journal Article]. 2023 2023-1-20;10:1215220.\u003c/li\u003e\n\u003cli\u003ePorciello G, Coluccia S, Vitale S, Palumbo E, Luongo A, Grimaldi M, et al. Baseline Association between Healthy Eating Index-2015 and Health-Related Quality of Life in Breast Cancer Patients Enrolled in a Randomized Trial. CANCERS. [Journal Article]. 2024 2024-7-18;16(14).\u003c/li\u003e\n\u003cli\u003eDela CR, Park SY, Shvetsov YB, Boushey CJ, Monroe KR, Le Marchand L, et al. Diet Quality and Breast Cancer Incidence in the Multiethnic Cohort. EUR J CLIN NUTR. [Journal Article; Research Support, N.I.H., Extramural]. 2020 2020-12-1;74(12):1743-7.\u003c/li\u003e\n\u003cli\u003ePetimar J, Park YM, Smith-Warner SA, Fung TT, Sandler DP. Dietary index scores and invasive breast cancer risk among women with a family history of breast cancer. AM J CLIN NUTR. [Journal Article; Research Support, N.I.H., Extramural; Research Support, N.I.H., Intramural; Research Support, Non-U.S. Gov\u0026apos;t]. 2019 2019-5-1;109(5):1393-401.\u003c/li\u003e\n\u003cli\u003eErgas IJ, Cespedes FE, Bradshaw PT, Roh JM, Kwan ML, Cadenhead J, et al. Diet Quality and Breast Cancer Recurrence and Survival: The Pathways Study. JNCI CANCER SPECT. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov\u0026apos;t]. 2021 2021-4-1;5(2).\u003c/li\u003e\n\u003cli\u003eHariharan R, Odjidja EN, Scott D, Shivappa N, H\u0026eacute;bert JR, Hodge A, et al. The dietary inflammatory index, obesity, type 2 diabetes, and cardiovascular risk factors and diseases. OBES REV. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t; Review]. 2022 2022-1-1;23(1):e13349.\u003c/li\u003e\n\u003cli\u003eFowler ME, Akinyemiju TF. Meta-analysis of the association between dietary inflammatory index (DII) and cancer outcomes. INT J CANCER. [Journal Article; Meta-Analysis; Review; Systematic Review]. 2017 2017-12-1;141(11):2215-27.\u003c/li\u003e\n\u003cli\u003eKey TJ, Bradbury KE, Perez-Cornago A, Sinha R, Tsilidis KK, Tsugane S. Diet, nutrition, and cancer risk: what do we know and what is the way forward? BMJ-BRIT MED J. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2020 2020-3-5;368:m511.\u003c/li\u003e\n\u003cli\u003eChlebowski RT, Aragaki AK, Anderson GL, Pan K, Neuhouser ML, Manson JE, et al. Dietary Modification and Breast Cancer Mortality: Long-Term Follow-Up of the Women\u0026apos;s Health Initiative Randomized Trial. J CLIN ONCOL. [Journal Article; Multicenter Study; Randomized Controlled Trial; Research Support, N.I.H., Extramural]. 2020 2020-5-1;38(13):1419-28.\u003c/li\u003e\n\u003cli\u003eBiruete A, Leal-Escobar G, Espinosa-Cuevas \u0026Aacute;, Mojica L, Kistler BM. Dieta de la Milpa: A Culturally-Concordant Plant-Based Dietary Pattern for Hispanic/Latine People with Chronic Kidney Disease. NUTRIENTS. [Journal Article; Review]. 2024 2024-2-20;16(5).\u003c/li\u003e\n\u003cli\u003ePourmasoumi M, Karimbeiki R, Vosoughi N, Feizi A, Ghiasvand R, Barak F, et al. Healthy Eating Index/Alternative Healthy Eating Index and Breast Cancer Mortality and Survival: A Systematic Review and Meta-analysis. ASIA-PAC J ONCOL NUR. [Journal Article]. 2016 2016-7-1;3(3):297-305.\u003c/li\u003e\n\u003cli\u003eHayati Z, Jafarabadi MA, Pirouzpanah S. Dietary inflammatory index and breast cancer risk: an updated meta-analysis of observational studies. EUR J CLIN NUTR. [Journal Article; Meta-Analysis; Review]. 2022 2022-8-1;76(8):1073-87.