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Sasanfar B, Toorang F, Nemati S, Mohebbi E, Azadbakht L, Zendehdel K. Dietary Patterns and Risk of Breast Cancer among Pre and Post-Menopausal Women: A Case-Control Study in Iran. JNFS 2022; 7 (3) :282-298
URL: http://jnfs.ssu.ac.ir/article-1-508-en.html
URL: http://jnfs.ssu.ac.ir/article-1-508-en.html
Bahareh Sasanfar 
, Fatemeh Toorang , Saeed Nemati , Elham Mohebbi , Leila Azadbakht * , Kazem Zendehdel
, Fatemeh Toorang , Saeed Nemati , Elham Mohebbi , Leila Azadbakht * , Kazem Zendehdel
Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
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Dietary Patterns and Risk of Breast Cancer among Pre and Post-Menopausal Women: A Case-Control Study in Iran
Bahareh Sasanfar; MSc 1,2, Fatemeh Toorang; MSc 2,3, Saeed Nemati; MSc 2, Elham Mohebbi; DVM, MPH, PhD2, Leila Azadbakht; PhD *3,5,6 & Kazem Zendehdel; MD, PhD 2,7,8
Bahareh Sasanfar; MSc 1,2, Fatemeh Toorang; MSc 2,3, Saeed Nemati; MSc 2, Elham Mohebbi; DVM, MPH, PhD2, Leila Azadbakht; PhD *3,5,6 & Kazem Zendehdel; MD, PhD 2,7,8
1 Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
2 Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
3 Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.
4 Pathology and Stem Cell Research Center, Kerman University of Medical Sciences, Kerman, 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 Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
8 Breast Diseases Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
2 Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
3 Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.
4 Pathology and Stem Cell Research Center, Kerman University of Medical Sciences, Kerman, 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 Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
8 Breast Diseases Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
| ARTICLE INFO | ABSTRACT | |
| ORIGINAL ARTICLE | Background: Most of previous studies have investigated the association of specific food or nutrient with risk of breast cancer (BC) rather than overall diet and there is lack of evidence regarding dietary pattern in BC development. This study aims to examine the association between dietary patterns and risk of BC among Iranian women. Methods: This case–control study was carried out on 453 BC women and 496 healthy controls. A reliable and valid FFQ was used to evaluate usual dietary intake. Factor analysis was also applied to address the most major dietary patterns which were known as the healthy and unhealthy dietary patterns. Results: Those in the highest quartile of healthy dietary pattern had lower risk of BC (P = 0.001). However, being in the highest quartile of unhealthy pattern was associated with 2.04-fold increase of BC risk (P = 0.004) compared to the lowest quartile. In premenopausal women, risk of BC in upper quartile of healthy pattern was lower than the lowest quartile (OR = 0.47; CI = 0.26 – 0.83, P = 0.004). More adherence to the unhealthy dietary pattern was associated with higher risk of BC (OR = 2.85; 95% CI = 1.57- 5.17, P < 0.0001). No significant association was observed between the healthy or unhealthy dietary patterns among postmenopausal women. Conclusion: The findings indicated that the healthy dietary pattern could be considered as a protective factor in terms of BC in women and particularly among premenopausal women. Keywords: Dietary pattern; Breast neoplasms; Factor analysis; Case-control |
|
| Article history: Received: 29 Oct 2021 Revised:30 Nov 2021 Accepted: 12 Dec 2021 |
||
| *Corresponding author: azadbakhtleila@gmail.com Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran. Postal code: 14155-6117 Tel: +98 21 88955805 |
Introduction
Breast cancer (BC) is the most common cancer among women all over the world (Fitzmaurice et al., 2017). Although incidence rate of BC is much more in western countries than Asian countries, its rate is increasing in Middle-eastern countries, including Iran (Althuis et al., 2005, Pisani et al., 2002, Rouhollahi et al., 2014, Youlden et al., 2014). In 2018, the incidence of BC was 31 per 100000 in Iranian women (International Agency for Research on Cancer (IARC), 2018, Zendehdel, 2019). High burden of BC has directed many investigations to design and imply prevention strategies by knowing more about its risk factors (Daroudi et al., 2015).
BC is a multifactorial disease and is related to genetic, age, obesity, and several other factors (Ghiasvand et al., 2012). However, among the mentioned factors, diet is a modifiable risk factor that has been studied in several previous investigations (Lima et al., 2008, Michels et al., 2007, Seiler et al., 2018). The increasing trend of the incidence of BC among Asian immigrants in western countries (Althuis et al., 2005, Gomez et al., 2017, Shuldiner et al., 2018, Wu and Bernstein, 1998) suggests that environmental factors have an essential role in BC incidence (Hiatt and Brody, 2018). Several studies have shown the association between BC risk and dietary factors. However, controversial findings have been reported (Farvid et al., 2014, Farvid et al., 2018a, Kim et al., 2016). These studies have not considered the interaction between various components of diet (Farvid et al., 2018b, Genkinger et al., 2012). It could be important for public health programs because people do not intake separated nutrients or foods. These interactions could be captured when all foods of diet are evaluated entirety. Therefore, assessment of dietary patterns could add information about BC risk factors (Link et al., 2013).
Several studies have assessed the association between dietary pattern and BC in developed countries and found an increased risk of BC in individuals who followed "western dietary pattern" (Agurs-Collins et al., 2009, Buck et al., 2011). A study in Shanghai conducted on BC showed similar results in this regard (Cui et al., 2007). In China, vegetable-fruit-soy-milk-fish pattern was a protective factor, while refined grain-meat-pickle pattern was demonstrated as a risk factor of BC (Zhang et al., 2011). A study from Japan showed three dietary patterns, including vegetable pattern, animal food pattern, and dairy product pattern (Kojima et al., 2017). In contrast to previous studies (Guo et al., 2015, Inoue‐Choi et al., 2016), animal food pattern significantly decreased the risk of BC in the mentioned Japanese study (Kojima et al., 2017). However, some studies reported null relations regarding different dietary patterns and BC (Buck et al., 2011, Couto et al., 2013). In Iran, a case-control study on 274 BC patients illustrated that those who followed "unhealthy" dietary pattern had higher risk of BC (Karimi et al., 2014).
Middle-eastern region is a unique place for nutrition epidemiology studies due to the variety of dietary patterns and also high intake of refined carbohydrates and hydrogenated fats. Moreover, nutrition transition is taking place in this region (Esmaillzadeh and Azadbakht, 2008, Mazzocchi et al., 2008). Dietary pattern changes and non-communicable diseases are related in the region (Hawkes, 2006). Hence, Iranian dietary patterns should be evaluated in terms of BC risk. In this study, the association between dietary patterns and the risk of BC among Iranian women is evaluated.
Materials and Methods
Participants: A hospital based case-control study was conducted during 2014-2016. The participants were women in the age range of 19-80 years who were admitted in the Cancer Institute of Iran (Atefeh Ardestani et al., 2017). They were not on a specific diet. Moreover, 486 pathologically confirmed BC patients were recruited from surgery, chemotherapy or radiotherapy departments diagnosed within the previous year. They had no history of any cancers and were free of any diseases. The researchers recruited 523 controls frequency matched for age (10 year intervals), and residential place from healthy visitors, including visitors, relatives, and friends of non-cancer patients who were hospitalized for other chronic diseases in the hospital (a general hospital where cancer institute is situated in it).
For data analysis, the participants with a total energy intake of >5500 or <800 kcal/d, were excluded as under and over reporting (n = 35) (Vahid et al., 2018). This value was also checked with the range of ±3SD energy intakes of all participants. However, if less than 70 items were answered and could not be completed through calling, the participant would be omitted (n=25). Finally, sixty participants were excluded and 453 cases and 496 controls were analyzed.
Assessment of dietary intake: All the participants completed a general questionnaire through a face to face interview. The questionnaire covered information about demographic and anthropometric characteristics, physical activity, and history of obstetrics and gynecology. Weight was measured while the women were without shoes and minimally clothed with using digital scales to the nearest 100g. Height was measured while the women were standing and without shoes by using tape meter. Body mass index (BMI) was calculated by dividing weigh by height squared. Global Physical Activity Questionnaire (GPAQ) was used to assess level of physical activity. GPAQ consists of 16 questions which are in 4 physical activity domains, including job related activities, transportation activities, recreation and sport activities, and sedentary behaviors. GPAQ Analysis Guide was applied to calculate MET-hours per week values (World Health Organization, 2012).
Dietary intake in the last year of date of the interview was estimated using a 168 item semi-quantitative Food Frequency Questionnaire (FFQ) administered by trained interviewers. Its validity was confirmed in Iranian society against urine potassium and nitrogen level as a gold standard for potassium and protein intake and double labeled water as a gold standard for energy intake (Mirmiran et al., 2010). Moreover, it showed a good validity against 12 dietary recalls (Asghari et al., 2012). The reliability was high when it was repeated one year later (Esfahani et al., 2010). The data were converted to gram per day of food intake. Then, daily intake of energy and nutrients were computed using USDA food composition table (Gebhardt et al., 2006).
Ethical considerations: The study was approved by ethics committee of Tehran University of Medical Science (Cod: 93-03-51-27113). An informed written consent was given from all the participants before interviewing.
Sample size and data analysis: Sample size was calculated based on the odds ratio (OR=1.5), prevalence of healthy diet (P = 25%), study power (β = 0.8) and level of significance (α = 0.05). The Number of participants for each group was estimated n = 466; however, regarding attrition in sample size we designed to recruit at least 500 cases and 500 controls. A factor analysis was performed with orthogonal rotations transformation on 42 food groups (Appendix 1) to extract major dietary patterns and retained factors in connection with Eigen values >1. It was performed on these 42 food groups based on the study outcome (cancer) and previous publications (Esmaillzadeh and Azadbakht, 2008, Esmaillzadeh et al., 2007, Karimi et al., 2014).The extracted factors (dietary patterns) were labeled on the basis of the interpretation from the study data and prior studies. Then, factor scores were calculated for each dietary pattern by summing intakes of food groups weighted by their factor loading. Each individual received a factor score for each identified pattern. Scores were adjusted for energy by using the residual method.
