Fish consumption in relation to breast cancer: A case-control study

Fish consumption in relation to breast cancer: A 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fish consumption in relation to breast cancer: A case-control study Camellia Akhgarjand, Federica Fogacci, Sanaz Benisi-Kohansal, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4202460/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Even though regular fish consumption is highly recommended to reduce the risk of developing several cancers, available evidence on the association between breast cancer and fish is inconclusive. We aimed to explore the association between fish consumption and breast cancer in a well-characterized population of Iranian women. Methods: This study enrolled 350 newly diagnosed stage I-IV breast cancer patients and 700 cancer-free controls living in Isfahan. Controls had no family history of breast cancer and were matched to cancer patients in term of age and socioeconomic status (SES). Dietary intakes were determined using a validated food frequency questionnaire (DS-FFQ). In addition, data on potential confounding factors were collected using pre-tested questionnaires. Results: After adjusting for potential confounders, women in the highest quartile of fish consumption had a lower odds of breast cancer than those in the lowest quartile (OR: 0.57, 95% CI: 0.34-0.95). The stratified analysis by menopausal status revealed an inverse association in postmenopausal women (OR: 0.53, 95% CI: 0.30-0.94). Conclusions: Our findings suggest higher fish consumption is associated with a lower likelihood of having breast cancer, particularly in post-menopause. The findings should be interpreted with caution due to the case-control study design. To further examine this association, prospective studies are needed. Fish seafood diet breast cancer case-control study INTRODUCTION Breast cancer is the most frequently diagnosed type of cancer and the fifth biggest cause of cancer-related death globally [ 1 ]. Each woman has a 13% risk of breast cancer in the United States. This translates to one in every eight women developing breast cancer in the life course [ 2 ]. According to the Global Cancer Statistics 2020, 2.3 million women were diagnosed with breast cancer resulting in 685,000 deaths worldwide, which has more than doubled over the last two decades[ 3 ]. Breast cancer in recent years, in underdeveloped countries have soared [ 4 ]. Geographic variation in the incidence rate of breast cancer may be attributed to modifiable lifestyle factors, highlighting the need for preventive approaches. Aside from family history and genetics, several environmental and lifestyle-related factors are the main drivers in the etiology of breast cancer; among them, diet has received the greatest attention [ 5 ]. In particular, fish intake has long been studied in relation to various health outcomes. Fish is the most abundant dietary source of omega-3 long-chain polyunsaturated fatty acids (n-3 LCPUFAs), as well as protein, vitamin D, B vitamins, calcium, selenium, zinc, magnesium, and other nutrients [ 6 , 7 ]. It has previously been linked to a lower risk of all-cause mortality [ 8 ], coronary heart disease incidence and mortality [ 9 ], diabetes [ 10 ], stroke [ 11 ], and several cancers [ 12 – 14 ]. Its consumption has also been examined in relation to breast cancer; however, findings were inconsistent. Even though some studies reported a significant protective association [ 15 , 16 ], others failed to reach such an association [ 17 – 19 ]. In general, the research results appear conflicting. The significance of the results may be affected by the varying levels of fish intake among nations. Due to vast geographical differences, the demographics of breast cancer patients in developing countries are distinct from those in industrialized nations. Changes in dietary habits and lifestyle as a result of globalization and economic development, as well as the availability of breast cancer screening programs, may influence the breast cancer risk of the population. The majority of studies on the association between fish consumption and health outcomes have been conducted in countries with a high fish intake. Then, it remains unknown if the favorable contribution of fish intake to human health exists in countries with a low fish intake as well. This is particularly relevant for people residing in the Middle East, where fish consumption is low and dietary intakes are differ from other parts of the world [ 20 ]. Given the increasing incidence of breast cancer among women along with nutrition transition in this region, this study was designed to assess the relationship between fish consumption and odds of breast cancer in Iranian females. METHODS Study population This population-based case-control study included women over the age of 30 in Isfahan, Iran. Enrollment of study participants started in 2013 and ended in 2015. We recruited women with breast cancer using convenience sampling from patients referred to hospitals or private clinics who were undergoing chemotherapy, surgical tumor resection, or radiotherapy. The diagnosis was based on physical examination, mammography exam, and pathological confirmation performed during the maximum of last six months. Women with breast cancer included in the study had been diagnosed with invasive primary breast tumors, and whose histological medical records were available. Age-matched controls (± 5 years) were randomly selected among healthy women in the healthcare institutions in Isfahan, considering their living place as a measure of socio-economic status (SES). Inclusion/Exclusion criteria for the control group were (i) being women, (ii) having Iranian nationality, (iii) no history of any malignancy, cystic mass, and pathological states, (iv) no prior history of hormone replacement therapy, (v) following no specific diet-programs. Dietary intakes assessment Usual dietary intakes of the study population during the previous year were obtained using a dish-based Willett-format 106-item semiquantitative food frequency questionnaire (DS-FFQ) [ 21 ]. The questionnaire validity and details about its design and food items were previously reported [ 21 ]. Briefly, this questionnaire included five groups of foods and dishes: (1) cooked or canned foods called mixed dishes (29 items); (2) foods that are based on carbohydrates (various kinds of potatoes, biscuits, bread, cakes, ten items); (3) dairies (butter, yogurt, and cream, nine items); (4) fruit and vegetables (22 items); and (5) miscellaneous food products and beverages (sweets, junk foods, nuts, desserts and drinks, 36 items). The consumption frequency of each food item was questioned using nine multiple-choice responses ranging from “never or less than once a month” to “12 or more times per day”. The consumption frequency of fish items was assessed using six multiple-choice frequency responses ranging from "never or less than once a month" to "1–2 times per day". A trained nutritionist performed face-to-face interviews to complete the DS-FFQ. Study participants were requested to report their habitual intakes of food items during the last year. All reported frequencies were converted to grams per day considering given portion sizes in the questionnaire. This was done by a nutritionist using a previously published booklet on household measures of foods [ 22 ]. The Nutritionist IV Software (based on the USDA nutrient databank and modified for Iranian foods) was utilized to assess the participants' daily nutrient intakes. Fish intake was calculated by summing up total fish and canned tuna. The reliability and validity of the DS-FFQ were investigated in a subset of 200 randomly selected participants [ 21 ]. The DS-FFQ was completed by all participants in the validation study at the study baseline and six months later. Three detailed dietary records were obtained from participants for this validation study, which served as the gold standard. According to the findings of this study, the DS-FFQ may indicate reasonably valid and reliable measures of long-term dietary intakes in the Iranian population; for example, the dietary carbohydrate intake as determined by the DS-FFQ was considerably correlated with the average of three dietary records (r = 0.81). Breast cancer assessment Breast cancer diagnosis was made by physical examination and mammographic exams that final conformation was determined by pathological tests. All breast cancer patients were females of Iranian nationality with newly diagnosed breast cancer of stage I-IV. Assessment of covariates An overall pretested socio-economic status questionnaire filled by face-to-face interview was applied to collect information on age, residential area, education, family history of breast cancer in their immediate family, alcohol drinking, smoking status, marital status, menopausal status, prior lactating, supplement use, and past disease history. Body weight was measured with light clothing and without shoes, using a digital scale (Seca, Hamburg, Germany) to the nearest 0.1 Kg. Height was quantified without shoes, in the standing position, and near to the wall by employing a tape meter to the nearest 0.5 cm. Body mass index (BMI) was computed by dividing weight in kilograms by height per square meters. Participants were categorized according to BMI into “normal” and “overweight or obese” (18.5–24.9 and > 25 Kg/m 2 , respectively). Short-form translated Persian International Physical Activity Questionnaire (IPAQ) was employed through face-to-face interviews to assess participants’ physical activity. This translated form has been proved in previous studies to describe the exact information about physical activity among the people of Iran [ 23 – 26 ]. The information obtained from IPAQ was expressed as a metabolic equivalent time per week (METs/week). Statistical analysis The sample size calculation was according to the hypothesis that an unhealthy diet might increase breast cancer odds by 1.5 times. Considering a type I error of 5%, a study power of 80%, a typical ratio of 0.25, and a ratio of controls to cases as 2, the required sample size was estimated at almost 350 breast cancer patients and 700 seemingly healthy controls. Participants were divided into quartiles based on their fish consumption. To begin, the energy-adjusted dietary intake of fish was calculated using the residual method [ 27 ]. To examine differences in quantitative variables across quartiles of fish consumption and between cases and controls, we respectively used one-way analysis of variance (ANOVA) and independent samples' t-test (preceded by the Levene’s test for homogeneity of variances). The chi-square test was used to investigate the distribution of categorical variables across fish consumption quartiles. Analysis of covariance (ANCOVA) was used to compare participants' dietary intake across quartiles of fish intake, which was adjusted for age, and total energy intake. We calculated odds ratios (ORs) for breast cancer using binary logistic regression in adjusted models. We controlled for age (continuous) and energy intake (continuous) in the first model. Additional adjustments were made in the second model for education (educated/non-educated), marital status (single/married), social-economic status (poor/middle/high class), residential area (urban/rural), dietary supplement use (yes/no), family history of breast cancer (yes/no), disease history (yes/no), physical activity (MET-h/week), smoking (non-smoker/smoker), alcohol drinking (yes/no), breastfeeding history (yes/no), and menopausal status (premenopausal/postmenopausal). Further adjustments were made in the third model for dietary intake such as refined grains (continuous), whole grains (continuous), fruit (continuous), vegetables (continuous), dairy (continuous), nuts (continuous), and legumes (continuous). In the final model, BMI was controlled to rule out the confounding effect of obesity on the association between fish intake and breast cancer. These covariates were selected based on previous studies [ 28 – 30 ], as well as significant differences in fish intake between quartiles. Participants in the first quartile of fish consumption were considered as the reference category in these analyses. We treated these categories as ordinal variables to determine the overall trend in ORs for breast cancer across fish consumption quartiles. We conducted all statistical analyses via SPSS software (version 26; SPSS Inc., Chicago, IL). A two-sided P-value < 0.05 was regarded as statistically significant for all tests. RESULTS Table 1 details the baseline characteristics of the study population (across case and controls as well as across quartiles of fish consumption). Compared to controls, breast cancer patients were older, more likely to be postmenopausal, current smokers, and less likely to be educated and married. In addition, they had a lower BMI and a breast cancer family history. Women in the highest quartile of fish consumption were younger, had a higher BMI, more likely to be educated, premenopausal, live in urban areas. In addition, they had higher consumption of alcohol, and higher socioeconomic status than women in the lowest quartile of fish consumption. Table 1 General characteristics of the study participants based on cases and controls as well as across quartiles of fish intake. Groups Quartile of fish intake Controls ( n = 700 ) Cases ( n = 350 ) P * Q1 n = 306 Q2 n = 218 Q3 n = 260 Q4 n = 266 P * Age (years) 61.0 ± 10.3 65.3 ± 11.2 < 0.001 64.7 ± 10.3 61.9 ± 10.9 60.7 ± 10.5 62.0 ± 11.3 < 0.001 BMI (kg/m 2 ) 25.5 ± 5.0 21.8 ± 4.8 < 0.001 23.5 ± 5.3 23.6 ± 4.4 25.1 ± 5.3 24.9 ± 5.6 < 0.001 Physical activity (MET-h/week) 