\u003c/li\u003e\n\u003cli\u003eSteck SE, Guinter M, Zheng J, Thomson CA. Index-based dietary patterns and colorectal cancer risk: a systematic review. ADV NUTR. [Journal Article; Research Support, N.I.H., Extramural; Review; Systematic Review]. 2015 2015-11-1;6(6):763-73.\u003c/li\u003e\n\u003cli\u003eCastro-Espin C, Bonet C, Crous-Bou M, Nadal-Zaragoza N, Tj\u0026oslash;nneland A, Mellemkj\u0026aelig;r L, et al. Association of Mediterranean diet with survival after breast cancer diagnosis in women from nine European countries: results from the EPIC cohort study. BMC MED. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2023 2023-6-26;21(1):225.\u003c/li\u003e\n\u003cli\u003eBordoni A. Insight into the Sustainability of the Mediterranean Diet: The Water Footprint of the Recommended Italian Diet. NUTRIENTS. [Journal Article]. 2023 2023-5-5;15(9).\u003c/li\u003e\n\u003cli\u003eWang SS, Lay S, Yu HN, Shen SR. Dietary Guidelines for Chinese Residents (2016): comments and comparisons. J ZHEJIANG UNIV-SC B. [Journal Article]. 2016 2016-9-1;17(9):649-56.\u003c/li\u003e\n\u003cli\u003eChinese Food Guide Pagoda(2016).\u003c/li\u003e\n\u003cli\u003eChinese Food Guide Pagoda(2022).\u003c/li\u003e\n\u003cli\u003eWang F, Cai H, Gu K, Shi L, Yu D, Zhang M, et al. Adherence to Dietary Recommendations among Long-Term Breast Cancer Survivors and Cancer Outcome Associations. CANCER EPIDEM BIOMAR. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov\u0026apos;t; Research Support, U.S. Gov\u0026apos;t, Non-P.H.S.]. 2020 2020-2-1;29(2):386-95.\u003c/li\u003e\n\u003cli\u003eYu D, Zhang X, Xiang YB, Yang G, Li H, Gao YT, et al. Adherence to dietary guidelines and mortality: a report from prospective cohort studies of 134,000 Chinese adults in urban Shanghai. AM J CLIN NUTR. [Journal Article; Meta-Analysis; Research Support, N.I.H., Extramural; Review]. 2014 2014-8-1;100(2):693-700.\u003c/li\u003e\n\u003cli\u003eNguyen S, Li H, Yu D, Gao J, Gao Y, Tran H, et al. Adherence to dietary recommendations and colorectal cancer risk: results from two prospective cohort studies. INT J EPIDEMIOL. [Journal Article; Research Support, N.I.H., Extramural]. 2020 2020-2-1;49(1):270-80.\u003c/li\u003e\n\u003cli\u003eBao H, Liu L, Cong S, Wang F, Yu L, Fang L, et al. Breast Cancer Awareness and Association with Frequency of Screening Among Women - China, 2020. CHINA CDC WEEKLY. [Journal Article]. 2023 2023-4-14;5(15):327-32.\u003c/li\u003e\n\u003cli\u003ePaulose-Ram R, Graber JE, Woodwell D, Ahluwalia N. The National Health and Nutrition Examination Survey (NHANES), 2021-2022: Adapting Data Collection in a COVID-19 Environment. AM J PUBLIC HEALTH. [Journal Article]. 2021 2021-12-1;111(12):2149-56.\u003c/li\u003e\n\u003cli\u003eNational Health and Nutrition Examination Survey.\u003c/li\u003e\n\u003cli\u003eArtru F, Sacleux SC, Ursic-Bedoya J, Ntandja WL, Lutu A, L\u0026apos;Hermite S, et al. Long-term outcome following liver transplantation of patients with ACLF grade 3. J HEPATOL. [Journal Article; Multicenter Study]. 2025 2025-1-1;82(1):62-71.\u003c/li\u003e\n\u003cli\u003eLiu F, Ran Q, Zhang H, Chen J. The Systemic Immune-Inflammation Index and the Risk of Parkinson\u0026apos;s Disease in the U.S.: A Cross-Sectional Study. J CLIN MED. [Journal Article]. 2025 2025-1-10;14(2).\u003c/li\u003e\n\u003cli\u003ePan S, Chen S. Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health. INT J ENV RES PUB HE. [Journal Article; Research Support, N.I.H., Extramural]. 2023 2023-1-14;20(2).