The participants were categorized into quartiles based on dietary pattern scores. One-way ANOVA and chi-square tests were used to compare distribution of continuous and categorical variables across dietary quartiles. Logistic regression was also utilized to assess the association of dietary patterns and the risk of BC by reporting odds ratio and 95% confidence interval.
Three main covariate-adjusted models were considered. The first model was adjusted for age, energy, family history of BC, BMI (continuously), menarche age, weight at age 18-20 y, and menopausal status (premenopausal or postmenopausal). Marital status was also added, parity in the 2nd model as a covariate. Additionally, in the 3rd model effect of couple of variables, including alcohol intake, menopausal hormone use, contraceptive use, and cigarette smoking, were also controlled as the confounders. Sub-group analysis was also done and all analyses were repeated for pre and post menopause women separately. All analysis was performed by Stata version 14 (State Corp, College Station, TX).
Results
Two main dietary patterns were identified from this study (Appendix 2). These patterns included the healthy dietary pattern (high intake of fruits, fruit juices, cruciferous vegetables, yellow vegetables, tomatoes, green leafy vegetables, other vegetables, and garlic), and the unhealthy dietary pattern (high intake of processed meat, eggs, butter, refined grains, mayonnaise, hydrogenated fat, sugars, soft drinks, and pickles). These labels were chosen based on the dietary pattern labels in previous studies (Esmaillzadeh and Azadbakht, 2008, Esmaillzadeh et al., 2007, Karimi et al., 2014). These two dietary patterns explained 71.9% of the whole variance and 47.9 and 23.9% for healthy dietary pattern and unhealthy dietary pattern, respectively.
Demographic, reproductive, and lifestyle characteristics of the subjects of case and control groups are provided in Table 1. BC patients were slightly older (P = 0.002), had lower BMI (P = 0.02), and have more family history of BC (P < 0.001) compared to the control group. The patients had lower physical activity (P < 0.001), were less married (P = 0.001), had lower rate of consumption of oral contraceptives (P = 0.02), had lower rate of using postmenopausal hormones (P = 0.03) and were less alcohol users (P = 0.01) compared to the control group. The patients had lower intakes of vegetables compared to the control group.
Table 2 shows the participants’ characteristics across quartiles of the addressed dietary patterns. The highest mean age was observed in the fourth quartile healthy dietary pattern (47.7 ± 10.2 years); while in unhealthy dietary pattern the first quartile had the highest mean age (49.1 ± 9.6 years). Women in the top quartile of the healthy dietary pattern score had more slightly higher BMI, higher age at menarche, more percentage of menopause women, and more number of children compared to those in the lowest quartile of the healthy dietary pattern. Significant differences were found for physical activity level across quartile of healthy dietary pattern (P < 0.0001). Compared with those in the lowest quartile, the subjects in the upper quartile of healthy dietary pattern consumed more carbohydrate (P = 0.04). Individuals with greater unhealthy dietary pattern score had lower BMI, later menarche, lower percentage of menopause women, more nulliparous/missing, and low number of children compared to those in the lowest quartile. Higher intakes of carbohydrate and lower intakes of protein and fat were found among the subjects in top quartile of unhealthy dietary pattern.
Odds ratios of BC across categories of each dietary pattern score are presented in Table 3. The odds ratios of before and after multivariable adjustment for age and energy have shown significant association between the two major dietary pattern and BC. It is observed that healthy dietary pattern could decrease risk of BC (OR = 0.61; 95% CI = 0.41-0.92, P = 0.008) in model 1. In contrast, the unhealthy pattern was in relation to an increase of BC (OR = 1.82; 95% CI=1.20-2.76, P = 0.001). Comparing to the lowest quartile, risk of BC was decreased by 43% in the highest quartile of healthy dietary pattern (P = 0.003); whereas according to model 2, the highest quartile of unhealthy pattern had 1.87-fold increase of BC risk (OR = 1.87; 95% CI = 1.23-2.86, P = 0.001) compared to the lowest quartile. After additional adjustment for all confounders in model 3, negative association was addressed between the healthy pattern score and risk of BC (OR = 0.52; 95% CI = 0.33-0.82, P = 0.001) while a positive association was found between score of unhealthy pattern and risk of BC (OR = 1.96; 95% CI = 1.23-3.10, P = 0.004).
Table 4 provides the relationship between each dietary pattern and risk of BC for pre-and postmenopausal women. In premenopausal women, there is a negative association between risk of BC and score of healthy dietary pattern. Odds ratio for BC decreased by increasing the score of healthy pattern (OR= 0.52; 95% CI = 0.31-0.87, P = 0.008) in model 1. In contrast, premenopausal women in upper quartile of unhealthy pattern increased risk of BC compare to the lowest quartile (OR = 2.55; CI = 1.48-4.40, P < 0.0001). After controlling for further confounders, these associations remained significant in model 2. Additional adjustment for premenopausal showed that healthy pattern had 53% decrease in BC risk (P = 0.004), while unhealthy pattern had 2.85-fold increase in BC risk (P < 0.0001). In the postmenopausal women, no significant association was observed between the healthy or unhealthy dietary pattern
BC is a multifactorial disease and is related to genetic, age, obesity, and several other factors (Ghiasvand et al., 2012). However, among the mentioned factors, diet is a modifiable risk factor that has been studied in several previous investigations (Lima et al., 2008, Michels et al., 2007, Seiler et al., 2018). The increasing trend of the incidence of BC among Asian immigrants in western countries (Althuis et al., 2005, Gomez et al., 2017, Shuldiner et al., 2018, Wu and Bernstein, 1998) suggests that environmental factors have an essential role in BC incidence (Hiatt and Brody, 2018). Several studies have shown the association between BC risk and dietary factors. However, controversial findings have been reported (Farvid et al., 2014, Farvid et al., 2018a, Kim et al., 2016). These studies have not considered the interaction between various components of diet (Farvid et al., 2018b, Genkinger et al., 2012). It could be important for public health programs because people do not intake separated nutrients or foods. These interactions could be captured when all foods of diet are evaluated entirety. Therefore, assessment of dietary patterns could add information about BC risk factors (Link et al., 2013).
Several studies have assessed the association between dietary pattern and BC in developed countries and found an increased risk of BC in individuals who followed "western dietary pattern" (Agurs-Collins et al., 2009, Buck et al., 2011). A study in Shanghai conducted on BC showed similar results in this regard (Cui et al., 2007). In China, vegetable-fruit-soy-milk-fish pattern was a protective factor, while refined grain-meat-pickle pattern was demonstrated as a risk factor of BC (Zhang et al., 2011). A study from Japan showed three dietary patterns, including vegetable pattern, animal food pattern, and dairy product pattern (Kojima et al., 2017). In contrast to previous studies (Guo et al., 2015, Inoue‐Choi et al., 2016), animal food pattern significantly decreased the risk of BC in the mentioned Japanese study (Kojima et al., 2017). However, some studies reported null relations regarding different dietary patterns and BC (Buck et al., 2011, Couto et al., 2013). In Iran, a case-control study on 274 BC patients illustrated that those who followed "unhealthy" dietary pattern had higher risk of BC (Karimi et al., 2014).
Middle-eastern region is a unique place for nutrition epidemiology studies due to the variety of dietary patterns and also high intake of refined carbohydrates and hydrogenated fats. Moreover, nutrition transition is taking place in this region (Esmaillzadeh and Azadbakht, 2008, Mazzocchi et al., 2008). Dietary pattern changes and non-communicable diseases are related in the region (Hawkes, 2006). Hence, Iranian dietary patterns should be evaluated in terms of BC risk. In this study, the association between dietary patterns and the risk of BC among Iranian women is evaluated.
Materials and Methods
Participants: A hospital based case-control study was conducted during 2014-2016. The participants were women in the age range of 19-80 years who were admitted in the Cancer Institute of Iran (Atefeh Ardestani et al., 2017). They were not on a specific diet. Moreover, 486 pathologically confirmed BC patients were recruited from surgery, chemotherapy or radiotherapy departments diagnosed within the previous year. They had no history of any cancers and were free of any diseases. The researchers recruited 523 controls frequency matched for age (10 year intervals), and residential place from healthy visitors, including visitors, relatives, and friends of non-cancer patients who were hospitalized for other chronic diseases in the hospital (a general hospital where cancer institute is situated in it).
For data analysis, the participants with a total energy intake of >5500 or <800 kcal/d, were excluded as under and over reporting (n = 35) (Vahid et al., 2018). This value was also checked with the range of ±3SD energy intakes of all participants. However, if less than 70 items were answered and could not be completed through calling, the participant would be omitted (n=25). Finally, sixty participants were excluded and 453 cases and 496 controls were analyzed.
Assessment of dietary intake: All the participants completed a general questionnaire through a face to face interview. The questionnaire covered information about demographic and anthropometric characteristics, physical activity, and history of obstetrics and gynecology. Weight was measured while the women were without shoes and minimally clothed with using digital scales to the nearest 100g. Height was measured while the women were standing and without shoes by using tape meter. Body mass index (BMI) was calculated by dividing weigh by height squared. Global Physical Activity Questionnaire (GPAQ) was used to assess level of physical activity. GPAQ consists of 16 questions which are in 4 physical activity domains, including job related activities, transportation activities, recreation and sport activities, and sedentary behaviors. GPAQ Analysis Guide was applied to calculate MET-hours per week values (World Health Organization, 2012).