34.8 ± 6.5 35.4 ± 6.7 0.20 34.5 ± 6.8 35.3 ± 6.5 35.2 ± 6.3 35.4 ± 6.7 0.44 Residing in rural area, n (%) 447 (63.9) 224 (64.0) 0.96 238 (77.8) 157 (72.0) 155 (59.6) 121 (45.5) < 0.001 Married, n (%) 618 (88.3) 261 (74.6) < 0.001 242 (79.1) 178 (81.7) 224 (86.2) 235 (88.3) 0.08 Educated, n (%) 202 (28.9) 61 (17.4) < 0.001 29 (9.5) 45 (20.6) 79 (30.4) 110 (41.4) < 0.001 Poor Social Economic Status, n (%) 203 (29.0) 117 (33.4) 0.31 133 (43.5) 75 (34.4) 68 (26.2) 44 (16.5) < 0.001 Disease history (yes), n (%) a 61 (8.7) 36 (10.3) 0.41 32 (10.5) 21 (9.6) 24 (9.2) 20 (7.5) 0.68 Current smokers, n (%) b 91 (13.0) 61 (17.4) 0.05 43 (14.1) 35 (16.1) 39 (15.0) 35(13.2) 0.82 Family history of breast cancer, n (%) 24 (3.4) 33 (9.4) < 0.001 20 (6.5) 11 (5.0) 17 (6.5) 9 (3.4) 0.31 History of breast feeding, n (%) 236 (33.7) 119 (34.0) 0.93 110 (35.9) 73 (33.5) 87 (33.5) 85 (32.0) 0.79 Alcohol drinking, n (%) c 52 (7.4) 16 (4.6) 0.08 6 (2.0) 7 (3.2) 22 (8.5) 33 (12.4) < 0.001 Supplement use, n (%) d 71 (10.1) 33 (9.4) 0.71 40 (13.1) 19 (8.7) 27 (10.4) 18 (6.8) 0.08 Post menopause, n (%) 542 (77.4) 309 (88.3) < 0.001 267 (87.3) 171 (78.4) 202 (77.7) 211 (79.3) 0.01 - Data are reported as mean ± SD or percentage. a Subjects with a history of diabetes, heart disease, stroke, or cancer b Individuals who smoke at least one cigarette per day c People who regularly drink alcohol (2–3 times a week) d Supplementation with multivitamins and minerals * Obtained from ANOVA for continuous variables and chi-square test for categorical variables. Abbreviations: BMI: body mass index, MET: Metabolic equivalent of task Women with breast cancer had higher intakes of total energy, total fat, saturated fatty acids (SFAs), and fruits than women without breast cancer (Table 2 ). They also consumed lower amounts of carbohydrates, protein, polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs), thiamine, riboflavin, niacin, folate, whole grains, vegetables, red and process meats, poultry, legumes, and nuts. Table 2 Dietary and nutrient intakes of study participants based on cases and controls as well as across quartiles of fish intake. Groups Quartiles of fish intake Controls ( n = 700 ) Cases ( n = 350 ) P * Q1 n = 306 Q2 n = 218 Q3 n = 260 Q4 n = 266 P * Total energy intake (kcal/d) 2164 ± 25 2525 ± 36 < 0.001 2247 ± 39 2132 ± 46 2285 ± 42 2454 ± 42 < 0.001 Nutrient intakes Carbohydrate (g/d) 320.6 ± 1.9 310.6 ± 2.8 0.005 323.0 ± 2.9 323.6 ± 3.4 322.0 ± 3.1 301.0 ± 3.1 < 0.001 Protein (g/d) 80.7 ± 0.7 71.3 ± 1.1 < 0.001 71.9 ± 1.1 75.9 ± 1.3 80.5 ± 1.2 82.5 ± 1.2 < 0.001 Total Fat (g/d) 81.6 ± 0.8 90.6 ± 1.1 < 0.001 85.0 ± 1.2 82.4 ± 1.4 81.2 ± 1.3 89.3 ± 1.3 < 0.001 Cholesterol (mg/d) 191.8 ± 3.7 185.0 ± 5.3 0.30 154.4 ± 5.2 180.8 ± 6.2 193.6 ± 5.7 233.0 ± 5.6 < 0.001 SFA (g/d) 27.6 ± 0.9 41.1 ± 1.3 < 0.001 37.3 ± 1.3 30.1 ± 1.6 27.7 ± 1.5 32.0 ± 1.4 < 0.001 PUFA (g/d) 11.0 ± 0.2 8.0 ± 0.3 < 0.001 8.6 ± 0.4 9.8 ± 0.4 10.4 ± 0.4 11.5 ± 0.4 < 0.001 MUFA (g/d) 20.8 ± 0.2 19.4 ± 0.3 < 0.001 19.0 ± 0.3 20.6 ± 0.3 20.7 ± 0.3 21.2 ± 0.3 < 0.001 n-3 Fatty acids (g/d) 0.88 ± 0.02 1.00 ± 0.03 0.002 0.82 ± 0.03 0.91 ± 0.03 0.91 ± 0.03 1.20 ± 0.03 < 0.001 Thiamine (mg/day) 2.0 ± 0.01 1.8 ± 0.02 < 0.001 2.0 ± 0.02 2.0 ± 0.03 2.0 ± 0.02 1.9 ± 0.02 0.002 Riboflavin (mg/day) 2.3 ± 0.01 2.2 ± 0.02 < 0.001 2.2 ± 0.02 2.3 ± 0.03 2.3 ± 0.02 2.3 ± 0.02 < 0.001 Niacin (mg/d) 26.5 ± 0.2 23.3 ± 0.3 < 0.001 24.7 ± 0.3 25.2 ± 0.4 26.5 ± 0.3 25.4 ± 0.3 0.003 Folate (µg/d) 605.5 ± 4.3 551.0 ± 6.1 < 0.001 585.1 ± 6.5 588.2 ± 7.7 605.6 ± 7.1 571.3 ± 7.0 0.008 Food groups Refined grains (g/d) 97.1 ± 2.8 90.0 ± 4.0 0.16 78.4 ± 4.1 97.4 ± 4.9 96.2 ± 4.5 110.0 ± 4.4 < 0.001 Whole grains (g/d) 326.1 ± 5.0 298.4 ± 7.1 0.002 343.5 ± 7.3 321.3 ± 8.7 329.4 ± 7.9 270.4 ± 7.9 < 0.001 Fruits (g/d) 149.2 ± 5.3 203.8 ± 7.6 < 0.001 157.7 ± 7.9 135.4 ± 9.3 163.2 ± 8.5 209.0 ± 8.4 < 0.001 Vegetables (g/d) 145.2 ± 3.3 110.6 ± 4.8 < 0.001 111.0 ± 4.9 118.7 ± 5.8 137.7 ± 5.3 168.1 ± 5.3 < 0.001 Red and processed meats (g/d) 84.6 ± 2.5 66.9 ± 3.6 < 0.001 70.8 ± 3.8 76.7 ± 4.5 87.8 ± 4.1 80.5 ± 4.1 0.02 Poultry (g/d) 67.0 ± 2.5 48.5 ± 3.6 < 0.001 58.9 ± 3.8 60.5 ± 4.5 66.0 ± 4.1 58.4 ± 4.0 0.52 Dairy (g/d) 229.6 ± 5.5 238.4 ± 8.0 0.378 200.5 ± 8.2 222.4 ± 9.7 236.6 ± 8.8 273.7 ± 8.8 < 0.001 Legumes (g/d) 16.1 ± 0.6 12.1 ± 0.8 < 0.001 12.0 ± 0.8 14.7 ± 1.0 15.2 ± 0.9 17.6 ± 0.9 < 0.001 Nuts (g/d) 3.0 ± 0.2 1.0 ± 0.3 < 0.001 1.1 ± 0.3 2.1 ± 0.4 3.2 ± 0.3 3.2 ± 0.3 < 0.001 Fish (g/d) 8.4 ± 1.2 10.9 ± 1.7 0.25 0.0 ± 0.0 2.0 ± 1.9 5.1 ± 1.8 29.8 ± 1.8 < 0.001 Data are presented as mean ± standard error (SE) *All values were adjusted for age and energy intake, except for dietary energy intake, which was only adjusted for age using ANCOVA. Abbreviations: SFA: saturated fatty acid; PUFA: polyunsaturated fatty acid; MUFA; monounsaturated fatty acid Crude and multivariable-adjusted ORs along with the corresponding 95% confidence intervals (CIs) for breast cancers across quartiles of fish intake are outlined in Table 3 . There was a significant inverse association between fish consumption and breast cancer (OR: 0.58; 95% CI: 0.42–0.82, P trend =0.001), even after adjusting for potential confounders (OR 0.57; 95% CI 0.34–0.95, Ptrend = 0.02); indicating that higher fish consumption was associated with a lower odds of breast cancer. Table 3 Multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) for breast cancer across quartiles of fish intake Quartiles of fish intake Q1 Q2 Q3 Q4 P trend * Crude 1.00 0.33 (0.22–0.48) 0.38 (0.27–0.55) 0.58 (0.42–0.82) 0.001 Model 1 1.00 0.36 (0.24–0.54) 0.40 (0.27–0.58) 0.52 (0.36–0.74) < 0.001 Model 2 1.00 0.34 (0.22–0.53) 0.40 (0.27–0.60) 0.56 (0.38–0.84) 0.003 Model 3 1.00 0.37 (0.23–0.58) 0.48 (0.31–0.74) 0.59 (0.37–0.95) 0.02 Model 4 1.00 0.32 (0.19–0.52) 0.49 (0.30–0.78) 0.57 (0.34–0.95) 0.02 Model 1: Adjusted for age and energy intake Model 2: Additionally, adjusted for education, marital status, social economic status, residential area, supplement use, family history of breast cancer, disease history, physical activity, smoking, alcohol drinking, breast feeding history, and menopausal status Model 3: Further adjustment for refined grains, whole grains, fruits, vegetables, dairy, nuts, legumes, and n-3 Fatty acids Model 4: Additional adjustment for BMI *Obtained from binary logistic regression In the stratified analysis by menopausal status (Table 4 ), there was no significant association between fish intake and chance of breast cancer among premenopausal women, neither before (OR: 0.49; 95% CI: 0.20–1.18) nor after considering potential confounders (OR: 0.15; 95% CI: 0.02–1.84). However, greater fish intake was inversely associated with the chance of breast cancer in postmenopausal women (OR: 0.62; 95% CI: 0.43, 0.90). The association was robust after controlling for potential confounders (OR: 0.53; 95% CI 0.30, 0.94). Moreover, there was a significant inverse relationship between fish consumption and the chance of breast cancer in normal-weight women (OR: 0.41; 95% CI: 0.21, 0.80), while no association was found in overweight or obese women (OR: 1.60; 95% CI: 0.49, 5.27) (Table 5 ). Table 4 Multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) for breast cancer across quartiles of fish intake stratified based on menopausal status Quartiles of fish intake Q1 Q2 Q3 Q4 P trend * Premenopausal women Crude 1.00 0.06 (0.01–0.30) 0.26 (0.10–0.69) 0.49 (0.20–1.18) 0.37 Model 1 1.00 0.07 (0.01–0.32) 0.25 (0.09–0.66) 0.47 (0.19–1.2) 0.34 Model 2 1.00 0.07 (0.01–0.37) 0.30 (0.10–0.90) 0.55 (0.17–1.80) 0.55 Model 3 1.00 0.08 (0.01–0.53) 0.34 (0.08–1.33) 0.71 (0.14–3.49) 0.77 Model 4 1.00 0.01 (0.01–0.12) 0.24 (0.03–1.63) 0.15 (0.02–1.84) 0.94 Postmenopausal women Crude 1.00 0.41 (0.27–0.62) 0.43 (0.29–0.63) 0.62 (0.43–0.90) 0.005 Model 1 1.00 0.44 (0.28–0.67) 0.43 (0.29–0.65) 0.49 (0.33–0.73) < 0.001 Model 2 1.00 0.42 (0.27–0.66) 0.43 (0.28–0.67) 0.53 (0.34–0.82) 0.002 Model 3 1.00 0.40 (0.24–0.66) 0.50 (0.31–0.80) 0.55 (0.32–0.93) 0.01 Model 4 1.00 0.34 (0.20–0.58) 0.48 (0.28–0.78) 0.53 (0.30–0.94) 0.01 Model 1: Adjusted for age and energy intake Model 2: Additionally, adjusted for education, marital status, social economic status, residential area, supplement use, family history of breast cancer, disease history, physical activity, smoking, alcohol drinking, and breast-feeding history Model 3: Further adjustment for refined grains, whole grains, fruits, vegetables, dairy, nuts, legumes, and n-3 Fatty acids Model 4: Additional adjustment for BMI *Obtained from binary logistic regression Table 5 Multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) for breast cancer across quartiles of fish intake stratified by BMI status Quartiles of fish intake Q1 Q2 Q3 Q4 P trend * Normal-weight women Crude 1.00 0.30 (0.19–0.48) 0.53 (0.34–0.82) 0.56 (0.36–0.86) 0.02 Model 1 1.00 0.31 (0.19–0.50) 0.51 (0.32–0.81) 0.47 (0.30–0.75) 0.004 Model 2 1.00 0.32 (0.19–0.53) 0.54 (0.33–0.90) 0.50 (0.30–0.85) 0.02 Model 3 1.00 0.35 (0.20–0.61) 0.69 (0.39–1.21) 0.47 (0.25–0.87) 0.04 Model 4 1.00 0.34 (0.18–0.61) 0.66 (0.36–1.18) 0.41 (0.21–0.80) 0.03 Overweight or obese women Crude 1.00 0.30 (0.13–0.69) 0.23 (0.11–0.49) 0.78 (0.43–1.41) 0.27 Model 1 1.00 0.37 (0.15–0.88) 0.24 (0.11–0.53) 0.67 (0.36–1.27) 0.14 Model 2 1.00 0.39 (0.15–1.01) 0.23 (0.10–0.54) 0.72 (0.34–1.53) 0.17 Model 3 1.00 0.46 (0.15–1.46) 0.33 (0.12–0.88) 1.65 (0.52–5.19) 0.50 Model 4 1.00 0.34 (0.11–1.07) 0.31 (0.11–0.84) 1.60 (0.49–5.27) 0.54 Model 1: Adjusted for age and energy intake Model 2: Additionally, adjusted for education, marital status, social economic status, residential area, supplement use, family history of breast cancer, disease history, physical activity, smoking, alcohol drinking, breast feeding history, and menopausal status Model 3: Further adjustment for refined grains, whole grains, fruits, vegetables, dairy, nuts, legumes, and n-3 Fatty acids Model 4: Additional adjustment for BMI *Obtained from binary logistic regression DISCUSSION In this case-control study, we found an inverse association between fish consumption and chance of breast cancer, indicating that a higher fish intake was associated with a lower odds of breast cancer. Despite the absence of a significant association in premenopausal women, we found a protective association between normal weight and postmenopausal women. According to the World Health Organization (WHO) report, over 2.3 million women will be diagnosed with breast cancer by 2050 [ 31 , 32 ]. Breast cancer has been recognized as the most common kind of cancer among Iranian women and is associated with a significant burden of mortality, based on crude mortality statistics. [ 33 , 34 ]. Previous studies demonstrated that diet exerts an effect on the risk of developing breast cancer [ 35 ]. Even though a variety of dietary components have been studied in relation to breast cancer, fish consumption has gotten little attention to this point. The present study revealed that fish consumption was associated with a reduced risk of breast cancer, particularly in postmenopausal women. In keeping with our findings, a Korean case-control study indicated a protective association between fish intake and breast cancer risk [ 16 ]. This was also observed in a prospective study of 35,298 Chinese Singaporean women followed for five years. Also reported is the beneficial relationship between fish consumption and other forms of cancer, including colorectal, liver, and lung cancers [ 36 – 38 ]. In contrast to our findings, Kiyabu et al. [ 39 ] found no association between total fish consumption and breast cancer risk in a 14-year prospective analysis of Japanese women. Another cohort study of British women revealed no relationship between fish consumption and breast cancer incidence [ 40 ]. In addition, a prospective cohort study of Danish postmenopausal women revealed that women with a higher fish consumption had a greater risk of developing breast cancer [ 18 ]. Various factors, including sample size, considering several potential confounders, cancer stage at diagnosis, different cooking methods across various populations, different ranges of fish consumption, and different types of fish, such as salted, oily, and lean fish, may explain these divergent findings across different studies in different regions [ 41 ] as well as different methodologies used to examine fish intake and breast cancer. Furthermore, the role of measurement errors in dietary assessment cannot be ignored [ 27 ]. Multiple mechanisms may explain the beneficial association between fish consumption and breast cancer risk. Previous research has linked the preventive effect of fish against breast cancer to its high omega-3 PUFA concentration [ 16 , 19 ]. Omega-3 PUFA may reduce breast cancer risk by downregulating the inflammatory cascade, increasing fatty acid (FA) breakdown while decreasing FA production, causing apoptosis and inhibiting cell proliferation [ 42 , 43 ]. In addition, n-3 PUFA could reduce estrogen synthesis, hence inhibiting estrogen-induced cell proliferation [ 44 , 45 ]. Specifically, studies employing cell lines and mouse models have demonstrated that n-3 PUFA inhibit the formation of breast tumors [ 46 ]. In addition, the anti-inflammatory properties of EPA (Eicosapentaenoic acid) and DHA (Docosahexaenoic acid) and their ability to alter the fatty acid structure of cell membranes make susceptible individuals’ suitable candidates for breast cancer prevention [ 43 , 47 ]. Fish also include essential micronutrients such as magnesium, phosphate, potassium, and protein, which might have a role in this regard [ 48 – 50 ]. In addition, fish has been recognized as a key source of B-vitamins, including riboflavin, niacin, and folate [ 51 ], which