\u003c/li\u003e\n\u003cli\u003eAbellana SR, Farran CA. The identification, impact and management of missing values and outlier data in nutritional epidemiology. NUTR HOSP. [Journal Article; Review]. 2015 2015-2-26;31 Suppl 3:189-95.\u003c/li\u003e\n\u003cli\u003eJunaid KP, Kiran T, Gupta M, Kishore K, Siwatch S. How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations. POPUL HEALTH METR. [Journal Article]. 2025 2025-2-1;23(1):2.\u003c/li\u003e\n\u003cli\u003eHolcomb LA, Maldonado L, Nietert PJ, Hayes MA, Witcraft SM, Newman RB, et al. Examining the role of social determinants of health in maternal mental health screening and treatment engagement during the perinatal period. BIOL SEX DIFFER. [Journal Article]. 2025 2025-2-12;16(1):11.\u003c/li\u003e\n\u003cli\u003eJunaid KP, Kiran T, Gupta M, Kishore K, Siwatch S. How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations. POPUL HEALTH METR. [Journal Article]. 2025 2025-2-1;23(1):2.\u003c/li\u003e\n\u003cli\u003eKord-Varkaneh H, Salehi-Sahlabadi A, Zarezade M, Rahmani J, Tan SC, Hekmatdoost A, et al. Association between Healthy Eating Index-2015 and Breast Cancer Risk: A Case-Control Study. Asian Pac J Cancer Prev. [Journal Article]. 2020 2020-5-1;21(5):1363-7.\u003c/li\u003e\n\u003cli\u003eLong M, Fan X, Wang M, Liu X, Fu C, Huang J, et al. Plasma metabolomic signature of a proinflammatory diet in relation to breast cancer risk: a prospective cohort study. AM J CLIN NUTR. [Journal Article]. 2025 2025-1-11.\u003c/li\u003e\n\u003cli\u003ePetersen KS, Kris-Etherton PM. Diet Quality Assessment and the Relationship between Diet Quality and Cardiovascular Disease Risk. NUTRIENTS. [Journal Article; Review]. 2021 2021-11-28;13(12).\u003c/li\u003e\n\u003cli\u003eWang Y, Chen GC, Wang Z, Luo K, Zhang Y, Li Y, et al. Dietary Acculturation Is Associated With Altered Gut Microbiome, Circulating Metabolites, and Cardiovascular Disease Risk in US Hispanics and Latinos: Results From HCHS/SOL. CIRCULATION. [Journal Article]. 2024 2024-7-16;150(3):215-29.\u003c/li\u003e\n\u003cli\u003eCastro-Espin C, Bonet C, Crous-Bou M, Nadal-Zaragoza N, Tj\u0026oslash;nneland A, Mellemkj\u0026aelig;r L, et al. Association of Mediterranean diet with survival after breast cancer diagnosis in women from nine European countries: results from the EPIC cohort study. BMC MED. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2023 2023-6-26;21(1):225.\u003c/li\u003e\n\u003cli\u003eMarx W, Veronese N, Kelly JT, Smith L, Hockey M, Collins S, et al. The Dietary Inflammatory Index and Human Health: An Umbrella Review of Meta-Analyses of Observational Studies. ADV NUTR. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t; Systematic Review]. 2021 2021-10-1;12(5):1681-90.\u003c/li\u003e\n\u003cli\u003eMirowsky J, Ross CE. Education, Health, and the Default American Lifestyle. J HEALTH SOC BEHAV. [Journal Article]. 2015 2015-9-1;56(3):297-306.\u003c/li\u003e\n\u003cli\u003eAspry KE, Van Horn L, Carson J, Wylie-Rosett J, Kushner RF, Lichtenstein AH, et al. Medical Nutrition Education, Training, and Competencies to Advance Guideline-Based Diet Counseling by Physicians: A Science Advisory From the American Heart Association. CIRCULATION. [Journal Article; Review]. 2018 2018-6-5;137(23):e821-41.\u003c/li\u003e\n\u003cli\u003eMozaffarian D, Aspry KE, Garfield K, Kris-Etherton P, Seligman H, Velarde GP, et al. \u0026quot;Food Is Medicine\u0026quot; Strategies for Nutrition Security and Cardiometabolic Health Equity: JACC State-of-the-Art Review. J AM COLL CARDIOL. [Journal Article; Review]. 