Dietary intake in the last year of date of the interview was estimated using a 168 item semi-quantitative Food Frequency Questionnaire (FFQ) administered by trained interviewers. Its validity was confirmed in Iranian society against urine potassium and nitrogen level as a gold standard for potassium and protein intake and double labeled water as a gold standard for energy intake (Mirmiran et al., 2010). Moreover, it showed a good validity against 12 dietary recalls (Asghari et al., 2012). The reliability was high when it was repeated one year later (Esfahani et al., 2010). The data were converted to gram per day of food intake. Then, daily intake of energy and nutrients were computed using USDA food composition table (Gebhardt et al., 2006).
Ethical considerations: The study was approved by ethics committee of Tehran University of Medical Science (Cod: 93-03-51-27113). An informed written consent was given from all the participants before interviewing.
Sample size and data analysis: Sample size was calculated based on the odds ratio (OR=1.5), prevalence of healthy diet (P = 25%), study power (β = 0.8) and level of significance (α = 0.05). The Number of participants for each group was estimated n = 466; however, regarding attrition in sample size we designed to recruit at least 500 cases and 500 controls. A factor analysis was performed with orthogonal rotations transformation on 42 food groups (Appendix 1) to extract major dietary patterns and retained factors in connection with Eigen values >1. It was performed on these 42 food groups based on the study outcome (cancer) and previous publications (Esmaillzadeh and Azadbakht, 2008, Esmaillzadeh et al., 2007, Karimi et al., 2014).The extracted factors (dietary patterns) were labeled on the basis of the interpretation from the study data and prior studies. Then, factor scores were calculated for each dietary pattern by summing intakes of food groups weighted by their factor loading. Each individual received a factor score for each identified pattern. Scores were adjusted for energy by using the residual method.
The participants were categorized into quartiles based on dietary pattern scores. One-way ANOVA and chi-square tests were used to compare distribution of continuous and categorical variables across dietary quartiles. Logistic regression was also utilized to assess the association of dietary patterns and the risk of BC by reporting odds ratio and 95% confidence interval.
Three main covariate-adjusted models were considered. The first model was adjusted for age, energy, family history of BC, BMI (continuously), menarche age, weight at age 18-20 y, and menopausal status (premenopausal or postmenopausal). Marital status was also added, parity in the 2nd model as a covariate. Additionally, in the 3rd model effect of couple of variables, including alcohol intake, menopausal hormone use, contraceptive use, and cigarette smoking, were also controlled as the confounders. Sub-group analysis was also done and all analyses were repeated for pre and post menopause women separately. All analysis was performed by Stata version 14 (State Corp, College Station, TX).
Results
Two main dietary patterns were identified from this study (Appendix 2). These patterns included the healthy dietary pattern (high intake of fruits, fruit juices, cruciferous vegetables, yellow vegetables, tomatoes, green leafy vegetables, other vegetables, and garlic), and the unhealthy dietary pattern (high intake of processed meat, eggs, butter, refined grains, mayonnaise, hydrogenated fat, sugars, soft drinks, and pickles). These labels were chosen based on the dietary pattern labels in previous studies (Esmaillzadeh and Azadbakht, 2008, Esmaillzadeh et al., 2007, Karimi et al., 2014). These two dietary patterns explained 71.9% of the whole variance and 47.9 and 23.9% for healthy dietary pattern and unhealthy dietary pattern, respectively.
Demographic, reproductive, and lifestyle characteristics of the subjects of case and control groups are provided in Table 1. BC patients were slightly older (P = 0.002), had lower BMI (P = 0.02), and have more family history of BC (P < 0.001) compared to the control group. The patients had lower physical activity (P < 0.001), were less married (P = 0.001), had lower rate of consumption of oral contraceptives (P = 0.02), had lower rate of using postmenopausal hormones (P = 0.03) and were less alcohol users (P = 0.01) compared to the control group. The patients had lower intakes of vegetables compared to the control group.
Table 2 shows the participants’ characteristics across quartiles of the addressed dietary patterns. The highest mean age was observed in the fourth quartile healthy dietary pattern (47.7 ± 10.2 years); while in unhealthy dietary pattern the first quartile had the highest mean age (49.1 ± 9.6 years). Women in the top quartile of the healthy dietary pattern score had more slightly higher BMI, higher age at menarche, more percentage of menopause women, and more number of children compared to those in the lowest quartile of the healthy dietary pattern. Significant differences were found for physical activity level across quartile of healthy dietary pattern (P < 0.0001). Compared with those in the lowest quartile, the subjects in the upper quartile of healthy dietary pattern consumed more carbohydrate (P = 0.04). Individuals with greater unhealthy dietary pattern score had lower BMI, later menarche, lower percentage of menopause women, more nulliparous/missing, and low number of children compared to those in the lowest quartile. Higher intakes of carbohydrate and lower intakes of protein and fat were found among the subjects in top quartile of unhealthy dietary pattern.
Odds ratios of BC across categories of each dietary pattern score are presented in Table 3. The odds ratios of before and after multivariable adjustment for age and energy have shown significant association between the two major dietary pattern and BC. It is observed that healthy dietary pattern could decrease risk of BC (OR = 0.61; 95% CI = 0.41-0.92, P = 0.008) in model 1. In contrast, the unhealthy pattern was in relation to an increase of BC (OR = 1.82; 95% CI=1.20-2.76, P = 0.001). Comparing to the lowest quartile, risk of BC was decreased by 43% in the highest quartile of healthy dietary pattern (P = 0.003); whereas according to model 2, the highest quartile of unhealthy pattern had 1.87-fold increase of BC risk (OR = 1.87; 95% CI = 1.23-2.86, P = 0.001) compared to the lowest quartile. After additional adjustment for all confounders in model 3, negative association was addressed between the healthy pattern score and risk of BC (OR = 0.52; 95% CI = 0.33-0.82, P = 0.001) while a positive association was found between score of unhealthy pattern and risk of BC (OR = 1.96; 95% CI = 1.23-3.10, P = 0.004).