the breast cancer-preventive benefits of these nutrients have been shown in prior studies [ 52 – 54 ]. Fish may be contaminated by environmental contaminants such as heavy metals, pesticides, or organic pollutants, which may have estrogenic effects [ 55 ]. These constituents in fish may diminish or even negate the health benefits of eating fish [ 56 ]. Because these chemicals accumulate in fat tissue, one would expect the increased risk to be greater with higher eating of fatty fish compared to ingestion of lean fish [ 55 ]. In the current study, after stratification by menopausal status, the protective link of fish consumption remained significant for postmenopausal women but was no longer significant for premenopausal women. This discrepant outcome may be explained by the small number of premenopausal women in our study (23% of controls and 12% of breast cancer patients). Additionally, premenopausal and postmenopausal women differ in reproductive and adiposity characteristics that have opposing effects on breast tissue sensitivity [ 57 , 58 ]. Several potential strengths of the current investigation must be highlighted. Accurate assessment of study exposure and adjustment for a wide range of confounding variables, an acceptable sample size, and being the first study among women in the Middle East can be noted. In addition, newly diagnosed cases of breast cancer were included in this investigation. Therefore, changes in dietary intakes were less likely. we also calculated energy adjusted amount of fish intake in this study, which can help reduce participants’ misclassification. The limitations of this study, however, should be considered when interpreting its findings. A major limitation of this study is cancer treatment before dietary assessments. Therefore, the precision of acquired data indicating fish frequency may be impacted by a random error known as diagnostic bias and due to the nature of case-control studies, our study was susceptible to selection and recall biases, which would make it impossible to assume causality. Furthermore, as with other epidemiological studies, the use of FFQ may result in participants’ misclassification. Although we used a validated FFQ for dietary intake assessment, this FFQ was validated in a nonmalignant population, so there is cause for concern for populations with cancer and during cancer treatment. Second, the relationship between breast cancer and nutrition is complicated, particularly because breast cancer is a multifactorial and complex disease. Third, we also did not investigate the types of fish (fatty or lean), their sources (sea/salty water or fresh water), or their preparation methods (boiled, fried, salted, canned, etc.), which could have an impact on the observed association. It is suggested that these characteristics be taken into account in future studies on fish consumption. Fourth, we did not collect information on estrogen or progesterone receptor status, as well as breast cancer stage. Fifth, breast cancer patients may recall their past diet differently in the context of their cancer diagnosis, or they might have altered their diet prior to diagnosis as a result of early signs of the disease. Sixth, the IPAQ questionnaire used in our research assesses physical activities performed by individuals during the previous nine days, which does not account for total lifetime physical activity. The questionnaire assesses current activity levels across various domains of physical activity during a typical week. Consequently, our investigation did not assess patterns of lifetime physical activity. In addition, sedentary behavior data were not considered in our survey, and we focused primarily on assessing various domains of physical activity, such as work-related activities, transportation, domestic chores, and leisure activities. Seventh, it is impossible to rule out the possibility of residual confounding in this study as in all observational studies. Finally, over 80% of the population were postmenopausal women, and it would be interesting to conduct additional research on premenopausal women. The current case-control study found a statistically significant inverse association between fish consumption and likelihood of having breast cancer, especially in postmenopausal women. This finding is significant in light of the region's low fish consumption. These data support the role of food in breast cancer prevention. Declarations Acknowledgements Not applicable. Authors’ contributions CA, and AE contributed in conception, design, statistical analyses, data interpretation and manuscript drafting. SBK, HH and LA contributed in data collection, interpretation and manuscript drafting. FF contributed to the data analysis and approving the final manuscript. AE supervised the study. All authors contributed to the article and approved the submitted version. Funding No financial support was provided in any way for this research. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available, but are available from the corresponding author at reasonable request. Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics approval and consent to participate The study was performed by the ethical standards promoted by the 1964 Declaration of Helsinki and its later amendments, and the Ethical Committee approved its protocol of Isfahan University of Medical Sciences in Isfahan, Iran. Informed consent was taken from all study participants after acquaintance with the study methodology. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran. 2 Hypertension and Cardiovascular risk factors Research Center, Medical and Surgical Sciences Department, Alma Mater Studio rum University of Bologna, Bologna, Italy 3 Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran. 4 Faculty of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran 5 Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran 6 Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran 7 Obesity and Eating Habits Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries . CA: a cancer journal for clinicians 2021, 71 (3):209-249. American Cancer Society: How Common Is Breast Cancer? 2021. Field JP, Zuckerwise LC, DeMare AM, Zhao S, Lipscomb BE, Raees MA, Walia S, Lovvorn HN, Novotny NM: Identifying prenatal ultrasound predictors and the ideal neonatal management of closing gastroschisis: the key is prevention . Journal of perinatology : official journal of the California Perinatal Association 2021, 41 (12):2789-2794. Key TJ, Verkasalo PK, Banks E: Epidemiology of breast cancer . The lancet oncology 2001, 2 (3):133-140. Winters S, Martin C, Murphy D, Shokar NK: Breast cancer epidemiology, prevention, and screening . Progress in molecular biology and translational science 2017, 151 :1-32. Pan XF, Marklund M, Wu JH: Fish consumption for cardiovascular health: benefits from long-chain omega-3 fatty acids versus potential harms due to mercury . Heart (British Cardiac Society) 2019, 105 (18):1384-1385. Zargarzadeh N, Severo JS, Pizarro AB, Persad E, Mousavi SMJCT: The Effects of Folic Acid Supplementation on Pro-inflammatory Mediators: a Systematic Review and Dose–Response Meta‐Analysis of Randomized Controlled Trials . 2021, 43 (12):e346-e363. Zhao L, Sun J, Yang Y, Ma X, Wang Y, Xiang Y: Fish consumption and all-cause mortality: a meta-analysis of cohort studies . European journal of clinical nutrition 2016, 70 (2):155-161. Zhang B, Xiong K, Cai J, Ma A: Fish consumption and coronary heart disease: A meta-analysis . Nutrients 2020, 12 (8):2278. Rylander C, Sandanger TM, Engeset D, Lund E: Consumption of lean fish reduces the risk of type 2 diabetes mellitus: a prospective population based cohort study of Norwegian women . PloS one 2014, 9 (2):e89845. Zhao W, Tang H, Yang X, Luo X, Wang X, Shao C, He J: Fish consumption and stroke risk: a meta-analysis of prospective cohort studies . Journal of Stroke and Cerebrovascular Diseases 2019, 28 (3):604-611. Szymanski KM, Wheeler DC, Mucci LA: Fish consumption and prostate cancer risk: a review and meta-analysis . The American journal of clinical nutrition 2010, 92 (5):1223-1233. Wu S, Feng B, Li K, Zhu X, Liang S, Liu X, Han S, Wang B, Wu K, Miao D: Fish consumption and colorectal cancer risk in humans: a systematic review and meta-analysis . The American journal of medicine 2012, 125 (6):551-559. e555. Salehi M, Moradi-Lakeh M, Salehi MH, Nojomi M, Kolahdooz F: Meat, fish, and esophageal cancer risk: a systematic review and dose-response meta-analysis . Nutrition reviews 2013, 71 (5):257-267. Shannon J, Cook LS, Stanford JL: Dietary intake and risk of postmenopausal breast cancer (United States) . Cancer Causes & Control 2003, 14 (1):19-27. Kim J, Lim S-Y, Shin A, Sung M-K, Ro J, Kang H-S, Lee KS, Kim S-W, Lee E-S: Fatty fish and fish omega-3 fatty acid intakes decrease the breast cancer risk: a case-control study . BMC cancer 2009, 9 (1):1-10. Kim AE, Lundgreen A, Wolff RK, Fejerman L, John EM, Torres-Mejía G, Ingles SA, Boone SD, Connor AE, Hines LM: Red meat, poultry, and fish intake and breast cancer risk among Hispanic and Non-Hispanic white women: The Breast Cancer Health Disparities Study . Cancer Causes & Control 2016, 27 (4):527-543. Stripp C, Overvad K, Christensen J, Thomsen BL, Olsen A, Møller S, Tjønneland A: Fish intake is positively associated with breast cancer incidence rate . The Journal of nutrition 2003, 133 (11):3664-3669. Zheng J-S, Hu X-J, Zhao Y-M, Yang J, Li D: Intake of fish and marine n-3 polyunsaturated fatty acids and risk of breast cancer: meta-analysis of data from 21 independent prospective cohort studies . Bmj 2013, 346 :f3706. Esmaillzadeh A, Azadbakht L: Major dietary patterns in relation to general obesity and central adiposity among Iranian women . The Journal of nutrition 2008, 138 (2):358-363. Keshteli AH, Esmaillzadeh A, Rajaie S, Askari G, Feinle-Bisset C, Adibi P: A dish-based semi-quantitative food frequency questionnaire for assessment of dietary intakes in epidemiologic studies in Iran: design and development . International journal of preventive medicine 2014, 5 (1):29. Ghaffarpour M, Houshiar-Rad A, Kianfar H: The manual for household measures, cooking yields factors and edible portion of foods . Tehran: Nashre Olume Keshavarzy 1999, 7 (213):42-58. Dabbagh-Moghadam A, Mozaffari-Khosravi H, Nasiri M, Miri A, Rahdar M, Sadeghi O: Association of white and red meat consumption with general and abdominal obesity: a cross-sectional study among a population of Iranian military families in 2016 . Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity 2017, 22 (4):717-724. Anjom-Shoae J, Sadeghi O, Keshteli AH, Afshar H, Esmaillzadeh A, Adibi P: The association between dietary intake of magnesium and psychiatric disorders among Iranian adults: a cross-sectional study . British Journal of Nutrition 2018, 120 (6):693-702. Miri A, Nasiri M, Zonoori S, Yarahmad F, Dabbagh-Moghadam A, Askari G, Sadeghi O, Asadi M: The association between obesity and migraine in a population of Iranian adults: a case-control study . Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2018, 12 (5):733-736. Sadeghi O, Keshteli AH, Doostan F, Esmaillzadeh A, Adibi P: Association between dairy consumption, dietary calcium intake and general and abdominal obesity among Iranian adults . Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2018, 12 (5):769-775. Willett W: Nutritional epidemiology , vol. 40: Oxford university press; 2012. Sharif Y, Sadeghi O, Benisi-Kohansal S, Azadbakht L, Esmaillzadeh A: Legume and Nuts Consumption in Relation to Odds of Breast Cancer: A Case-Control Study . Nutrition and cancer 2021, 73 (5):750-759. Rigi S, Mousavi SM, Benisi-Kohansal S, Azadbakht L, Esmaillzadeh A: The association between plant-based dietary patterns and risk of breast cancer: a case–control study . Scientific Reports 2021, 11 (1):1-10. Bernstein L: Epidemiology of endocrine-related risk factors for breast cancer . Journal of mammary gland biology and neoplasia 2002, 7 (1):3-15. Hortobagyi GN, de la Garza Salazar J, Pritchard K, Amadori D, Haidinger R, Hudis CA, Khaled H, Liu M-C, Martin M, Namer M: The global breast cancer burden: variations in epidemiology and survival . Clinical breast cancer 2005, 6 (5):391-401. YektaKooshali M, ESMAEILPOUR BM, Sharami H, Alipour Z: Survival rate and average age of the patients with breast cancer in iran: systematic review and meta-analysis . 2016. Mousavi SM, Gouya MM, Ramazani R, Davanlou M, Hajsadeghi N, Seddighi Z: Cancer incidence and mortality in Iran . Annals of oncology 2009, 20 (3):556-563. Akbari M, Abachizadeh K, Khayamzadeh M, Tabatabaee M, Esnaashari F, Motlagh A: Iran cancer report . Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Qom: Darolfekr 2008. Vostakolaei FA, Broeders MJ, Mousavi SM, Kiemeney LA, Verbeek AL: The effect of demographic and lifestyle changes on the burden of breast cancer in Iranian women: a projection to 2030 . The Breast 2013, 22 (3):277-281. Huang R-X, Duan Y-Y, Hu J-A: Fish intake and risk of liver cancer: a meta-analysis . PloS one 2015, 10 (1):e0096102. Xu M, Fang Y-J, Chen Y-M, Lu M-S, Pan Z-Z, Yan B, Zhong X, Zhang C-X: Higher freshwater fish and sea fish intake is inversely associated with colorectal cancer risk among Chinese population: a case-control study . Scientific reports 2015, 5 (1):1-10. Song J, Su H, Wang BL, Zhou YY, Guo LL: Fish consumption and lung cancer risk: systematic review and meta-analysis . Nutrition and cancer 2014, 66 (4):539-549. Kiyabu GY, Inoue M, Saito E, Abe SK, Sawada N, Ishihara J, Iwasaki M, Yamaji T, Shimazu T, Sasazuki S et al : Fish, n - 3 polyunsaturated fatty acids and n - 6 polyunsaturated fatty acids intake and breast cancer risk: The Japan Public Health Center-based prospective study . International journal of cancer 2015, 137 (12):2915-2926. Dunneram Y, Greenwood DC, Cade JE: Diet and risk of breast, endometrial and ovarian cancer: UK Women's Cohort Study . Br J Nutr 2019, 122 (5):564-574. Terry PD, Terry JB, Rohan TE: Long-chain (n-3) fatty acid intake and risk of cancers of the breast and the prostate: recent epidemiological studies, biological mechanisms, and directions for future research . J Nutr 2004, 134 (12 Suppl):3412s-3420s. Joshi A, Hegde M, Adekar S: Omega-3 Fatty Acids in Cancer: Insight into the Mechanism of Actions in Preclinical Cancer Models . In . , edn.; 2016: 157-171. Liu J, Ma DWL: The role of n-3 polyunsaturated fatty acids in the prevention and treatment of breast cancer . Nutrients 2014, 6 (11):5184-5223. Larsson SC, Kumlin M, Ingelman-Sundberg M, Wolk AJTAjocn: Dietary long-chain n− 3 fatty acids for the prevention of cancer: a review of potential mechanisms . 