2024 2024-2-27;83(8):843-64.\u003c/li\u003e\n\u003cli\u003eLian Y, Gu L, Yang L, Wang L, Li H. The Reasonableness and Spatial Differences of the Food Consumption Structure of Urban and Rural Residents in China, 2015-2021. FOODS. [Journal Article]. 2023 2023-5-15;12(10).\u003c/li\u003e\n\u003cli\u003eZhang J, Wang H, Du W, Su C, Jia X, Huang F, et al. [Food intake among adult residents in 10 provinces (autonomous regions) of China in 2022-2023]. Wei Sheng Yan Jiu. [English Abstract; Journal Article]. 2024 2024-11-1;53(6):870-3.\u003c/li\u003e\n\u003cli\u003eScialo TE, Pace CM, Abrams DI. The Dairy and Cancer Controversy: Milking the Evidence. CURR ONCOL REP. [Journal Article; Review]. 2024 2024-3-1;26(3):191-9.\u003c/li\u003e\n\u003cli\u003eGao P, Li C, Gong Q, Liu L, Qin R, Liu J. Sex steroid hormone residues in milk and their potential risks for breast and prostate cancer. FRONT NUTR. [Journal Article; Review]. 2024 2024-1-20;11:1390379.\u003c/li\u003e\n\u003cli\u003eZhang D, Yuan Y, Xiong J, Zeng Q, Gan Y, Jiang K, et al. Anti-breast cancer effects of dairy protein active peptides, dairy products, and dairy protein-based nanoparticles. FRONT PHARMACOL. [Journal Article; Review]. 2024 2024-1-20;15:1486264.\u003c/li\u003e\n\u003cli\u003eHe Y, Tao Q, Zhou F, Si Y, Fu R, Xu B, et al. The relationship between dairy products intake and breast cancer incidence: a meta-analysis of observational studies. BMC CANCER. [Journal Article; Meta-Analysis]. 2021 2021-10-15;21(1):1109.\u003c/li\u003e\n\u003cli\u003eRonco AL, De St\u0026eacute;fani E, D\u0026aacute;ttoli R. Dairy foods and risk of breast cancer: a case-control study in Montevideo, Uruguay. EUR J CANCER PREV. [Comparative Study; Evaluation Study; Journal Article]. 2002 2002-10-1;11(5):457-63.\u003c/li\u003e\n\u003cli\u003eMoorman PG, Terry PD. Consumption of dairy products and the risk of breast cancer: a review of the literature. AM J CLIN NUTR. [Journal Article; Review]. 2004 2004-7-1;80(1):5-14.\u003c/li\u003e\n\u003cli\u003eKroenke CH, Kwan ML, Sweeney C, Castillo A, Caan BJ. High- and low-fat dairy intake, recurrence, and mortality after breast cancer diagnosis. JNCI-J NATL CANCER I. [Journal Article; Research Support, N.I.H., Extramural]. 2013 2013-5-1;105(9):616-23.\u003c/li\u003e\n\u003cli\u003eNelson ER, Wardell SE, Jasper JS, Park S, Suchindran S, Howe MK, et al. 27-Hydroxycholesterol links hypercholesterolemia and breast cancer pathophysiology. SCIENCE. [Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov\u0026apos;t, Non-P.H.S.]. 2013 2013-11-29;342(6162):1094-8.\u003c/li\u003e\n\u003cli\u003eLiu W, Chakraborty B, Safi R, Kazmin D, Chang CY, McDonnell DP. Dysregulated cholesterol homeostasis results in resistance to ferroptosis increasing tumorigenicity and metastasis in cancer. NAT COMMUN. [Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov\u0026apos;t, Non-P.H.S.]. 2021 2021-8-24;12(1):5103.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, Dietary guidelines, Cross-cultural comparison, Cancer prevention","lastPublishedDoi":"10.21203/rs.3.rs-6867878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6867878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDiet is a key modifiable risk factor for breast cancer. This study examined the adherence to the Chinese Food Pagoda (CHFP) dietary guidelines in association with breast cancer risk in women from two culturally distinct cohorts in China and the United States.