Table 4 provides the relationship between each dietary pattern and risk of BC for pre-and postmenopausal women. In premenopausal women, there is a negative association between risk of BC and score of healthy dietary pattern. Odds ratio for BC decreased by increasing the score of healthy pattern (OR= 0.52; 95% CI = 0.31-0.87, P = 0.008) in model 1. In contrast, premenopausal women in upper quartile of unhealthy pattern increased risk of BC compare to the lowest quartile (OR = 2.55; CI = 1.48-4.40, P < 0.0001). After controlling for further confounders, these associations remained significant in model 2. Additional adjustment for premenopausal showed that healthy pattern had 53% decrease in BC risk (P = 0.004), while unhealthy pattern had 2.85-fold increase in BC risk (P < 0.0001). In the postmenopausal women, no significant association was observed between the healthy or unhealthy dietary pattern
| Table 1. Baseline demographic and daily dietary intake in termr of case and control. | |||
| Variables | Case (n = 453) | Control (n = 496) | P-valuea |
| Age (y) | 46.0 ± 10.3b | 44.0 ± 11.2 | 0.002 |
| Body mass index (kg/m2) | 28.1 ± 5.1 | 28.8 ± 6.0 | 0.02 |
| Physical activity (MET h/wk) | 20.0 ± 24.8 | 27.1 ± 38.8 | <0.001 |
| Age (y) | 12.9 ± 2.8 | 12.9 ± 2.7 | 0.47 |
| Whole grains (g) | 98.4 ± 4.9 | 98.4 ± 5.2 | 0.49 |
| Refined grains (g) | 338.3 ± 9.4 | 318.5 ± 8.4 | 0.05 |
| Fruits (g) | 581.8 ± 18.7 | 595.8 ± 17.8 | 0.29 |
| Vegetables (g) | 358.9 ± 11.8 | 385.0 ± 10.5 | 0.05 |
| Legumes (g) | 38.8 ± 1.9 | 42.6 ± 2.9 | 0.15 |
| Red meat (g) | 14.6 ± 0.8 | 16.4 ± 0.9 | 0.08 |
| Energy (kcal) | 2783.0 ± 48.5 | 2720.5 ± 47.8 | 0.17 |
| Carbohydrate (g) | 346.8 ± 6.4 | 340.8 ± 6.4 | 0.25 |
| Protein (g) | 80.6 ± 1.5 | 82.5 ± 1.7 | 0.21 |
| Fat (g) | 125.7 ± 3.3 | 120.5 ± 3.1 | 0.12 |
| Fiber (g) | 23.0 ± 0.5 | 23.5 ± 0.5 | 0.23 |
| Educational level | |||
| Non-University | 377 (83.7)c | 408 (84.1) | 0.27 |
| University | 73 (16.2) | 77 (15.8) | |
| Marital status | |||
| Married | 368 (81.2) | 417 (84.0) | 0.001 |
| Unmarried/divorced/widowed | 85 (18.7) | 79 (15.9) | |
| Menopausal status | |||
| Premenopausal | 301 (48.1) | 324 (51.8) | 0.97 |
| Postmenopausal | 152 (48.2) | 163 (51.7) | |
| Family history of breast cancer BC (yes) | 44 (9.7) | 7 (1.4) | <0.001 |
| Oral contraceptive use (yes) | 214 (52.3) | 258 (60.9) | 0.02 |
| Postmenopausal hormone use | 2 (0.4) | 10 (2.0) | 0.03 |
| Current smoking | 17 (3.7) | 24 (4.9) | 0.27 |
| Alcohol intake | 12 (2.6) | 30 (6.1) | 0.01 |
| Fertility treatment | 19 (4.5) | 10 (6.1) | 0.29 |
| Parity | |||
| Nulliparous/missing | 196 (43.2) | 210 (42.3) | 0.82 |
| 1 | 39 (8.6) | 51 (10.2) | |
| 2-3 | 147 (32.4) | 155 (31.2) | |
| ≥4 | 71 (16.6) | 80 (16.1) | |
| a: χ2 Test for ordinal qualitative variables and t-test for continuous variable; b: Mean ± SD ; c: N(%). | |||
| Table 2. Characteristics and daily dietary intake of the study population by quartile (Q) categories of dietary patterns scores . | ||||||||||
| Variables | Quartile of "healthy" dietary pattern score | Quartile of "unhealthy" dietary pattern score | ||||||||
| Q1 | Q2 | Q3 | Q4 | P-trenda | Q1 | Q2 | Q3 | Q4 | P-trend | |
| Case/control | 124/104 | 113/114 | 90/138 | 103/124 | 106/122 | 101/126 | 95/133 | 128/99 | ||
| Age ( y) | 42.2 ± 10.8b | 44.7 ± 11.2 | 44.8 ± 10.6 | 47.7 ± 10.2 | 0.03 | 49.1 ± 9.6 | 44.8 ± 11.2 | 43.0 ± 10.7 | 42.4 ± 10.7 | 0.05 |
| BMI (kg/m2) | 28.2 ± 5.5 | 28.4 ± 6,4 | 28.6 ± 5.9 | 28.7 ± 4,7 | 0.001 | 29.7 ± 5.8 | 27.9 ± 4.8 | 28.5 ± 5.9 | 27.8 ± 5.8 | 0.01 |
| Weight at age 18-20 y | 50.5 ± 17.5 | 50.8 ± 17.1 | 50.9 ± 17.4 | 52.9 ± 16.2 | 0.003 | 50.6 ± 17.1 | 52.4 ± 16.0 | 49.7 ± 18.1 | 52.3 ± 16.9 | 0.004 |
| Age at menarche (y) | 12.7 ± 3.6 | 12.8 ± 2.8 | 12.9 ± 2.8 | 13.5 ± 2.3 | 0.001 | 12.7 ± 2.8 | 13.0 ± 2.8 | 12.8 ± 2.8 | 13.0 ± 2.6 | 0.002 |
| No. of children | 2.2 ± 1.8 | 2.4 ± 1/8 | 2.4 ± 1.7 | 2.5 ± 1.6 | 0.002 | 2.7 ± 1.7 | 2.5 ± 1.8 | 2.1 ± 1.7 | 2.2 ± 1.7 | 0.01 |
| Total energy (kcal) | 3115.6 ± 1116.9 | 2427.8 ± 1048.1 | 2599.7 ± 905.3 | 2859.9 ± 1008.4 | 0.06 | 3050.6 ± 1128.9 | 2575.3 ± 973.6 | 2381 ± 924.4 | 2997.5 ± 1028.9 | 0.07 |
| Carbohydrate (% of energy) | 47.1 ± 12.4 | 51.8 ± 10.2 | 51.8 ± 9.1 | 52.4 ± 8.9 | 0.04 | 50.1 ± 12.0 | 50.2 ± 10.8 | 51.0 ± 9.31 | 51.7 ± 9.5 | 0.004 |
| Protein (% of energy) | 9.9 ± 2.9 | 12.4 ± 3.8 | 12.9 ± 3.1 | 13.8 ± 3.3 | 0.15 | 12.4 ± 4.1 | 12.2 ± 3.4 | 12.7 ± 3.7 | 11.9 ± 3.2 | 0.006 |
| Fat (% of energy) | 44.7 ± 13.8 | 37.9 ± 10.7 | 37.6 ± 9.4 | 36.1 ± 9.3 | 0.08 | 39.5 ± 13.4 | 39.7 ± 12.4 | 38.5 ± 9.9 | 38.7 ± 9.7 | 0.002 |
| Fiber (g) | 18.3 ± 8.2 | 19.6 ± 11.0 | 23.5 ± 7.8 | 31.9 ± 11.9 | 0.22 | 28.8 ± 14.4 | 21.5 ± 8.4 | 19.9 ± 9.5 | 23.2 ± 9.6 | 0.09 |
| Physical activity level | ||||||||||
| Low | 141 (61.4)c | 122 (53.7) | 132 (57.8) | 91 (40.09) | <0.0001 | 112 (49.1) | 114 (50.2) | 130 (57.0) | 130 (57.2) | 0.09 |
| Moderate | 59 (25.8) | 75 (33.0) | 59 (25.8) | 81 (35.6) | 77 (33.7) | 75 (33.0) | 69 (30.2) | 53 (23.3).9 | ||
| Vigorous | 28 (12.2) | 30 (13.2) | 37 (16.2) | 55 (24.2) | 39 (17.1) | 38 (16.7) | 29 (12.7) | 44 (19.3) | ||
| Education level | ||||||||||
| None | 31 (13.8) | 31 (13.9) | 20 (8.8) | 16 (7.14) | 0.1 | 29 (13.0) | 27 (12.0) | 21 (9.3) | 21 (9.3) | 0.35 |
| Primary | 56 (25.0) | 59 (26.4) | 59 (26.2) | 51 (22.7) | 61 (27.3) | 49 (21.8) | 58 (25.7) | 57 (25.4) | ||
| Secondary school | 106 (47.3) | 102 (45.7) | 100 (44.4) | 120 (53.5) | 102 (45.7) | 102 (45.5) | 107 (47.5) | 117 (52.2) | ||
| University | 31 (13.8) | 31 (13.9) | 46 (20.4) | 37 (16.5) | 31 (13.9) | 46 (20.5) | 39 (17.3) | 29 (12.9) | ||
| Marital status | ||||||||||
| Unmarried | 16 (7.0) | 17 (7.4) | 16 (7.0) | 11 (4.8) | 0.96 | 12 (5.2) | 14 (6.17) | 16 (7.0) | 18 (7.9) | 0.86 |
| Married | 189 (82.2) | 183 (80.6) | 188 (82.4) | 190 (83.7) | 186 (81.5) | 187 (82.3) | 187 (82.0) | 190 (83.7) | ||
| Divorced/widowed | 17 (7.4) | 22 (9.6) | 20 (8.7) | 22 (9.6) | 24 (10.5) | 22 (9.6) | 19 (8.3) | 16 (7.0) | ||
| Family history of BC | 12 (5.2) | 13 (5.7) | 6 (2.6) | 17 (7.4) | 0.13 | 16 (7.02) | 7 (3.08) | 11 (4.8) | 14 (6.1) | 0.25 |
| Oral contraceptive use | 116 (55.2) | 117 (57.1) | 113 (55.1) | 125 (59.5) | 0.66 | 108 (51.4) | 118 (56.4) | 124 (61.4) | 121 (57.9) | 0.31 |
| Postmenopausal women | 55 (25.3) | 65 (29.9) | 76 (35.1) | 94 (43.1) | 0.004 | 100 (45.8) | 73 (33.4) | 57 (26.5) | 60 (27.6) | 0.0001 |
| Postmenopausal hormone use | 1 (0.4) | 5 (2.2) | 3 (1.3) | 2 (0.8) | 0.35 | 2 (0.8) | 3 (1.3) | 5 (2.1) | 1 (0.4) | 0.36 |
| Alcohol intake | 10 (4.4) | 8 (3.6) | 11 (4.8) | 12 (5.3) | 0.84 | 8 (3.5) | 12 (5.3) | 9 (4.02) | 12 (5.3) | 0.73 |
| Smoking status | 11 (4.9) | 7 (3.1) | 9 (4.0) | 13 (5.8) | 0.67 | 10 (4.4) | 7 (3.1) | 7 (3.1) | 16 (7.1) | 0.07 |
| Nulliparous/missing | 96 (42.1) | 107 (47.1) | 9 8(42.9) | 86 (37.8) | 0.26 | 76 (33.3) | 95 (41.8) | 109 (47.8) | 107 (47.1) | 0.006 |
a: χ2 Test for ordinal qualitative variables and ANOVA for continuous variables; b: Mean ± SD; c: N (%).