2004, 79 (6):935-945. Mousavi SM, Jalilpiran Y, Karimi E, Aune D, Larijani B, Mozaffarian D, Willett WC, Esmaillzadeh AJDC: Dietary intake of linoleic acid, its concentrations, and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of prospective cohort studies . 2021, 44 (9):2173-2181. Cave WTJBcr, treatment: Omega-3 polyunsaturated fatty acids in rodent models of breast cancer . 1997, 46 (2):239-246. Fabian CJ, Kimler BF, Hursting SD: Omega-3 fatty acids for breast cancer prevention and survivorship . Breast Cancer Research 2015, 17 (1):62. Hicks CC, Cohen PJ, Graham NA, Nash KL, Allison EH, D’Lima C, Mills DJ, Roscher M, Thilsted SH, Thorne-Lyman AL: Harnessing global fisheries to tackle micronutrient deficiencies . Nature 2019, 574 (7776):95-98. Pauly D: Micronutrient richness of global fish catches . Nature: International Weekly Journal of Science 2019(7776). Mohanty BP, Sankar T, Ganguly S, Mahanty A, Anandan R, Chakraborty K, Paul B, Sarma D, Dayal JS, Mathew S: Micronutrient composition of 35 food fishes from India and their significance in human nutrition . Biological trace element research 2016, 174 (2):448-458. Nölle N, Genschick S, Schwadorf K, Hrenn H, Brandner S, Biesalski HK: Fish as a source of (micro)nutrients to combat hidden hunger in Zambia . Food Security 2020, 12 (6):1385-1406. Yang B, Ren X-L, Fu Y-Q, Gao J-L, Li D: Ratio of n-3/n-6 PUFAs and risk of breast cancer: a meta-analysis of 274135 adult females from 11 independent prospective studies . BMC cancer 2014, 14 (1):1-14. Huang W-Q, Long W-Q, Mo X-F, Zhang N-Q, Luo H, Lin F-Y, Huang J, Zhang C-X: Direct and indirect associations between dietary magnesium intake and breast cancer risk . Scientific reports 2019, 9 (1):5764-5764. Liu X, Montissol S, Uber A, Ganley S, Grossestreuer AV, Berg K, Heydrick S, Donnino MW: The effects of thiamine on breast cancer cells . Molecules 2018, 23 (6):1464. Tashla T, Žuža M, Kenjveš T, Prodanović R, Soleša D, Bursić V, Petrović A, Pelić DL, Bošković J, Puvača N: Fish as an important bio-indicator of environmental pollution with persistent organic pollutants and heavy metals . Journal of Agronomy 2018, 28 . Li N, Wu X, Zhuang W, Xia L, Chen Y, Wu C, Rao Z, Du L, Zhao R, Yi M: Fish consumption and multiple health outcomes: Umbrella review . Trends in Food Science & Technology 2020, 99 :273-283. Cho E, Spiegelman D, Hunter DJ, Chen WY, Stampfer MJ, Colditz GA, Willett WC: Premenopausal fat intake and risk of breast cancer . Journal of the National Cancer Institute 2003, 95 (14):1079-1085. Huang Z, Hankinson SE, Colditz GA, Stampfer MJ, Hunter DJ, Manson JE, Hennekens CH, Rosner B, Speizer FE, Willett WC: Dual effects of weight and weight gain on breast cancer risk . Jama 1997, 278 (17):1407-1411. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-4202460","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286977532,"identity":"9dc8de00-e310-4800-89a2-f936863741ac","order_by":0,"name":"Camellia Akhgarjand","email":"data:image/png;base64,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","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Camellia","middleName":"","lastName":"Akhgarjand","suffix":""},{"id":286977533,"identity":"1e2c77d5-f010-4a9a-9e72-2e6c8bd3c613","order_by":1,"name":"Federica Fogacci","email":"","orcid":"","institution":"Alma Mater Studio rum University of Bologna","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Federica","middleName":"","lastName":"Fogacci","suffix":""},{"id":286977534,"identity":"4c3f403c-f2d0-48ba-81a5-460b42852c4f","order_by":2,"name":"Sanaz Benisi-Kohansal","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sanaz","middleName":"","lastName":"Benisi-Kohansal","suffix":""},{"id":286977535,"identity":"29554405-77eb-4047-b19e-d4c94518fb9e","order_by":3,"name":"Hirad Houjaghani","email":"","orcid":"","institution":"Iran University of Medical Sciences (IUMS)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hirad","middleName":"","lastName":"Houjaghani","suffix":""},{"id":286977536,"identity":"035071c2-3d7b-47b5-8118-e63bb42eb7a6","order_by":4,"name":"Leila Azadbakht","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Leila","middleName":"","lastName":"Azadbakht","suffix":""},{"id":286977537,"identity":"5ff9ee83-175a-4791-a4ef-df5bae6f7d41","order_by":5,"name":"Ahmad Esmaillzadeh","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Esmaillzadeh","suffix":""}],"badges":[],"createdAt":"2024-04-01 19:44:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4202460/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4202460/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71983281,"identity":"d6093f4b-38c6-4a5c-be82-7de5de949667","added_by":"auto","created_at":"2024-12-20 10:17:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2194229,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4202460/v1/0b523933-39a7-4d75-a3d8-499600f41d32.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fish consumption in relation to breast cancer: A case-control study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBreast cancer is the most frequently diagnosed type of cancer and the fifth biggest cause of cancer-related death globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Each woman has a 13% risk of breast cancer in the United States. This translates to one in every eight women developing breast cancer in the life course [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the Global Cancer Statistics 2020, 2.3\u0026nbsp;million women were diagnosed with breast cancer resulting in 685,000 deaths worldwide, which has more than doubled over the last two decades[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Breast cancer in recent years, in underdeveloped countries have soared [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Geographic variation in the incidence rate of breast cancer may be attributed to modifiable lifestyle factors, highlighting the need for preventive approaches.\u003c/p\u003e \u003cp\u003eAside from family history and genetics, several environmental and lifestyle-related factors are the main drivers in the etiology of breast cancer; among them, diet has received the greatest attention [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In particular, fish intake has long been studied in relation to various health outcomes. Fish is the most abundant dietary source of omega-3 long-chain polyunsaturated fatty acids (n-3 LCPUFAs), as well as protein, vitamin D, B vitamins, calcium, selenium, zinc, magnesium, and other nutrients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It has previously been linked to a lower risk of all-cause mortality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], coronary heart disease incidence and mortality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], diabetes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], stroke [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and several cancers [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Its consumption has also been examined in relation to breast cancer; however, findings were inconsistent. Even though some studies reported a significant protective association [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], others failed to reach such an association [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In general, the research results appear conflicting. The significance of the results may be affected by the varying levels of fish intake among nations.\u003c/p\u003e \u003cp\u003eDue to vast geographical differences, the demographics of breast cancer patients in developing countries are distinct from those in industrialized nations. Changes in dietary habits and lifestyle as a result of globalization and economic development, as well as the availability of breast cancer screening programs, may influence the breast cancer risk of the population. The majority of studies on the association between fish consumption and health outcomes have been conducted in countries with a high fish intake. Then, it remains unknown if the favorable contribution of fish intake to human health exists in countries with a low fish intake as well. This is particularly relevant for people residing in the Middle East, where fish consumption is low and dietary intakes are differ from other parts of the world [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Given the increasing incidence of breast cancer among women along with nutrition transition in this region, this study was designed to assess the relationship between fish consumption and odds of breast cancer in Iranian females.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis population-based case-control study included women over the age of 30 in Isfahan, Iran. Enrollment of study participants started in 2013 and ended in 2015. We recruited women with breast cancer using convenience sampling from patients referred to hospitals or private clinics who were undergoing chemotherapy, surgical tumor resection, or radiotherapy. The diagnosis was based on physical examination, mammography exam, and pathological confirmation performed during the maximum of last six months. Women with breast cancer included in the study had been diagnosed with invasive primary breast tumors, and whose histological medical records were available. Age-matched controls (\u0026plusmn;\u0026thinsp;5 years) were randomly selected among healthy women in the healthcare institutions in Isfahan, considering their living place as a measure of socio-economic status (SES). Inclusion/Exclusion criteria for the control group were (i) being women, (ii) having Iranian nationality, (iii) no history of any malignancy, cystic mass, and pathological states, (iv) no prior history of hormone replacement therapy, (v) following no specific diet-programs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDietary intakes assessment\u003c/h2\u003e \u003cp\u003eUsual dietary intakes of the study population during the previous year were obtained using a dish-based Willett-format 106-item semiquantitative food frequency questionnaire (DS-FFQ) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The questionnaire validity and details about its design and food items were previously reported [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Briefly, this questionnaire included five groups of foods and dishes: (1) cooked or canned foods called mixed dishes (29 items); (2) foods that are based on carbohydrates (various kinds of potatoes, biscuits, bread, cakes, ten items); (3) dairies (butter, yogurt, and cream, nine items); (4) fruit and vegetables (22 items); and (5) miscellaneous food products and beverages (sweets, junk foods, nuts, desserts and drinks, 36 items). The consumption frequency of each food item was questioned using nine multiple-choice responses ranging from \u0026ldquo;never or less than once a month\u0026rdquo; to \u0026ldquo;12 or more times per day\u0026rdquo;. The consumption frequency of fish items was assessed using six multiple-choice frequency responses ranging from \"never or less than once a month\" to \"1\u0026ndash;2 times per day\". A trained nutritionist performed face-to-face interviews to complete the DS-FFQ. Study participants were requested to report their habitual intakes of food items during the last year. All reported frequencies were converted to grams per day considering given portion sizes in the questionnaire. This was done by a nutritionist using a previously published booklet on household measures of foods [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The Nutritionist IV Software (based on the USDA nutrient databank and modified for Iranian foods) was utilized to assess the participants' daily nutrient intakes. Fish intake was calculated by summing up total fish and canned tuna. The reliability and validity of the DS-FFQ were investigated in a subset of 200 randomly selected participants [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The DS-FFQ was completed by all participants in the validation study at the study baseline and six months later. Three detailed dietary records were obtained from participants for this validation study, which served as the gold standard. According to the findings of this study, the DS-FFQ may indicate reasonably valid and reliable measures of long-term dietary intakes in the Iranian population; for example, the dietary carbohydrate intake as determined by the DS-FFQ was considerably correlated with the average of three dietary records (r\u0026thinsp;=\u0026thinsp;0.81).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBreast cancer assessment\u003c/h2\u003e \u003cp\u003eBreast cancer diagnosis was made by physical examination and mammographic exams that final conformation was determined by pathological tests. All breast cancer patients were females of Iranian nationality with newly diagnosed breast cancer of stage I-IV.