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eNested case-control studies were conducted among 133 and 45 breast cancer cases within two cohorts: 46,980 women from the Breast Cancer Study of Chinese Women (BCCS-CW) and 8,655 women from the National Health and Nutrition Examination Survey (NHANES), respectively. Adherence to both the CHFP-2022 and CHFP-2016 dietary guidelines was evaluated using a scoring system based on food group recommendations. The primary outcome was the association between adherence to CHFP guidelines and breast cancer risk, assessed by adjusted odds ratios (ORs) and 95% confidence intervals (CIs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHigher CHFP adherence was associated with a 34\u0026ndash;36% lower breast cancer risk per 5-point increase of CHFP-2022 score in BCCS-CW (adjusted OR: 0.64, 95%CI: 0.48\u0026ndash;0.85) and 27%-30% in NHANES (adjusted OR: 0.70, 95%CI: 0.56\u0026ndash;0.88) cohort. Women in the highest quartile of adherence had substantially lower risk than their counterparts in the lowest quartiles in BCCS-CW cohort (OR: 0.50, 95% CI: 0.28\u0026ndash;0.89). Risk reductions were consistent across subgroups defined by menopausal status, Body Mass Index, education, income, marital status and the use of estrogen and progesterone, and similar for CHFP-2016 adherence score. Population-specific effects with individual food components were observed, with dairy products associated with lower risk in the NHANES cohort (adjusted OR: 0.78, 95%CI: 0.62\u0026ndash;0.98) but higher risk in BCCS-CW cohort (adjusted OR: 1.29, 95%CI: 1.02\u0026ndash;1.63).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study provides the first evidence that adherence to a culturally adapted dietary framework may be associated with a reduced risk of breast cancer across diverse populations. Further research is warranted to confirm causality and explore population-specific dietary influences.\u003c/p\u003e","manuscriptTitle":"Adherence to Culturally Tailored Dietary Guideline and Breast Cancer Risk Reduction: A Cross-Cultural Nested Case-Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:40:12","doi":"10.21203/rs.3.rs-6867878/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-21T14:12:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-31T14:36:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T09:35:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8420163415133298419217197851232318003","date":"2025-07-21T08:02:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255197230789256734099956455239080474142","date":"2025-07-19T09:39:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309842347166714206841570122998050909778","date":"2025-07-17T08:35:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T09:44:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-13T10:05:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-12T04:48:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-12T04:45:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-06-11T04:41:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2655c4b-1d90-4000-a834-c277acf5fdc5","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T14:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 10:40:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6867878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6867878","identity":"rs-6867878","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}