| Table 3. Dietary pattern and risk of BC | ||||||||
| Dietary pattern | Quartile, all (n 946 breast cancer cases = 452) | |||||||
| Q1 | Q2 | Q3 | Q4 | P-trenda | ||||
| OR | OR | 95%CI | OR | 95%CI | OR | 95%CI | ||
| Healthy diet | ||||||||
| No. of cases (%) | 124 (28.8) | 113(26.3) | 90(20.9) | 103(23.9) | ||||
| Crude | 1.00 (Ref) | 0.83 | 0.57-1. 20 | 0.54 | 0.37-0.79 | 0.69 | 0.48-1.00 | 0.03 |
| Age adjusted | 1.00 (Ref) | 0.76 | 0.52-1.11 | 0.51 | 0.34-0.74 | 0.61 | 0.41-0.88 | 0.006 |
| Age and energy adjusted | 1.00 (Ref) | 0.79 | 0.53-1.16 | 0.52 | 0.35-0.76 | 0.61 | 0.41-0.89 | 0.006 |
| Model 1b | 1.00 (Ref) | 0.86 | 0.57-1.31 | 0.58 | 0.38-0.88 | 0.61 | 0.41-0.92 | 0.008 |
| Model 2c | 1.00 (Ref) | 0.84 | 0.55-1.27 | 0.56 | 0.37-0.84 | 0.57 | 0.37-0.86 | 0.003 |
| Model 3d | 1.00 (Ref) | 0.93 | 0.59-1.48 | 0.59 | 0.37-0.92 | 0.52 | 0.33-0.82 | 0.001 |
| Unhealthy diet | ||||||||
| No. of cases (%) | 106 (24.6) | 101(23.5) | 95(22.1) | 128(29.8) | ||||
| Crude | 1.00 (Ref) | 0.92 | 0.63-1.33 | 0.82 | 0.56-1.19 | 1.48 | 1.02-2.15 | 0.007 |
| Age adjusted | 1.00 (Ref) | 0.99 | 0.68-1.45 | 0.94 | 0.64-1.38 | 1.73 | 1.17-2.54 | 0.001 |
| Age and energy adjusted | 1.00 (Ref) | 1.02 | 0.69-1.49 | 0.97 | 0.65-1.44 | 1.74 | 1.18-2.56 | 0.001 |
| Model 1b | 1.00 (Ref) | 1.01 | 0.66-1.52 | 1.02 | 0.66-1.56 | 1.82 | 1.20-2.76 | 0.001 |
| Model 2c | 1.00 (Ref) | 1.05 | 0.69-1.59 | 1.05 | 0.68-1.63 | 1.87 | 1.23-2.86 | 0.001 |
| Model 3d | 1.00 (Ref) | 1.26 | 0.80-1.99 | 1.33 | 0.83-2.15 | 1.96 | 1.23-3.10 | 0.004 |
| a: P-trend is resulted from logistic regression test based on median values of each quartile; b: Adjusted for age, energy, family history of BC, BMI (continuously), menarche age, weight at age 18-20, menopausal status; c: further adjusted for marital status and parity; d: Additionally, adjusted for alcohol intake, menopausal hormone use, contraceptive use, cigarette smoking. | ||||||||
| Table 4. Odds ratios for breast cancer risk across quartile categories of dietary pattern scores by menopausal status. | ||||||||
| Dietary pattern | Quartile of dietary pattern score | |||||||
| Q1 | Q2 | Q3 | Q4 | |||||
| OR | 95%CI | OR | 95%CI | OR | 95%CI | P-trenda | ||
| Premenopause (n 576; cases = 285) | ||||||||
| Healthy dietary pattern | ||||||||
| Cases/controls | 92/70 | 76/75 | 61/78 | 56/68 | ||||
| Model 1b | 1 | 0.79 | 0.48-1.29 | 0.59 | 0.36-0.96 | 0.52 | 0.31-0.87 | 0.008 |
| Model 2c | 1 | 0.82 | 0.50-1.34 | 0.59 | 0.36-0.97 | 0.51 | 0.30-0.85 | 0.006 |
| Model 3d | 1 | 0.89 | 0.52-1.53 | 0.59 | 0.34-1.02 | 0.47 | 0.26-0.83 | 0.004 |
| Unhealthy dietary pattern | ||||||||
| Cases/controls | 51/61 | 63/85 | 75/73 | 95/60 | ||||
| Model 1b | 1 | 1.12 | 0.65-1.94 | 1.59 | 0.91-2.78 | 2.55 | 1.48-4.40 | <0.0001 |
| Model 2c | 1 | 1.13 | 0.65-1.97 | 1.62 | 0.92-2.84 | 2.56 | 1.48-4.42 | <0.0001 |
| Model 3d | 1 | 1.51 | 0.83-2.76 | 2.27 | 1.22-4.23 | 2.85 | 1.57-5.17 | <0.0001 |
| Postmenopause (n 290; cases = 138) | ||||||||
| Healthy dietary pattern | ||||||||
| Cases/controls | 28/27 | 36/29 | 28/48 | 46/48 | ||||
| Model 1b | 1 | 1.19 | 0.53-2.69 | 0.58 | 0.26-1.24 | 0.81 | 0.39-1.66 | 0.34 |
| Model 2c | 1 | 1.02 | 0.45-2.34 | 0.48 | 0.22-1.07 | 0.68 | 0.32-1.42 | 0.19 |
| Model 3d | 1 | 1.24 | 0.49-3.09 | 0.54 | 0.22-1.28 | 0.71 | 0.32-1.59 | 0.20 |
| Unhealthy dietary pattern | ||||||||
| Cases/controls | 50/50 | 37/36 | 20/37 | 31/29 | ||||
| Model 1b | 1 | 1.20 | 0.61-2.36 | 0.52 | 0.25-1.10 | 1.25 | 0.62-2.53 | 0.86 |
| Model 2c | 1 | 1.26 | 0.63-2.52 | 0.54 | 0.25-1.13 | 1.34 | 0.65-2.74 | 0.77 |
| Model 3d | 1 | 1.46 | 0.69-3.11 | 0.48 | 0.21-1.10 | 1.23 | 0.56-2.70 | 0.90 |
| a: P-trend is resulted from logistic regression test based on median values of each quartile; b: Adjusted for age, energy, family history of BC, BMI (continuously), menarche age, weight at age 18-20, menopausal status; c: Further adjusted for marital status and parity; d: Additionally, adjusted for alcohol intake, menopausal hormone use, contraceptive use, cigarette smoking | ||||||||
Discussion
The present study identified two major dietary patterns, named healthy and unhealthy dietary patterns. These extracted dietary patterns were similar to patterns found in a previous study in Iran that used the same FFQ (Hajizadeh et al., 2010, Rezazadeh et al., 2010). It was found that a healthy dietary pattern with high intake of fruits, fruit juices, cruciferous vegetables, yellow vegetables, tomato, green leafy vegetables, other vegetables, and garlic was associated with a decrease in the risk of BC. However, an unhealthy dietary pattern with high intake of processed meats, eggs, butter, refined grains, mayonnaise, hydrogenated fat, sugars, soft drinks, and pickles increased the risk of BC. These associations remained significant, even after stratifying by menopausal status, in premenopausal women.
The results found from previous studies regarding the association of BC risk and dietary pattern were inconsistent. Moreover, there are not several studies that can examine the association between BC risk and dietary patterns in Middle-Eastern countries. Most previous studies were conducted in developed countries and have derived different patterns (Adebamowo et al., 2005, Agurs-Collins et al., 2009, Baglietto et al., 2011, Fung et al., 2005, Harris et al., 2017, Männistö et al., 2005, Sieri et al., 2004, Velie et al., 2005). Only one case-control study with small sample size (100 cases-174 controls) assessed the association between dietary pattern and BC risk in Iran and found the relationship between a healthy and unhealthy dietary pattern with BC risk (Karimi et al., 2014). In agreement with the present results, some case-control studies have illustrated an inverse association between the risk of BC and healthy or similar to healthy dietary pattern (De Stefani et al., 2009, Hirose et al., 2007, Murtaugh et al., 2008, Ronco et al., 2006, Wu et al., 2009, Zhang et al., 2011). A positive association have been distinguished between unhealthy or western dietary patterns and BC risk (Cui et al., 2007, De Stefani et al., 2009, Edefonti et al., 2008, Murtaugh et al., 2008, Ronco et al., 2006, Wu et al., 2009). The results of a recent meta-analysis on case-control studies indicated that a higher score of healthy pattern was associated with a decreased BC risk and higher adherence to unhealthy pattern was associated with a 33% increase in risk of BC (Grosso et al., 2017). A number of studies explored the protective effects of fruits and vegetables against BC (Eliassen et al., 2012, Fritz et al., 2013, Gandini et al., 2000, Gong et al., 2014, Harris et al., 2014, Wu et al., 2013) that may have stemmed from high content of fiber and much amounts of antioxidants and certain photochemical which contribute to inhibition bio-activation of carcinogen, signaling of cell, cell cycle regulation, inflammation, and angiogenesis (Giacosa et al., 2013). Fiber can improve insulin sensitivity (Tucker et al., 2015) and decrease circulating insulin-like growth factors (Barnard et al., 2006) that are related to BC risk. In addition, dietary fiber can inhibit colonic β-D-glucuronidase activity, which results in decreased deconjugation and reabsorption of oestrogen, thus decreasing plasma levels of oestrogen and increasing fecal excretion (Gerber, 1996, Rose et al., 1991). In this context, the study patients had higher intake of refined grain and lower consumption of vegetables. Due to low price, carbohydrate intake, especially refined grain, is high among Iranian people compared to other food resources. However, a study on Chinese women reported a null association between vegetable-soy dietary pattern and risk of BC , which may resulted from different effects of raw and cooked vegetable on BC risk (Cui et al., 2007). A prospective study identified that higher consumption of fruits and salad, as raw vegetables, was related with the reduced risk of BC (Baglietto et al., 2011). Given that some micronutrients of fruits and vegetables are destroyed by cooking, raw and cooked vegetables were separated in the present analysis. A meta-analysis study demonstrated that red meat increased the risk of BC by 10% in the highest compared to lowest categories of intake, the corresponding number was 8% for processed meat (Guo et al., 2015). While animal food pattern in premenopausal Japanese women (Kojima et al., 2017) and pork, processed meat, potatoes pattern in the NLCS cohort (Van den Brandt et al., 1990), had a protective effect against BC. Several possible mechanisms may identify the positive association between processed meat and BC risk. These mechanisms include the presence of pro-carcinogenic factors, such as salt, N-nitroso compounds, heme iron, heterocyclic amines (HCA), and polycyclic aromatic hydrocarbons (PAH), which are produced in high heat cooking of meat, in processed meat (Abid et al., 2014, Kazerouni et al., 2001, Knize et al., 1999). Human studies have reported the positive association between intake of Polycyclic Aromatic Hydrocarbons (PAHs) and Heterocyclic Amines (HCAs) and BC risk (Bonner et al., 2005, Zheng and Lee, 2009).
In addition, diet rich in refined grains, sugars, desserts and fatty dietary pattern particularly saturated fat may lead to increased blood glucose levels, body fats, and impaired hormonal homeostasis (Hirose et al., 2007, Sieri et al., 2014, Zhang et al., 2011). Higher insulin regulation creates cellular proliferation and encourages tumor growth, also hyperinsulinemia indirectly leads to increased free oestrogen concentration resulting from inhibition of sex-hormone-binding globulin production (Bradshaw et al., 2009).