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of covariates\u003c/h2\u003e \u003cp\u003eAn overall pretested socio-economic status questionnaire filled by face-to-face interview was applied to collect information on age, residential area, education, family history of breast cancer in their immediate family, alcohol drinking, smoking status, marital status, menopausal status, prior lactating, supplement use, and past disease history. Body weight was measured with light clothing and without shoes, using a digital scale (Seca, Hamburg, Germany) to the nearest 0.1 Kg. Height was quantified without shoes, in the standing position, and near to the wall by employing a tape meter to the nearest 0.5 cm. Body mass index (BMI) was computed by dividing weight in kilograms by height per square meters. Participants were categorized according to BMI into \u0026ldquo;normal\u0026rdquo; and \u0026ldquo;overweight or obese\u0026rdquo; (18.5\u0026ndash;24.9 and \u0026gt;\u0026thinsp;25 Kg/m\u003csup\u003e2\u003c/sup\u003e, respectively). Short-form translated Persian International Physical Activity Questionnaire (IPAQ) was employed through face-to-face interviews to assess participants\u0026rsquo; physical activity. This translated form has been proved in previous studies to describe the exact information about physical activity among the people of Iran [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The information obtained from IPAQ was expressed as a metabolic equivalent time per week (METs/week).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe sample size calculation was according to the hypothesis that an unhealthy diet might increase breast cancer odds by 1.5 times. Considering a type I error of 5%, a study power of 80%, a typical ratio of 0.25, and a ratio of controls to cases as 2, the required sample size was estimated at almost 350 breast cancer patients and 700 seemingly healthy controls.\u003c/p\u003e \u003cp\u003eParticipants were divided into quartiles based on their fish consumption. To begin, the energy-adjusted dietary intake of fish was calculated using the residual method [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To examine differences in quantitative variables across quartiles of fish consumption and between cases and controls, we respectively used one-way analysis of variance (ANOVA) and independent samples' t-test (preceded by the Levene\u0026rsquo;s test for homogeneity of variances). The chi-square test was used to investigate the distribution of categorical variables across fish consumption quartiles. Analysis of covariance (ANCOVA) was used to compare participants' dietary intake across quartiles of fish intake, which was adjusted for age, and total energy intake. We calculated odds ratios (ORs) for breast cancer using binary logistic regression in adjusted models. We controlled for age (continuous) and energy intake (continuous) in the first model. Additional adjustments were made in the second model for education (educated/non-educated), marital status (single/married), social-economic status (poor/middle/high class), residential area (urban/rural), dietary supplement use (yes/no), family history of breast cancer (yes/no), disease history (yes/no), physical activity (MET-h/week), smoking (non-smoker/smoker), alcohol drinking (yes/no), breastfeeding history (yes/no), and menopausal status (premenopausal/postmenopausal). Further adjustments were made in the third model for dietary intake such as refined grains (continuous), whole grains (continuous), fruit (continuous), vegetables (continuous), dairy (continuous), nuts (continuous), and legumes (continuous). In the final model, BMI was controlled to rule out the confounding effect of obesity on the association between fish intake and breast cancer. These covariates were selected based on previous studies [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], as well as significant differences in fish intake between quartiles. Participants in the first quartile of fish consumption were considered as the reference category in these analyses. We treated these categories as ordinal variables to determine the overall trend in ORs for breast cancer across fish consumption quartiles. We conducted all statistical analyses via SPSS software (version 26; SPSS Inc., Chicago, IL). A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistically significant for all tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e details the baseline characteristics of the study population (across case and controls as well as across quartiles of fish consumption). Compared to controls, breast cancer patients were older, more likely to be postmenopausal, current smokers, and less likely to be educated and married. In addition, they had a lower BMI and a breast cancer family history. Women in the highest quartile of fish consumption were younger, had a higher BMI, more likely to be educated, premenopausal, live in urban areas. In addition, they had higher consumption of alcohol, and higher socioeconomic status than women in the lowest quartile of fish consumption.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e General characteristics of the study participants based on cases and controls as well as across quartiles of fish intake.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e \u003cp\u003eQuartile of fish intake\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls (\u003cem\u003en\u0026thinsp;=\u0026thinsp;700\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (\u003cem\u003en\u0026thinsp;=\u0026thinsp;350\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;306\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;218\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;260\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;266\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e62.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity (MET-h/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResiding in rural area, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e447 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e238 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e157 (72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e155 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e121 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e618 (88.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e261 (74.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e242 (79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e178 (81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e224 (86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e235 (88.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducated, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e110 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor Social Economic Status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease history (yes), n (%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smokers, n (%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35(13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of breast cancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of breast feeding, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e85 (32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol drinking, n (%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupplement use, n (%)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost menopause, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e542 (77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e309 (88.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e267 (87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e171 (78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e202 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e211 (79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e- Data are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or percentage.\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Subjects with a history of diabetes, heart disease, stroke, or cancer\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Individuals who smoke at least one cigarette per day\u003c/p\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e People who regularly drink alcohol (2\u0026ndash;3 times a week)\u003c/p\u003e \u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Supplementation with multivitamins and minerals\u003c/p\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e Obtained from ANOVA for continuous variables and chi-square test for categorical variables.\u003c/p\u003e \u003cp\u003eAbbreviations: BMI: body mass index, MET: Metabolic equivalent of task\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWomen with breast cancer had higher intakes of total energy, total fat, saturated fatty acids (SFAs), and fruits than women without breast cancer (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). They also consumed lower amounts of carbohydrates, protein, polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs), thiamine, riboflavin, niacin, folate, whole grains, vegetables, red and process meats, poultry, legumes, and nuts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDietary and nutrient intakes of study participants based on cases and controls as well as across quartiles of fish intake.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eQuartiles of fish intake\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls (\u003cem\u003en\u0026thinsp;=\u0026thinsp;700\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (\u003cem\u003en\u0026thinsp;=\u0026thinsp;350\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;306\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;218\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;260\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;266\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal energy intake (kcal/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2164\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2525\u0026thinsp;\u0026plusmn;\u0026thinsp;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2247\u0026thinsp;\u0026plusmn;\u0026thinsp;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2132\u0026thinsp;\u0026plusmn;\u0026thinsp;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2285\u0026thinsp;\u0026plusmn;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2454\u0026thinsp;\u0026plusmn;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNutrient intakes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e323.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e323.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e322.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e301.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e82.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Fat (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e89.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e154.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e180.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e193.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e233.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUFA (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUFA (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en-3 Fatty acids (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiamine (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRiboflavin (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiacin (mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolate (\u0026micro;g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e605.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e551.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e585.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e588.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e605.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e571.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFood groups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefined grains (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e110.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole grains (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e343.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e321.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e329.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e270.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruits (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e157.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e135.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e163.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e209.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e118.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e137.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e168.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed and processed meats (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoultry (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDairy (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e200.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e222.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e236.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e273.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLegumes (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuts (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE)\u003c/p\u003e \u003cp\u003e*All values were adjusted for age and energy intake, except for dietary energy intake, which was only adjusted for age using ANCOVA.