In the present study, we could not observe a significant association between two major dietary patterns and BC risk among postmenopausal women. While, in premenopausal women healthy dietary patterns was inversely associated with BC risk and higher adherence to unhealthy dietary patterns was positively related to BC risk. In the current study premenopausal had higher refined grain than postmenopausal women (P < 0.01), which could result in hyperinsulinemia. Studies have illustrated that hyperinsulinemia can increase risk of BC via inhibiting apoptosis as well as by reducing production of sex hormone binding globulin, which can increase the level of free estrogen (Gupta et al., 2002, Kaaks and Lukanova, 2001). A significant association was observed in premenopausal women, where hormonal factors predominate, which is consistent with the present study. However, Hankinson et al. in a review study stated that the relation between circulating levels of estrogen and testosterone and BC risk was well established in postmenopausal women (Hankinson and Eliassen, 2007). Most of the studies examined the relationship between BC risk and dietary patterns in postmenopausal women rather than premenopausal and observed null or statistical significant association (Cui et al., 2007, Fung et al., 2005, Velie et al., 2005). The study in shanghai indicated that meat-sweet pattern (considering as unhealthy pattern) is positively associated with risk of BC in postmenopausal women (Cui et al., 2007). In Japanese women a westernized dietary pattern as an unhealthy pattern was associated with 29% increased risk of BC among postmenopausal subjects (Shin et al., 2016). A systematic review suggested that intake of Mediterranean dietary pattern (considering as a healthy pattern) was associated with reduced risk of postmenopausal BC (Coughlin et al., 2018).
The present study has some limitations. First, selection bias is an inherent limitation in case-control studies. Although the case-control studies suffered from selection bias in the present study, the authors tried to reduce the bias. Moreover, the variables in the models as the matching factors almost well adjusted the effect of them on the outcome. Second, using factor analysis contained subjective or arbitrary decisions that should be considered. Third, dietary assessment has measurement errors due to limitations of FFQ. It was compensated using a validated FFQ and elimination of over and under reports of energy intake. Fourth, there were limited data on hormone receptor status of the patients and could not evaluate the risk of BC with considering the hormone receptor subtypes due to small power.
This study had several strengths. The participants were recruited from a general hospital where subjects refer from throughout the country. Control group consisted of those healthy women referring to the same hospital of case group and were match for age and residential place. Matching for residential place was necessary to avoid biases due to difference in the referral pattern between the case and control groups. A wide spectrum of confounding variables was adjusted. Assessment of overall diet in regards to a dietary pattern method, instead of the effects of individual nutrients or specific food group could be the study strength. The large sample size allowed the authors to study the associations in pre-and postmenopausal women exclusively.
Conclusion
The present study is the first large case-control study to examine the association between dietary patterns and risk of BC using factor analysis in the Middle-Eastern region. The results demonstrated a decreased risk of BC for healthy dietary pattern characterized by high intake of fruits, fruit juices, cruciferous vegetables, yellow vegetables, tomatoes, green leafy vegetables, other vegetables, and garlic and increased risk of BC for unhealthy dietary pattern among overall and premenopausal subjects. However, no significant association was observed between BC risk and dietary patterns in postmenopausal women. The results can be used to for population awareness and cancer prevention interventions.
Acknowledgment
Thanks are owed to the study subjects, without whom the study would not be possible.
Authors’ contribution
Sasanfar B and Toorang F participated in the study design, analysis and drafted the initial version. Nemati S and Mohebbi E helped in data analysis. Sasanfar B implemented comments and suggestions of the co-authors. Zendehdel K and Azadbakht L contributed to the conception, design and data analysis. All authors reviewed the final version of the manuscript. Zendehdel K and Azadbakht L supervised the study.
Financial support
This study was financially supported by Cancer Research Center in Tehran University of Medical Science (no. 93-03-51-27113).
Conflict of Interest
None of the authors declared any conflict of interest.
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Hajizadeh B, Rashidkhani B, Rad AH, Moasheri SM & Saboori H 2010. Dietary patterns and risk of oesophageal squamous cell carcinoma: a case–control study. Public health nutrition. 13 (7): 1107-1112.
Hankinson SE & Eliassen AH 2007. Endogenous estrogen, testosterone and progesterone levels in relation to breast cancer risk. Journal of steroid biochemistry and molecular biology. 106 (1-5): 24-30.
Harris HR, Orsini N & Wolk A 2014. Vitamin C and survival among women with breast cancer: a meta-analysis. European journal of cancer. 50 (7): 1223-1231.
Harris HR, Willett WC, Vaidya RL & Michels KB 2017. An adolescent and early adulthood dietary pattern associated with inflammation and the incidence of breast cancer. Cancer research. 77 (5): 1179-1187.
Hawkes C 2006. Uneven dietary development: linking the policies and processes of globalization with the nutrition transition, obesity and diet-related chronic diseases. Globalization and health. 2 (1): 4.
Hiatt RA & Brody JG 2018. Environmental determinants of breast Cancer. Annual review of public health. 39: 113-133.
Hirose K, Matsuo K, Iwata H & Tajima K 2007. Dietary patterns and the risk of breast cancer in Japanese women. Cancer science. 98 (9): 1431-1438.
Inoue‐Choi M, Sinha R, Gierach GL & Ward MH 2016. Red and processed meat, nitrite, and heme iron intakes and postmenopausal breast cancer risk in the NIH‐AARP D iet and H ealth S tudy. International journal of cancer. 138 (7): 1609-1618.
International Agency for Research on Cancer (IARC) 2018. Globocan 2018 Latest global cancer data. World Health Organization/ International Agency for Research on Cancer
Kaaks R & Lukanova A 2001. Energy balance and cancer: the role of insulin and insulin-like growth factor-I. Proceedings of the nutrition society. 60 (1): 91-106.
Karimi Z, Jessri M, Houshiar-Rad A, Mirzaei H-R & Rashidkhani B 2014. Dietary patterns and breast cancer risk among women. Public health nutrition. 17 (5): 1098-1106.
Kazerouni N, Sinha R, Hsu C-H, Greenberg A & Rothman N 2001. Analysis of 200 food items for benzo [a] pyrene and estimation of its intake in an epidemiologic study. Food and chemical toxicology. 39 (5): 423-436.
Kim AE, et al. 2016. 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. 27 (4): 527-543.
Knize MG, Salmon CP, Pais P & Felton JS 1999. Food heating and the formation of heterocyclic aromatic amine and polycyclic aromatic hydrocarbon mutagens/carcinogens. In Impact of processing on food safety, pp. 179-193. Springer.
Kojima R, et al. 2017. Dietary patterns and breast cancer risk in a prospective Japanese study. Breast cancer. 24 (1): 152-160.
Lima FELd, Latorre MdRDd, Costa MJdC & Fisberg RM 2008. Diet and cancer in Northeast Brazil: evaluation of eating habits and food group consumption in relation to breast cancer. Cadernos de saude publica. 24 (4): 820-828.
Link LB, et al. 2013. Dietary patterns and breast cancer risk in the California Teachers Study cohort–. American journal of clinical nutrition. 98 (6): 1524-1532.
Männistö S, et al. 2005. Dietary patterns and breast cancer risk: results from three cohort studies in the DIETSCAN project. Cancer Causes & Control. 16 (6): 725-733.
Mazzocchi M, Brasili C & Sandri E 2008. Trends in dietary patterns and compliance with World Health Organization recommendations: a cross-country analysis. Public health nutrition. 11 (5): 535.
Michels KB, Mohllajee AP, Roset‐Bahmanyar E, Beehler GP & Moysich KB 2007. Diet and breast cancer. Cancer. 109 (S12): 2712-2749.
Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M & Azizi F 2010. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public health nutrition. 13 (5): 654-662.
Murtaugh MA, et al. 2008. Diet patterns and breast cancer risk in Hispanic and non-Hispanic white women: the Four-Corners Breast Cancer Study–. American journal of clinical nutrition. 87 (4): 978-984.
Pisani P, Bray F & Parkin DM 2002. Estimates of the world‐wide prevalence of cancer for 25 sites in the adult population. International journal of cancer. 97 (1): 72-81.
Rezazadeh A, Rashidkhani B & Omidvar N 2010. Association of major dietary patterns with socioeconomic and lifestyle factors of adult women living in Tehran, Iran. Nutrition. 26 (3): 337-341.
Ronco AL, et al. 2006. Food patterns and risk of breast cancer: a factor analysis study in Uruguay. International journal of cancer. 119 (7): 1672-1678.
Rose DP, Goldman M, Connolly JM & Strong LE 1991. High-fiber diet reduces serum estrogen concentrations in premenopausal women. American journal of clinical nutrition. 54 (3): 520-525.
Rouhollahi MR, et al. 2014. Situation analysis of the National Comprehensive Cancer Control Program (2013) in the IR of Iran; assessment and recommendations based on the IAEA imPACT mission. Archives of Iranian medicine (AIM). 17 (4): 222-231.
Seiler A, Chen MA, Brown RL & Fagundes CP 2018. Obesity, Dietary Factors, Nutrition, and Breast Cancer Risk. Current breast cancer reports. 10 (1): 14-27.
Shin S, et al. 2016. Dietary pattern and breast cancer risk in Japanese women: the Japan Public Health Center-based Prospective Study (JPHC Study). British journal of nutrition. 115 (10): 1769-1779.
Shuldiner J, Liu Y & Lofters A 2018. Incidence of breast and colorectal cancer among immigrants in Ontario, Canada: a retrospective cohort study from 2004-2014. BMC cancer. 18 (1): 537.
Sieri S, et al. 2014. Dietary fat intake and development of specific breast cancer subtypes. Journal of the national cancer institute. 106 (5): 1-6.
Sieri S, et al. 2004. Dietary patterns and risk of breast cancer in the ORDET cohort. Cancer epidemiology and prevention biomarkers. 13 (4): 567-572.
Tucker LA, Erickson A, LeCheminant JD & Bailey BW 2015. Dairy consumption and insulin resistance: the role of body fat, physical activity, and energy intake. Journal of diabetes research. 2015: 1-11.
Vahid F, et al. 2018. The association between the index of nutritional quality (INQ) and breast cancer and the evaluation of nutrient intake of breast cancer patients: A case-control study. Nutrition. 45: 11-16.
Van den Brandt PA, et al. 1990. A large-scale prospective cohort study on diet and cancer in The Netherlands. Journal of clinical epidemiology. 43 (3): 285-295.
Velie EM, et al. 2005. Empirically derived dietary patterns and risk of postmenopausal breast cancer in a large prospective cohort study–. American journal of clinical nutrition. 82 (6): 1308-1319.
World Health Organization 2012. Global physical activity questionnaire (GPAQ) analysis guide. Geneva.
Wu A & Bernstein L 1998. Breast Cancer among Asian Americans and Pacific Islanders. Asian American and Pacific Islander journal of health. 6 (2): 327-343.
Wu AH, Yu MC, Tseng C-C, Stanczyk FZ & Pike MC 2009. Dietary patterns and breast cancer risk in Asian American women–. American journal of clinical nutrition. 89 (4): 1145-1154.
Wu W, Kang S & Zhang D 2013. Association of vitamin B 6, vitamin B 12 and methionine with risk of breast cancer: a dose–response meta-analysis. British journal of cancer. 109 (7): 1926.
Youlden DR, Cramb SM, Yip CH & Baade PD 2014. Incidence and mortality of female breast cancer in the Asia-Pacific region. Cancer biology & medicine. 11 (2): 101.
Zendehdel K 2019. Cancer Statistics in IR Iran in 2018. Basic & clinical cancer research. 11 (1): 1-4.
Zhang C-X, et al. 2011. Dietary patterns and breast cancer risk among Chinese women. Cancer causes & control. 22 (1): 115-124.
Zheng W & Lee S-A 2009. Well-done meat intake, heterocyclic amine exposure, and cancer risk. Nutrition and cancer. 61 (4): 437-446.
The present study identified two major dietary patterns, named healthy and unhealthy dietary patterns. These extracted dietary patterns were similar to patterns found in a previous study in Iran that used the same FFQ (Hajizadeh et al., 2010, Rezazadeh et al., 2010). It was found that a healthy dietary pattern with high intake of fruits, fruit juices, cruciferous vegetables, yellow vegetables, tomato, green leafy vegetables, other vegetables, and garlic was associated with a decrease in the risk of BC. However, an unhealthy dietary pattern with high intake of processed meats, eggs, butter, refined grains, mayonnaise, hydrogenated fat, sugars, soft drinks, and pickles increased the risk of BC. These associations remained significant, even after stratifying by menopausal status, in premenopausal women.
The results found from previous studies regarding the association of BC risk and dietary pattern were inconsistent. Moreover, there are not several studies that can examine the association between BC risk and dietary patterns in Middle-Eastern countries. Most previous studies were conducted in developed countries and have derived different patterns (Adebamowo et al., 2005, Agurs-Collins et al., 2009, Baglietto et al., 2011, Fung et al., 2005, Harris et al., 2017, Männistö et al., 2005, Sieri et al., 2004, Velie et al., 2005). Only one case-control study with small sample size (100 cases-174 controls) assessed the association between dietary pattern and BC risk in Iran and found the relationship between a healthy and unhealthy dietary pattern with BC risk (Karimi et al., 2014). In agreement with the present results, some case-control studies have illustrated an inverse association between the risk of BC and healthy or similar to healthy dietary pattern (De Stefani et al., 2009, Hirose et al., 2007, Murtaugh et al., 2008, Ronco et al., 2006, Wu et al., 2009, Zhang et al., 2011). A positive association have been distinguished between unhealthy or western dietary patterns and BC risk (Cui et al., 2007, De Stefani et al., 2009, Edefonti et al., 2008, Murtaugh et al., 2008, Ronco et al., 2006, Wu et al., 2009). The results of a recent meta-analysis on case-control studies indicated that a higher score of healthy pattern was associated with a decreased BC risk and higher adherence to unhealthy pattern was associated with a 33% increase in risk of BC (Grosso et al., 2017). A number of studies explored the protective effects of fruits and vegetables against BC (Eliassen et al., 2012, Fritz et al., 2013, Gandini et al., 2000, Gong et al., 2014, Harris et al., 2014, Wu et al., 2013) that may have stemmed from high content of fiber and much amounts of antioxidants and certain photochemical which contribute to inhibition bio-activation of carcinogen, signaling of cell, cell cycle regulation, inflammation, and angiogenesis (Giacosa et al., 2013). Fiber can improve insulin sensitivity (Tucker et al., 2015) and decrease circulating insulin-like growth factors (Barnard et al., 2006) that are related to BC risk. In addition, dietary fiber can inhibit colonic β-D-glucuronidase activity, which results in decreased deconjugation and reabsorption of oestrogen, thus decreasing plasma levels of oestrogen and increasing fecal excretion (Gerber, 1996, Rose et al., 1991). In this context, the study patients had higher intake of refined grain and lower consumption of vegetables. Due to low price, carbohydrate intake, especially refined grain, is high among Iranian people compared to other food resources. However, a study on Chinese women reported a null association between vegetable-soy dietary pattern and risk of BC , which may resulted from different effects of raw and cooked vegetable on BC risk (Cui et al., 2007). A prospective study identified that higher consumption of fruits and salad, as raw vegetables, was related with the reduced risk of BC (Baglietto et al., 2011). Given that some micronutrients of fruits and vegetables are destroyed by cooking, raw and cooked vegetables were separated in the present analysis. A meta-analysis study demonstrated that red meat increased the risk of BC by 10% in the highest compared to lowest categories of intake, the corresponding number was 8% for processed meat (Guo et al., 2015). While animal food pattern in premenopausal Japanese women (Kojima et al., 2017) and pork, processed meat, potatoes pattern in the NLCS cohort (Van den Brandt et al., 1990), had a protective effect against BC. Several possible mechanisms may identify the positive association between processed meat and BC risk. These mechanisms include the presence of pro-carcinogenic factors, such as salt, N-nitroso compounds, heme iron, heterocyclic amines (HCA), and polycyclic aromatic hydrocarbons (PAH), which are produced in high heat cooking of meat, in processed meat (Abid et al., 2014, Kazerouni et al., 2001, Knize et al., 1999). Human studies have reported the positive association between intake of Polycyclic Aromatic Hydrocarbons (PAHs) and Heterocyclic Amines (HCAs) and BC risk (Bonner et al., 2005, Zheng and Lee, 2009).
In addition, diet rich in refined grains, sugars, desserts and fatty dietary pattern particularly saturated fat may lead to increased blood glucose levels, body fats, and impaired hormonal homeostasis (Hirose et al., 2007, Sieri et al., 2014, Zhang et al., 2011). Higher insulin regulation creates cellular proliferation and encourages tumor growth, also hyperinsulinemia indirectly leads to increased free oestrogen concentration resulting from inhibition of sex-hormone-binding globulin production (Bradshaw et al., 2009).
In the present study, we could not observe a significant association between two major dietary patterns and BC risk among postmenopausal women. While, in premenopausal women healthy dietary patterns was inversely associated with BC risk and higher adherence to unhealthy dietary patterns was positively related to BC risk. In the current study premenopausal had higher refined grain than postmenopausal women (P < 0.01), which could result in hyperinsulinemia. Studies have illustrated that hyperinsulinemia can increase risk of BC via inhibiting apoptosis as well as by reducing production of sex hormone binding globulin, which can increase the level of free estrogen (Gupta et al., 2002, Kaaks and Lukanova, 2001). A significant association was observed in premenopausal women, where hormonal factors predominate, which is consistent with the present study. However, Hankinson et al. in a review study stated that the relation between circulating levels of estrogen and testosterone and BC risk was well established in postmenopausal women (Hankinson and Eliassen, 2007). Most of the studies examined the relationship between BC risk and dietary patterns in postmenopausal women rather than premenopausal and observed null or statistical significant association (Cui et al., 2007, Fung et al., 2005, Velie et al., 2005). The study in shanghai indicated that meat-sweet pattern (considering as unhealthy pattern) is positively associated with risk of BC in postmenopausal women (Cui et al., 2007). In Japanese women a westernized dietary pattern as an unhealthy pattern was associated with 29% increased risk of BC among postmenopausal subjects (Shin et al., 2016). A systematic review suggested that intake of Mediterranean dietary pattern (considering as a healthy pattern) was associated with reduced risk of postmenopausal BC (Coughlin et al., 2018).
The present study has some limitations. First, selection bias is an inherent limitation in case-control studies. Although the case-control studies suffered from selection bias in the present study, the authors tried to reduce the bias. Moreover, the variables in the models as the matching factors almost well adjusted the effect of them on the outcome. Second, using factor analysis contained subjective or arbitrary decisions that should be considered. Third, dietary assessment has measurement errors due to limitations of FFQ. It was compensated using a validated FFQ and elimination of over and under reports of energy intake. Fourth, there were limited data on hormone receptor status of the patients and could not evaluate the risk of BC with considering the hormone receptor subtypes due to small power.
This study had several strengths. The participants were recruited from a general hospital where subjects refer from throughout the country. Control group consisted of those healthy women referring to the same hospital of case group and were match for age and residential place. Matching for residential place was necessary to avoid biases due to difference in the referral pattern between the case and control groups. A wide spectrum of confounding variables was adjusted. Assessment of overall diet in regards to a dietary pattern method, instead of the effects of individual nutrients or specific food group could be the study strength. The large sample size allowed the authors to study the associations in pre-and postmenopausal women exclusively.