\u003c/p\u003e \u003cp\u003eAbbreviations: SFA: saturated fatty acid; PUFA: polyunsaturated fatty acid; MUFA; monounsaturated fatty acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCrude and multivariable-adjusted ORs along with the corresponding 95% confidence intervals (CIs) for breast cancers across quartiles of fish intake are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There was a significant inverse association between fish consumption and breast cancer (OR: 0.58; 95% CI: 0.42\u0026ndash;0.82, P\u003csub\u003etrend\u003c/sub\u003e=0.001), even after adjusting for potential confounders (OR 0.57; 95% CI 0.34\u0026ndash;0.95, Ptrend\u0026thinsp;=\u0026thinsp;0.02); indicating that higher fish consumption was associated with a lower odds of breast cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) for breast cancer across quartiles of fish intake\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eQuartiles of fish intake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003etrend\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33 (0.22\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38 (0.27\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58 (0.42\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36 (0.24\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40 (0.27\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52 (0.36\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.22\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40 (0.27\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56 (0.38\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37 (0.23\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48 (0.31\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59 (0.37\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32 (0.19\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49 (0.30\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57 (0.34\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eModel 1: Adjusted for age and energy intake\u003c/p\u003e \u003cp\u003eModel 2: Additionally, adjusted for education, marital status, social economic status, residential area, supplement use, family history of breast cancer, disease history, physical activity, smoking, alcohol drinking, breast feeding history, and menopausal status\u003c/p\u003e \u003cp\u003eModel 3: Further adjustment for refined grains, whole grains, fruits, vegetables, dairy, nuts, legumes, and n-3 Fatty acids\u003c/p\u003e \u003cp\u003eModel 4: Additional adjustment for BMI\u003c/p\u003e \u003cp\u003e*Obtained from binary logistic regression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the stratified analysis by menopausal status (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), there was no significant association between fish intake and chance of breast cancer among premenopausal women, neither before (OR: 0.49; 95% CI: 0.20\u0026ndash;1.18) nor after considering potential confounders (OR: 0.15; 95% CI: 0.02\u0026ndash;1.84). However, greater fish intake was inversely associated with the chance of breast cancer in postmenopausal women (OR: 0.62; 95% CI: 0.43, 0.90). The association was robust after controlling for potential confounders (OR: 0.53; 95% CI 0.30, 0.94). Moreover, there was a significant inverse relationship between fish consumption and the chance of breast cancer in normal-weight women (OR: 0.41; 95% CI: 0.21, 0.80), while no association was found in overweight or obese women (OR: 1.60; 95% CI: 0.49, 5.27) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) for breast cancer across quartiles of fish intake stratified based on menopausal status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eQuartiles of fish intake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003etrend\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePremenopausal women\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.01\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26 (0.10\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49 (0.20\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.01\u0026ndash;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25 (0.09\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47 (0.19\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.01\u0026ndash;0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30 (0.10\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55 (0.17\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08 (0.01\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34 (0.08\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71 (0.14\u0026ndash;3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01 (0.01\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24 (0.03\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15 (0.02\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePostmenopausal women\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41 (0.27\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43 (0.29\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62 (0.43\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.28\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43 (0.29\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49 (0.33\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42 (0.27\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43 (0.28\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53 (0.34\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40 (0.24\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.31\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55 (0.32\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.20\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48 (0.28\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53 (0.30\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eModel 1: Adjusted for age and energy intake\u003c/p\u003e \u003cp\u003eModel 2: Additionally, adjusted for education, marital status, social economic status, residential area, supplement use, family history of breast cancer, disease history, physical activity, smoking, alcohol drinking, and breast-feeding history\u003c/p\u003e \u003cp\u003eModel 3: Further adjustment for refined grains, whole grains, fruits, vegetables, dairy, nuts, legumes, and n-3 Fatty acids\u003c/p\u003e \u003cp\u003eModel 4: Additional adjustment for BMI\u003c/p\u003e \u003cp\u003e*Obtained from binary logistic regression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) for breast cancer across quartiles of fish intake stratified by BMI status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eQuartiles of fish intake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003etrend\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNormal-weight women\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30 (0.19\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53 (0.34\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56 (0.36\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31 (0.19\u0026ndash;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51 (0.32\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47 (0.30\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32 (0.19\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.33\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50 (0.30\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35 (0.20\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69 (0.39\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47 (0.25\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.18\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.36\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41 (0.21\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eOverweight or obese women\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30 (0.13\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.11\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78 (0.43\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37 (0.15\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24 (0.11\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67 (0.36\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39 (0.15\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.10\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72 (0.34\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46 (0.15\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33 (0.12\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65 (0.52\u0026ndash;5.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.11\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31 (0.11\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60 (0.49\u0026ndash;5.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eModel 1: Adjusted for age and energy intake\u003c/p\u003e \u003cp\u003eModel 2: Additionally, adjusted for education, marital status, social economic status, residential area, supplement use, family history of breast cancer, disease history, physical activity, smoking, alcohol drinking, breast feeding history, and menopausal status\u003c/p\u003e \u003cp\u003eModel 3: Further adjustment for refined grains, whole grains, fruits, vegetables, dairy, nuts, legumes, and n-3 Fatty acids\u003c/p\u003e \u003cp\u003eModel 4: Additional adjustment for BMI\u003c/p\u003e \u003cp\u003e*Obtained from binary logistic regression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this case-control study, we found an inverse association between fish consumption and chance of breast cancer, indicating that a higher fish intake was associated with a lower odds of breast cancer. Despite the absence of a significant association in premenopausal women, we found a protective association between normal weight and postmenopausal women.\u003c/p\u003e \u003cp\u003eAccording to the World Health Organization (WHO) report, over 2.3\u0026nbsp;million women will be diagnosed with breast cancer by 2050 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Breast cancer has been recognized as the most common kind of cancer among Iranian women and is associated with a significant burden of mortality, based on crude mortality statistics. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Previous studies demonstrated that diet exerts an effect on the risk of developing breast cancer [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Even though a variety of dietary components have been studied in relation to breast cancer, fish consumption has gotten little attention to this point. The present study revealed that fish consumption was associated with a reduced risk of breast cancer, particularly in postmenopausal women. In keeping with our findings, a Korean case-control study indicated a protective association between fish intake and breast cancer risk [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This was also observed in a prospective study of 35,298 Chinese Singaporean women followed for five years. Also reported is the beneficial relationship between fish consumption and other forms of cancer, including colorectal, liver, and lung cancers [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In contrast to our findings, Kiyabu et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] found no association between total fish consumption and breast cancer risk in a 14-year prospective analysis of Japanese women. Another cohort study of British women revealed no relationship between fish consumption and breast cancer incidence [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In addition, a prospective cohort study of Danish postmenopausal women revealed that women with a higher fish consumption had a greater risk of developing breast cancer [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Various factors, including sample size, considering several potential confounders, cancer stage at diagnosis, different cooking methods across various populations, different ranges of fish consumption, and different types of fish, such as salted, oily, and lean fish, may explain these divergent findings across different studies in different regions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] as well as different methodologies used to examine fish intake and breast cancer. Furthermore, the role of measurement errors in dietary assessment cannot be ignored [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple mechanisms may explain the beneficial association between fish consumption and breast cancer risk. Previous research has linked the preventive effect of fish against breast cancer to its high omega-3 PUFA concentration [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Omega-3 PUFA may reduce breast cancer risk by downregulating the inflammatory cascade, increasing fatty acid (FA) breakdown while decreasing FA production, causing apoptosis and inhibiting cell proliferation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In addition, n-3 PUFA could reduce estrogen synthesis, hence inhibiting estrogen-induced cell proliferation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Specifically, studies employing cell lines and mouse models have demonstrated that n-3 PUFA inhibit the formation of breast tumors [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition, the anti-inflammatory properties of EPA (Eicosapentaenoic acid) and DHA (Docosahexaenoic acid) and their ability to alter the fatty acid structure of cell membranes make susceptible individuals\u0026rsquo; suitable candidates for breast cancer prevention [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Fish also include essential micronutrients such as magnesium, phosphate, potassium, and protein, which might have a role in this regard [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In