Conclusion
The present study is the first large case-control study to examine the association between dietary patterns and risk of BC using factor analysis in the Middle-Eastern region. The results demonstrated a decreased risk of BC for healthy dietary pattern characterized by high intake of fruits, fruit juices, cruciferous vegetables, yellow vegetables, tomatoes, green leafy vegetables, other vegetables, and garlic and increased risk of BC for unhealthy dietary pattern among overall and premenopausal subjects. However, no significant association was observed between BC risk and dietary patterns in postmenopausal women. The results can be used to for population awareness and cancer prevention interventions.
Acknowledgment
Thanks are owed to the study subjects, without whom the study would not be possible.
Authors’ contribution
Sasanfar B and Toorang F participated in the study design, analysis and drafted the initial version. Nemati S and Mohebbi E helped in data analysis. Sasanfar B implemented comments and suggestions of the co-authors. Zendehdel K and Azadbakht L contributed to the conception, design and data analysis. All authors reviewed the final version of the manuscript. Zendehdel K and Azadbakht L supervised the study.
Financial support
This study was financially supported by Cancer Research Center in Tehran University of Medical Science (no. 93-03-51-27113).
Conflict of Interest
None of the authors declared any conflict of interest.
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Kim AE, et al. 2016. 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. 27 (4): 527-543.
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Lima FELd, Latorre MdRDd, Costa MJdC & Fisberg RM 2008. Diet and cancer in Northeast Brazil: evaluation of eating habits and food group consumption in relation to breast cancer. Cadernos de saude publica. 24 (4): 820-828.
Link LB, et al. 2013. Dietary patterns and breast cancer risk in the California Teachers Study cohort–. American journal of clinical nutrition. 98 (6): 1524-1532.
Männistö S, et al. 2005. Dietary patterns and breast cancer risk: results from three cohort studies in the DIETSCAN project. Cancer Causes & Control. 16 (6): 725-733.
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Michels KB, Mohllajee AP, Roset‐Bahmanyar E, Beehler GP & Moysich KB 2007. Diet and breast cancer. Cancer. 109 (S12): 2712-2749.
Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M & Azizi F 2010. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public health nutrition. 13 (5): 654-662.
Murtaugh MA, et al. 2008. Diet patterns and breast cancer risk in Hispanic and non-Hispanic white women: the Four-Corners Breast Cancer Study–. American journal of clinical nutrition. 87 (4): 978-984.
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Rezazadeh A, Rashidkhani B & Omidvar N 2010. Association of major dietary patterns with socioeconomic and lifestyle factors of adult women living in Tehran, Iran. Nutrition. 26 (3): 337-341.
Ronco AL, et al. 2006. Food patterns and risk of breast cancer: a factor analysis study in Uruguay. International journal of cancer. 119 (7): 1672-1678.
Rose DP, Goldman M, Connolly JM & Strong LE 1991. High-fiber diet reduces serum estrogen concentrations in premenopausal women. American journal of clinical nutrition. 54 (3): 520-525.
Rouhollahi MR, et al. 2014. Situation analysis of the National Comprehensive Cancer Control Program (2013) in the IR of Iran; assessment and recommendations based on the IAEA imPACT mission. Archives of Iranian medicine (AIM). 17 (4): 222-231.
Seiler A, Chen MA, Brown RL & Fagundes CP 2018. Obesity, Dietary Factors, Nutrition, and Breast Cancer Risk. Current breast cancer reports. 10 (1): 14-27.
Shin S, et al. 2016. Dietary pattern and breast cancer risk in Japanese women: the Japan Public Health Center-based Prospective Study (JPHC Study). British journal of nutrition. 115 (10): 1769-1779.
Shuldiner J, Liu Y & Lofters A 2018. Incidence of breast and colorectal cancer among immigrants in Ontario, Canada: a retrospective cohort study from 2004-2014. BMC cancer. 18 (1): 537.
Sieri S, et al. 2014. Dietary fat intake and development of specific breast cancer subtypes. Journal of the national cancer institute. 106 (5): 1-6.
Sieri S, et al. 2004. Dietary patterns and risk of breast cancer in the ORDET cohort. Cancer epidemiology and prevention biomarkers. 13 (4): 567-572.
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Vahid F, et al. 2018. The association between the index of nutritional quality (INQ) and breast cancer and the evaluation of nutrient intake of breast cancer patients: A case-control study. Nutrition. 45: 11-16.
Van den Brandt PA, et al. 1990. A large-scale prospective cohort study on diet and cancer in The Netherlands. Journal of clinical epidemiology. 43 (3): 285-295.
Velie EM, et al. 2005. Empirically derived dietary patterns and risk of postmenopausal breast cancer in a large prospective cohort study–. American journal of clinical nutrition. 82 (6): 1308-1319.
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| Appendix 1. Food grouping used in the dietary pattern analyses | |
| Food items | Food groups |
| Sausage, hamburger | Processed meats |
| Beef, lamb | Red meats |
| Beef liver, kidney, heart, brain | Organ meats |
| Canned tuna fish, other fish | Fish |
| Chicken with or without skin | Poultry |
| Eggs | Eggs |
| Butter | Butter |
| Margarine | Margarine |
| Skim or low fat milk, low fat yoghurt | Low-fat dairy products |
| High-fat milk, whole milk, chocolate milk, cream, high-fat yoghurt, cream yoghurt, cream cheese, other cheese, ice cream, kashk | High-fat dairy products |
| Tea | Tea |
| Coffee | Coffee |
| Cantaloupe, melons, watermelon, pears, palms, cherries, apples, peaches, nectarine, grapes, kiwi, grapefruit, oranges, persimmons, tangerine, pomegranates, bananas, lemons, strawberries, fresh figs and dates | Fruits |
| Grapefruit juice, orange juice, apple juice, cantaloupe juice, lemon juice, pineapples, mulberry | Fruit juices |
| Cabbage, cauliflower, Brussels sprouts, kale | Cruciferous vegetables |
| Carrots, squash | Yellow vegetables |
| Tomatoes, tomato sauce | Tomatoes |
| Lettuce, spinach, mixed vegetables | Green leafy vegetables |
| Cucumber, eggplant, celery, green peas, green beans, green pepper, zucchini, bell pepper, turnip, corn, onion | Other vegetables |
| Fried vegetables | Fried vegetables |
| Beans, peas, lima beans, broad beans, lentils, | Legumes |
| Soy | Soy |
| Garlic, aged garlic | Garlic |
| Potatoes | Potatoes |
| French fries | French fries |
| Dark breads (Sangak, Barbari, Taftoon), barley bread, oat bread, popcorn, bulgur, whole toasted bread | Whole grains |
| White breads (lavash, baguettes), noodles, pasta, rice, white flour, biscuits, milled barley | Refined grains |
| Pizza | Pizza |
| Potato chips, crackers, popcorn | Snacks |
| Peanuts, almond, pistachios, hazelnuts, walnuts, roasted seeds | Nuts |
| Mayonnaise | Mayonnaise |
| Dried figs, dried dates, dried mulberries, dried apricots, dried peaches, raisins | Dried fruit |
| Canned pineapple, other canned fruits | Canned fruits |
| Olives, olive oils | Olives |
| Chocolates, cookies, cakes, confections, jam, sohan (a traditional Persian confectionery made of flour, egg, sugar, nuts and vegetable oil), creme caramel, halva | Sweets and desserts |
| Hydrogenated fats | Hydrogenated fats |
| Vegetables oils (except for olive oil) | Vegetables oils |
| Animal fat | Animal fat |
| Sugars, candies, jam, gaz (an Iranian confectionery made of sugar, nuts, and tamarisk) | Sugars |
| Soft drinks | Soft drinks |
| Doogh | Yogurt drink |
| Black pepper | Spice |
| Pickles | Pickles |
| Appendix 2. Factor-loading matrix for major dietary patterns | |||
| Dietary patterns | Food groups | ||
| Unhealthy | Healthy | ||
| 0.406 | - | Processed meats | |
| - | - | Red meats | |
| - | - | Organ meats | |
| - | - | Fish | |
| - | - | Poultry | |
| 0.323 | - | Eggs | |
| 0.330 | - | Butter | |
| - | - | Margarine | |
| - | - | Low-fat dairy products | |
| - | - | High-fat dairy products | |
| - | - | Tea | |
| - | - | Coffee | |
| - | 0.610 | Fruits | |
| - | 0.361 | Fruit juices | |
| - | 0.332 | Cruciferous vegetables | |
| - | 0.311 | Yellow vegetables | |
| - | 0.461 | Tomatoes | |
| - | 0.520 | Green leafy vegetables | |
| - | 0.586 | Other vegetables | |
| - | - | Fried vegetables | |
| - | - | Legumes | |
| - | - | Soy | |
| - | 0.372 | Garlic | |
| - | - | Potatoes | |
| - | - | French fries | |
| - | - | Whole grains | |
| 0.384 | - | Refined grains | |
| - | - | Pizza | |
| - | - | Snacks | |
| - | - | Nuts | |
| 0.403 | - | Mayonnaise | |
| - | - | Dried fruit | |
| - | - | Canned fruits | |
| - | - | Olives | |
| - | - | Sweets and desserts | |
| 0.309 | - | Hydrogenated fats | |
| - | - | Vegetables oils | |
| - | - | Animal fat | |
| 0.407 | - | Sugars | |
| 0.433 | - | Soft drinks | |
| - | - | Yogurt drink | |
| - | - | Spice | |
| 0.349 | - | Pickles | |
| 1.3 | 2.7 | Eigenvalue | |
| 71.9 | 23.9 | 47.9 | Variance explained by each factor |
| Loading <0.30 were omitted for simplicity | |||
Type of article: orginal article |
Subject:
public specific
Received: 2021/10/29 | Published: 2022/08/19 | ePublished: 2022/08/19
Received: 2021/10/29 | Published: 2022/08/19 | ePublished: 2022/08/19
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