addition, fish has been recognized as a key source of B-vitamins, including riboflavin, niacin, and folate [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], which the breast cancer-preventive benefits of these nutrients have been shown in prior studies [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Fish may be contaminated by environmental contaminants such as heavy metals, pesticides, or organic pollutants, which may have estrogenic effects [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. These constituents in fish may diminish or even negate the health benefits of eating fish [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Because these chemicals accumulate in fat tissue, one would expect the increased risk to be greater with higher eating of fatty fish compared to ingestion of lean fish [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In the current study, after stratification by menopausal status, the protective link of fish consumption remained significant for postmenopausal women but was no longer significant for premenopausal women. This discrepant outcome may be explained by the small number of premenopausal women in our study (23% of controls and 12% of breast cancer patients). Additionally, premenopausal and postmenopausal women differ in reproductive and adiposity characteristics that have opposing effects on breast tissue sensitivity [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral potential strengths of the current investigation must be highlighted. Accurate assessment of study exposure and adjustment for a wide range of confounding variables, an acceptable sample size, and being the first study among women in the Middle East can be noted. In addition, newly diagnosed cases of breast cancer were included in this investigation. Therefore, changes in dietary intakes were less likely. we also calculated energy adjusted amount of fish intake in this study, which can help reduce participants\u0026rsquo; misclassification. The limitations of this study, however, should be considered when interpreting its findings. A major limitation of this study is cancer treatment before dietary assessments. Therefore, the precision of acquired data indicating fish frequency may be impacted by a random error known as diagnostic bias and due to the nature of case-control studies, our study was susceptible to selection and recall biases, which would make it impossible to assume causality. Furthermore, as with other epidemiological studies, the use of FFQ may result in participants\u0026rsquo; misclassification. Although we used a validated FFQ for dietary intake assessment, this FFQ was validated in a nonmalignant population, so there is cause for concern for populations with cancer and during cancer treatment. Second, the relationship between breast cancer and nutrition is complicated, particularly because breast cancer is a multifactorial and complex disease. Third, we also did not investigate the types of fish (fatty or lean), their sources (sea/salty water or fresh water), or their preparation methods (boiled, fried, salted, canned, etc.), which could have an impact on the observed association. It is suggested that these characteristics be taken into account in future studies on fish consumption. Fourth, we did not collect information on estrogen or progesterone receptor status, as well as breast cancer stage. Fifth, breast cancer patients may recall their past diet differently in the context of their cancer diagnosis, or they might have altered their diet prior to diagnosis as a result of early signs of the disease. Sixth, the IPAQ questionnaire used in our research assesses physical activities performed by individuals during the previous nine days, which does not account for total lifetime physical activity. The questionnaire assesses current activity levels across various domains of physical activity during a typical week. Consequently, our investigation did not assess patterns of lifetime physical activity. In addition, sedentary behavior data were not considered in our survey, and we focused primarily on assessing various domains of physical activity, such as work-related activities, transportation, domestic chores, and leisure activities. Seventh, it is impossible to rule out the possibility of residual confounding in this study as in all observational studies. Finally, over 80% of the population were postmenopausal women, and it would be interesting to conduct additional research on premenopausal women.\u003c/p\u003e \u003cp\u003eThe current case-control study found a statistically significant inverse association between fish consumption and likelihood of having breast cancer, especially in postmenopausal women. This finding is significant in light of the region's low fish consumption. These data support the role of food in breast cancer prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCA, and AE contributed in conception, design, statistical analyses, data interpretation and manuscript drafting. SBK, HH and LA contributed in data collection, interpretation and manuscript drafting. FF contributed to the data analysis and approving the final manuscript. AE supervised the study. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo financial support was provided in any way for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available, but are available from the corresponding author at reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was performed by the ethical standards promoted by the 1964 Declaration of Helsinki and its later amendments, and the Ethical Committee approved its protocol of Isfahan University of Medical Sciences in Isfahan, Iran. Informed consent was taken from all study participants after acquaintance with the study methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eHypertension and Cardiovascular risk factors Research Center, Medical and Surgical Sciences Department, Alma Mater Studio rum University of Bologna, Bologna, Italy\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eFaculty of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eDiabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e6\u003c/sup\u003eDepartment of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e7\u003c/sup\u003eObesity and Eating Habits Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries\u003c/strong\u003e. \u003cem\u003eCA: a cancer journal for clinicians \u003c/em\u003e2021, \u003cstrong\u003e71\u003c/strong\u003e(3):209-249.\u003c/li\u003e\n\u003cli\u003eAmerican Cancer Society: \u003cstrong\u003eHow Common Is Breast Cancer?\u003c/strong\u003e 2021.\u003c/li\u003e\n\u003cli\u003eField JP, Zuckerwise LC, DeMare AM, Zhao S, Lipscomb BE, Raees MA, Walia S, Lovvorn HN, Novotny NM: \u003cstrong\u003eIdentifying prenatal ultrasound predictors and the ideal neonatal management of closing gastroschisis: the key is prevention\u003c/strong\u003e. \u003cem\u003eJournal of perinatology : official journal of the California Perinatal Association \u003c/em\u003e2021, \u003cstrong\u003e41\u003c/strong\u003e(12):2789-2794.\u003c/li\u003e\n\u003cli\u003eKey TJ, Verkasalo PK, Banks E: \u003cstrong\u003eEpidemiology of breast cancer\u003c/strong\u003e. \u003cem\u003eThe lancet oncology \u003c/em\u003e2001, \u003cstrong\u003e2\u003c/strong\u003e(3):133-140.\u003c/li\u003e\n\u003cli\u003eWinters S, Martin C, Murphy D, Shokar NK: \u003cstrong\u003eBreast cancer epidemiology, prevention, and screening\u003c/strong\u003e. \u003cem\u003eProgress in molecular biology and translational science \u003c/em\u003e2017, \u003cstrong\u003e151\u003c/strong\u003e:1-32.\u003c/li\u003e\n\u003cli\u003ePan XF, Marklund M, Wu JH: \u003cstrong\u003eFish consumption for cardiovascular health: benefits from long-chain omega-3 fatty acids versus potential harms due to mercury\u003c/strong\u003e. \u003cem\u003eHeart (British Cardiac Society) \u003c/em\u003e2019, \u003cstrong\u003e105\u003c/strong\u003e(18):1384-1385.\u003c/li\u003e\n\u003cli\u003eZargarzadeh N, Severo JS, Pizarro AB, Persad E, Mousavi SMJCT: \u003cstrong\u003eThe Effects of Folic Acid Supplementation on Pro-inflammatory Mediators: a Systematic Review and Dose\u0026ndash;Response Meta‐Analysis of Randomized Controlled Trials\u003c/strong\u003e. 2021, \u003cstrong\u003e43\u003c/strong\u003e(12):e346-e363.\u003c/li\u003e\n\u003cli\u003eZhao L, Sun J, Yang Y, Ma X, Wang Y, Xiang Y: \u003cstrong\u003eFish consumption and all-cause mortality: a meta-analysis of cohort studies\u003c/strong\u003e. \u003cem\u003eEuropean journal of clinical nutrition \u003c/em\u003e2016, \u003cstrong\u003e70\u003c/strong\u003e(2):155-161.\u003c/li\u003e\n\u003cli\u003eZhang B, Xiong K, Cai J, Ma A: \u003cstrong\u003eFish consumption and coronary heart disease: A meta-analysis\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2020, \u003cstrong\u003e12\u003c/strong\u003e(8):2278.\u003c/li\u003e\n\u003cli\u003eRylander C, Sandanger TM, Engeset D, Lund E: \u003cstrong\u003eConsumption of lean fish reduces the risk of type 2 diabetes mellitus: a prospective population based cohort study of Norwegian women\u003c/strong\u003e. \u003cem\u003ePloS one \u003c/em\u003e2014, \u003cstrong\u003e9\u003c/strong\u003e(2):e89845.\u003c/li\u003e\n\u003cli\u003eZhao W, Tang H, Yang X, Luo X, Wang X, Shao C, He J: \u003cstrong\u003eFish consumption and stroke risk: a meta-analysis of prospective cohort studies\u003c/strong\u003e. \u003cem\u003eJournal of Stroke and Cerebrovascular Diseases \u003c/em\u003e2019, \u003cstrong\u003e28\u003c/strong\u003e(3):604-611.\u003c/li\u003e\n\u003cli\u003eSzymanski KM, Wheeler DC, Mucci LA: \u003cstrong\u003eFish consumption and prostate cancer risk: a review and meta-analysis\u003c/strong\u003e. \u003cem\u003eThe American journal of clinical nutrition \u003c/em\u003e2010, \u003cstrong\u003e92\u003c/strong\u003e(5):1223-1233.\u003c/li\u003e\n\u003cli\u003eWu S, Feng B, Li K, Zhu X, Liang S, Liu X, Han S, Wang B, Wu K, Miao D: \u003cstrong\u003eFish consumption and colorectal cancer risk in humans: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eThe American journal of medicine \u003c/em\u003e2012, \u003cstrong\u003e125\u003c/strong\u003e(6):551-559. e555.\u003c/li\u003e\n\u003cli\u003eSalehi M, Moradi-Lakeh M, Salehi MH, Nojomi M, Kolahdooz F: \u003cstrong\u003eMeat, fish, and esophageal cancer risk: a systematic review and dose-response meta-analysis\u003c/strong\u003e. \u003cem\u003eNutrition reviews \u003c/em\u003e2013, \u003cstrong\u003e71\u003c/strong\u003e(5):257-267.\u003c/li\u003e\n\u003cli\u003eShannon J, Cook LS, Stanford JL: \u003cstrong\u003eDietary intake and risk of postmenopausal breast cancer (United States)\u003c/strong\u003e. \u003cem\u003eCancer Causes \u0026amp; Control \u003c/em\u003e2003, \u003cstrong\u003e14\u003c/strong\u003e(1):19-27.\u003c/li\u003e\n\u003cli\u003eKim J, Lim S-Y, Shin A, Sung M-K, Ro J, Kang H-S, Lee KS, Kim S-W, Lee E-S: \u003cstrong\u003eFatty fish and fish omega-3 fatty acid intakes decrease the breast cancer risk: a case-control study\u003c/strong\u003e. \u003cem\u003eBMC cancer \u003c/em\u003e2009, \u003cstrong\u003e9\u003c/strong\u003e(1):1-10.\u003c/li\u003e\n\u003cli\u003eKim AE, Lundgreen A, Wolff RK, Fejerman L, John EM, Torres-Mej\u0026iacute;a G, Ingles SA, Boone SD, Connor AE, Hines LM: \u003cstrong\u003eRed meat, poultry, and fish intake and breast cancer risk among Hispanic and Non-Hispanic white women: The Breast Cancer Health Disparities Study\u003c/strong\u003e. \u003cem\u003eCancer Causes \u0026amp; Control \u003c/em\u003e2016, \u003cstrong\u003e27\u003c/strong\u003e(4):527-543.\u003c/li\u003e\n\u003cli\u003eStripp C, Overvad K, Christensen J, Thomsen BL, Olsen A, M\u0026oslash;ller S, Tj\u0026oslash;nneland A: \u003cstrong\u003eFish intake is positively associated with breast cancer incidence rate\u003c/strong\u003e. \u003cem\u003eThe Journal of nutrition \u003c/em\u003e2003, \u003cstrong\u003e133\u003c/strong\u003e(11):3664-3669.\u003c/li\u003e\n\u003cli\u003eZheng J-S, Hu X-J, Zhao Y-M, Yang J, Li D: \u003cstrong\u003eIntake of fish and marine n-3 polyunsaturated fatty acids and risk of breast cancer: meta-analysis of data from 21 independent prospective cohort studies\u003c/strong\u003e. \u003cem\u003eBmj \u003c/em\u003e2013, \u003cstrong\u003e346\u003c/strong\u003e:f3706.\u003c/li\u003e\n\u003cli\u003eEsmaillzadeh A, Azadbakht L: \u003cstrong\u003eMajor dietary patterns in relation to general obesity and central adiposity among Iranian women\u003c/strong\u003e. \u003cem\u003eThe Journal of nutrition \u003c/em\u003e2008, \u003cstrong\u003e138\u003c/strong\u003e(2):358-363.\u003c/li\u003e\n\u003cli\u003eKeshteli AH, Esmaillzadeh A, Rajaie S, Askari G, Feinle-Bisset C, Adibi P: \u003cstrong\u003eA dish-based semi-quantitative food frequency questionnaire for assessment of dietary intakes in epidemiologic studies in Iran: design and development\u003c/strong\u003e. \u003cem\u003eInternational journal of preventive medicine \u003c/em\u003e2014, \u003cstrong\u003e5\u003c/strong\u003e(1):29.\u003c/li\u003e\n\u003cli\u003eGhaffarpour M, Houshiar-Rad A, Kianfar H: \u003cstrong\u003eThe manual for household measures, cooking yields factors and edible portion of foods\u003c/strong\u003e. \u003cem\u003eTehran: Nashre Olume Keshavarzy \u003c/em\u003e1999, \u003cstrong\u003e7\u003c/strong\u003e(213):42-58.\u003c/li\u003e\n\u003cli\u003eDabbagh-Moghadam A, Mozaffari-Khosravi H, Nasiri M, Miri A, Rahdar M, Sadeghi O: \u003cstrong\u003eAssociation of white and red meat consumption with general and abdominal obesity: a cross-sectional study among a population of Iranian military families in 2016\u003c/strong\u003e. \u003cem\u003eEating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity \u003c/em\u003e2017, \u003cstrong\u003e22\u003c/strong\u003e(4):717-724.\u003c/li\u003e\n\u003cli\u003eAnjom-Shoae J, Sadeghi O, Keshteli AH, Afshar H, Esmaillzadeh A, Adibi P: \u003cstrong\u003eThe association between dietary intake of magnesium and psychiatric disorders among Iranian adults: a cross-sectional study\u003c/strong\u003e. \u003cem\u003eBritish Journal of Nutrition \u003c/em\u003e2018, \u003cstrong\u003e120\u003c/strong\u003e(6):693-702.\u003c/li\u003e\n\u003cli\u003eMiri A, Nasiri M, Zonoori S, Yarahmad F, Dabbagh-Moghadam A, Askari G, Sadeghi O, Asadi M: \u003cstrong\u003eThe association between obesity and migraine in a population of Iranian adults: a case-control study\u003c/strong\u003e. \u003cem\u003eDiabetes \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews \u003c/em\u003e2018, \u003cstrong\u003e12\u003c/strong\u003e(5):733-736.\u003c/li\u003e\n\u003cli\u003eSadeghi O, Keshteli AH, Doostan F, Esmaillzadeh A, Adibi P: \u003cstrong\u003eAssociation between dairy consumption, dietary calcium intake and general and abdominal obesity among Iranian adults\u003c/strong\u003e. \u003cem\u003eDiabetes \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews \u003c/em\u003e2018, \u003cstrong\u003e12\u003c/strong\u003e(5):769-775.\u003c/li\u003e\n\u003cli\u003eWillett W: \u003cstrong\u003eNutritional epidemiology\u003c/strong\u003e, vol. 40: Oxford university press; 2012.\u003c/li\u003e\n\u003cli\u003eSharif Y, Sadeghi O, Benisi-Kohansal S, Azadbakht L, Esmaillzadeh A: \u003cstrong\u003eLegume and Nuts Consumption in Relation to Odds of Breast Cancer: A Case-Control Study\u003c/strong\u003e. \u003cem\u003eNutrition and cancer \u003c/em\u003e2021, \u003cstrong\u003e73\u003c/strong\u003e(5):750-759.\u003c/li\u003e\n\u003cli\u003eRigi S, Mousavi SM, Benisi-Kohansal S, Azadbakht L, Esmaillzadeh A: \u003cstrong\u003eThe association between plant-based dietary patterns and risk of breast cancer: a case\u0026ndash;control study\u003c/strong\u003e. \u003cem\u003eScientific Reports \u003c/em\u003e2021, \u003cstrong\u003e11\u003c/strong\u003e(1):1-10.\u003c/li\u003e\n\u003cli\u003eBernstein L: \u003cstrong\u003eEpidemiology of endocrine-related risk factors for breast cancer\u003c/strong\u003e. \u003cem\u003eJournal of mammary gland biology and neoplasia \u003c/em\u003e2002, \u003cstrong\u003e7\u003c/strong\u003e(1):3-15.\u003c/li\u003e\n\u003cli\u003eHortobagyi GN, de la Garza Salazar J, Pritchard K, Amadori D, Haidinger R, Hudis CA, Khaled H, Liu M-C, Martin M, Namer M: \u003cstrong\u003eThe global breast cancer burden: variations in epidemiology and survival\u003c/strong\u003e. \u003cem\u003eClinical breast cancer \u003c/em\u003e2005, \u003cstrong\u003e6\u003c/strong\u003e(5):391-401.\u003c/li\u003e\n\u003cli\u003eYektaKooshali M, ESMAEILPOUR BM, Sharami H, Alipour Z: \u003cstrong\u003eSurvival rate and average age of the patients with breast cancer in iran: systematic review and meta-analysis\u003c/strong\u003e. 2016.\u003c/li\u003e\n\u003cli\u003eMousavi SM, Gouya MM, Ramazani R, Davanlou M, Hajsadeghi N, Seddighi Z: \u003cstrong\u003eCancer incidence and mortality in Iran\u003c/strong\u003e. \u003cem\u003eAnnals of oncology \u003c/em\u003e2009, \u003cstrong\u003e20\u003c/strong\u003e(3):556-563.\u003c/li\u003e\n\u003cli\u003eAkbari M, Abachizadeh K, Khayamzadeh M, Tabatabaee M, Esnaashari F, Motlagh A: \u003cstrong\u003eIran cancer report\u003c/strong\u003e. \u003cem\u003eCancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Qom: Darolfekr \u003c/em\u003e2008.\u003c/li\u003e\n\u003cli\u003eVostakolaei FA, Broeders MJ, Mousavi SM, Kiemeney LA, Verbeek AL: \u003cstrong\u003eThe effect of demographic and lifestyle changes on the burden of breast cancer in Iranian women: a projection to 2030\u003c/strong\u003e. \u003cem\u003eThe Breast \u003c/em\u003e2013, \u003cstrong\u003e22\u003c/strong\u003e(3):277-281.\u003c/li\u003e\n\u003cli\u003eHuang R-X, Duan Y-Y, Hu J-A: \u003cstrong\u003eFish intake and risk of liver cancer: a meta-analysis\u003c/strong\u003e. \u003cem\u003ePloS one \u003c/em\u003e2015, \u003cstrong\u003e10\u003c/strong\u003e(1):e0096102.\u003c/li\u003e\n\u003cli\u003eXu M, Fang Y-J, Chen Y-M, Lu M-S, Pan Z-Z, Yan B, Zhong X, Zhang C-X: \u003cstrong\u003eHigher freshwater fish and sea fish intake is inversely associated with colorectal cancer risk among Chinese population: a case-control study\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2015, \u003cstrong\u003e5\u003c/strong\u003e(1):1-10.\u003c/li\u003e\n\u003cli\u003eSong J, Su H, Wang BL, Zhou YY, Guo LL: \u003cstrong\u003eFish consumption and lung cancer risk: systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eNutrition and cancer \u003c/em\u003e2014, \u003cstrong\u003e66\u003c/strong\u003e(4):539-549.\u003c/li\u003e\n\u003cli\u003eKiyabu GY, Inoue M, Saito E, Abe SK, Sawada N, Ishihara J, Iwasaki M, Yamaji T, Shimazu T, Sasazuki S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFish, n\u0026thinsp;-\u0026thinsp;3 polyunsaturated fatty acids and n\u0026thinsp;-\u0026thinsp;6 polyunsaturated fatty acids intake and breast cancer risk: The Japan Public Health Center-based prospective study\u003c/strong\u003e. \u003cem\u003eInternational journal of cancer \u003c/em\u003e2015, \u003cstrong\u003e137\u003c/strong\u003e(12):2915-2926.\u003c/li\u003e\n\u003cli\u003eDunneram Y, Greenwood DC, Cade JE: \u003cstrong\u003eDiet and risk of breast, endometrial and ovarian cancer: UK Women\u0026apos;s Cohort Study\u003c/strong\u003e. \u003cem\u003eBr J Nutr \u003c/em\u003e2019, \u003cstrong\u003e122\u003c/strong\u003e(5):564-574.\u003c/li\u003e\n\u003cli\u003eTerry PD, Terry JB, Rohan TE: \u003cstrong\u003eLong-chain (n-3) fatty acid intake and risk of cancers of the breast and the prostate: recent epidemiological studies, biological mechanisms, and directions for future research\u003c/strong\u003e. \u003cem\u003eJ Nutr \u003c/em\u003e2004, \u003cstrong\u003e134\u003c/strong\u003e(12 Suppl):3412s-3420s.\u003c/li\u003e\n\u003cli\u003eJoshi A, Hegde M, Adekar S: \u003cstrong\u003eOmega-3 Fatty Acids in Cancer: Insight into the Mechanism of Actions in Preclinical Cancer Models\u003c/strong\u003e. In\u003cem\u003e.\u003c/em\u003e, edn.; 2016: 157-171.\u003c/li\u003e\n\u003cli\u003eLiu J, Ma DWL: \u003cstrong\u003eThe role of n-3 polyunsaturated fatty acids in the prevention and treatment of breast cancer\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2014, \u003cstrong\u003e6\u003c/strong\u003e(11):5184-5223.\u003c/li\u003e\n\u003cli\u003eLarsson SC, Kumlin M, Ingelman-Sundberg M, Wolk AJTAjocn: \u003cstrong\u003eDietary long-chain n\u0026minus; 3 fatty acids for the prevention of cancer: a review of potential mechanisms\u003c/strong\u003e. 2004, \u003cstrong\u003e79\u003c/strong\u003e(6):935-945.\u003c/li\u003e\n\u003cli\u003eMousavi SM, Jalilpiran Y, Karimi E, Aune D, Larijani B, Mozaffarian D, Willett WC, Esmaillzadeh AJDC: \u003cstrong\u003eDietary intake of linoleic acid, its concentrations, and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of prospective cohort studies\u003c/strong\u003e. 2021, \u003cstrong\u003e44\u003c/strong\u003e(9):2173-2181.\u003c/li\u003e\n\u003cli\u003eCave WTJBcr, treatment: \u003cstrong\u003eOmega-3 polyunsaturated fatty acids in rodent models of breast cancer\u003c/strong\u003e. 1997, \u003cstrong\u003e46\u003c/strong\u003e(2):239-246.\u003c/li\u003e\n\u003cli\u003eFabian CJ, Kimler BF, Hursting SD: \u003cstrong\u003eOmega-3 fatty acids for breast cancer prevention and survivorship\u003c/strong\u003e. \u003cem\u003eBreast Cancer Research \u003c/em\u003e2015, \u003cstrong\u003e17\u003c/strong\u003e(1):62.\u003c/li\u003e\n\u003cli\u003eHicks CC, Cohen PJ, Graham NA, Nash KL, Allison EH, D\u0026rsquo;Lima C, Mills DJ, Roscher M, Thilsted SH, Thorne-Lyman AL: \u003cstrong\u003eHarnessing global fisheries to tackle micronutrient deficiencies\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2019, \u003cstrong\u003e574\u003c/strong\u003e(7776):95-98.\u003c/li\u003e\n\u003cli\u003ePauly D: \u003cstrong\u003eMicronutrient richness of global fish catches\u003c/strong\u003e. \u003cem\u003eNature: International Weekly Journal of Science \u003c/em\u003e2019(7776).\u003c/li\u003e\n\u003cli\u003eMohanty BP, Sankar T, Ganguly S, Mahanty A, Anandan R, Chakraborty K, Paul B, Sarma D, Dayal JS, Mathew S: \u003cstrong\u003eMicronutrient composition of 35 food fishes from India and their significance in human nutrition\u003c/strong\u003e. \u003cem\u003eBiological trace element research \u003c/em\u003e2016, \u003cstrong\u003e174\u003c/strong\u003e(2):448-458.\u003c/li\u003e\n\u003cli\u003eN\u0026ouml;lle N, Genschick S, Schwadorf K, Hrenn H, Brandner S, Biesalski HK: \u003cstrong\u003eFish as a source of (micro)nutrients to combat hidden hunger in Zambia\u003c/strong\u003e. \u003cem\u003eFood Security \u003c/em\u003e2020, \u003cstrong\u003e12\u003c/strong\u003e(6):1385-1406.\u003c/li\u003e\n\u003cli\u003eYang B, Ren X-L, Fu Y-Q, Gao J-L, Li D: \u003cstrong\u003eRatio of n-3/n-6 PUFAs and risk of breast cancer: a meta-analysis of 274135 adult females from 11 independent prospective studies\u003c/strong\u003e. \u003cem\u003eBMC cancer \u003c/em\u003e2014, \u003cstrong\u003e14\u003c/strong\u003e(1):1-14.\u003c/li\u003e\n\u003cli\u003eHuang W-Q, Long W-Q, Mo X-F, Zhang N-Q, Luo H, Lin F-Y, Huang J, Zhang C-X: \u003cstrong\u003eDirect and indirect associations between dietary magnesium intake and breast cancer risk\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2019, \u003cstrong\u003e9\u003c/strong\u003e(1):5764-5764.\u003c/li\u003e\n\u003cli\u003eLiu X, Montissol S, Uber A, Ganley S, Grossestreuer AV, Berg K, Heydrick S, Donnino MW: \u003cstrong\u003eThe effects of thiamine on breast cancer cells\u003c/strong\u003e. \u003cem\u003eMolecules \u003c/em\u003e2018, \u003cstrong\u003e23\u003c/strong\u003e(6):1464.\u003c/li\u003e\n\u003cli\u003eTashla T, Žuža M, Kenjve\u0026scaron; T, Prodanović R, Sole\u0026scaron;a D, Bursić V, Petrović A, Pelić DL, Bo\u0026scaron;ković J, Puvača N: \u003cstrong\u003eFish as an important bio-indicator of environmental pollution with persistent organic pollutants and heavy metals\u003c/strong\u003e. \u003cem\u003eJournal of Agronomy \u003c/em\u003e2018, \u003cstrong\u003e28\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLi N, Wu X, Zhuang W, Xia L, Chen Y, Wu C, Rao Z, Du L, Zhao R, Yi M: \u003cstrong\u003eFish consumption and multiple health outcomes: Umbrella review\u003c/strong\u003e. \u003cem\u003eTrends in Food Science \u0026amp; Technology \u003c/em\u003e2020, \u003cstrong\u003e99\u003c/strong\u003e:273-283.\u003c/li\u003e\n\u003cli\u003eCho E, Spiegelman D, Hunter DJ, Chen WY, Stampfer MJ, Colditz GA, Willett WC: \u003cstrong\u003ePremenopausal fat intake and risk of breast cancer\u003c/strong\u003e. \u003cem\u003eJournal of the National Cancer Institute \u003c/em\u003e2003, \u003cstrong\u003e95\u003c/strong\u003e(14):1079-1085.\u003c/li\u003e\n\u003cli\u003eHuang Z, Hankinson SE, Colditz GA, Stampfer MJ, Hunter DJ, Manson JE, Hennekens CH, Rosner B, Speizer FE, Willett WC: \u003cstrong\u003eDual effects of weight and weight gain on breast cancer risk\u003c/strong\u003e. \u003cem\u003eJama \u003c/em\u003e1997, \u003cstrong\u003e278\u003c/strong\u003e(17):1407-1411.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fish, seafood, diet, breast cancer, case-control study","lastPublishedDoi":"10.21203/rs.3.rs-4202460/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4202460/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eEven though regular fish consumption is highly recommended to reduce the risk of developing several cancers, available evidence on the association between breast cancer and fish is inconclusive. We aimed to explore the association between fish consumption and breast cancer in a well-characterized population of Iranian women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study enrolled 350 newly diagnosed stage I-IV breast cancer patients and 700 cancer-free controls living in Isfahan. Controls had no family history of breast cancer and were matched to cancer patients in term of age and socioeconomic status (SES). Dietary intakes were determined using a validated food frequency questionnaire (DS-FFQ). In addition, data on potential confounding factors were collected using pre-tested questionnaires.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAfter adjusting for potential confounders, women in the highest quartile of fish consumption had a lower odds of breast cancer than those in the lowest quartile (OR: 0.57, 95% CI: 0.34-0.95). The stratified analysis by menopausal status revealed an inverse association in postmenopausal women (OR: 0.53, 95% CI: 0.30-0.94).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eOur findings suggest higher fish consumption is associated with a lower likelihood of having breast cancer, particularly in post-menopause. The findings should be interpreted with caution due to the case-control study design. To further examine this association, prospective studies are needed.\u003c/p\u003e","manuscriptTitle":"Fish consumption in relation to breast cancer: A case-control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 13:02:11","doi":"10.21203/rs.3.rs-4202460/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5892f1a6-c346-444f-a785-9f1137469f16","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-20T10:08:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 13:02:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4202460","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4202460","identity":"rs-4202460","version":["v1"]},"buildId":"cTy_lsJlmDsVRNrSptgXS","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}