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The Intake of Some Nutrients is Associated with the Risk of Breast Cancer: Results from Jordanian Case-Control Study

Reema F. Tayyem1, Reema I. Mahmoud2, Lina Salah Marei3

1Department of Nutrition and Food Technology, Faculty of Agriculture, The University of Jordan. Amman 11942 Jordan.
2Department of Nutrition and Food Technology, Faculty of Agriculture, The University of Jordan.
3Medical Oncologist, King Hussein Cancer Center, Amman, Jordan

Corresponding Author Email: r.tayyem@ju.edu.jo

DOI : https://dx.doi.org/10.12944/CRNFSJ.8.1.02

Article Publishing History

Received: 04/06/2019

Accepted: 14/12/2019

Published Online: 07/01/2020

Plagiarism Check: Yes

Reviewed by: Dr. Eshrat Halim Canada

Second Review by: Dr. Hadi Zare Marzouni Iran

Final Approval by: Prof. Min-Hsiung Pan

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Abstract:

Breast cancer (BC) is the most commonly diagnosed cancer and is considered the leading cause of cancer deaths in women globally. The aim of this study is to evaluate the relationship between macro- and micronutrient intake and BC risk among Jordanian women. A case-control design was used in this study, and BC patients were recruited from the main two hospitals provide cancer therapy in Jordan. Four hundred women aged 20-65 years of age were enrolled in the study. For the cases, 200 recently diagnosed BC women were selected from the two hospitals and matched in age and marital status to 200 BC-free women. Dietary data were collected through face-to-face interview using a validated food frequency questionnaire between October 2016 and September 2017. To calculate odds ratio (OR), logistic regression was used; while for p-trend the linear regression was performed. The study results demonstrated that increasing the intake of total energy and percentage of fat was significantly and positively associated with BC (p-= 0.001). The risk of BC increased significantly and positively as carbohydrate, sugar, fat, saturated fat and polyunsaturated and monounsaturated fatty acids intake increased. A significant trend in BC risk was found for cholesterol (p-= 0.005). The ORs for higher intakes of vitamins E, B1, B2, and B3, folate and phosphorus showed a significant association with the risk of BC (p-trend =0.001). A significant inverse effect was detected between iron intake and BC risk (p-=0.001). The study findings resulted in insight of the associations between the total energy intake and some macro/micronutrients intake can be an increasing risk of BC.

Keywords:

Macronutrient; Micronutrient; Breast Cancer; Case-Control Study

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Tayyem R. F, Mahmoud R. I, Marei L. S. The Intake of Some Nutrients is Associated with the Risk of Breast Cancer: Results from Jordanian Case-Control Study. Curr Res Nutr Food Sci 2020; 8(1). doi : http://dx.doi.org/10.12944/CRNFSJ.8.1.02


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Tayyem R. F, Mahmoud R. I, Marei L. S. The Intake of Some Nutrients is Associated with the Risk of Breast Cancer: Results from Jordanian Case-Control Study. Curr Res Nutr Food Sci 2020; 8(1). https://bit.ly/2QvhOEN


Introduction

Cancer is a primary cause of death in both developed and developing countries. The implication of all types of cancer is expected to grow globally owing to the growth and aging of the population1. About 18.1 million new cancer cases and 9.6 million deaths occurred in 2018 worldwide.2 Breast cancer (BC) is the leading cause of cancer deaths in women globally.1,2 Breast cancer alone is responsible for 24.5% of all cancer deaths among Jordanian women.3 In the year 2014 there, were 1187 newly diagnosed cases of BC in both sexes accounting for 20.8% of all newly diagnosed cancer cases.3

Several factors for preventing or developing BC have been reported in studies, including age, lactation, hormone levels, family history of BC, use of hormones or hormone replacement therapy and breast density,4 dietary patterns,5,6 physical activity (PA),7 tobacco smoking,8 reproductive history, weight status9,10 and macro- and micronutrients intake.11

Preventing cancer is more important than treating or inhibiting its progression. Prevention of such health problem means reducing the suffering and the pain of patients with cancer and their families. Scientific evidence showed that prevention of cancer could be achieved by reducing intake of alcohol, protein and fat and increasing intake of fiber and vitamin D from different food sources.10,11 The mechanisms of dietary risk factors are not clearly stated.10,11 Although, large body of scientific researches admitted the association between BC and diet,5,6,11 cultural differences between countries showed a main role in BC risk attenuation or enhancement in spite of the world globalization.11

In Jordan, there is a gap in the scientific knowledge that shows the dietary risk factors for developing BC. Only one study by Al-Qadire et al (2018) was conducted to identify lifestyle-related risk factors for breast cancer among Jordanian women.12 The authors revealed that physical activity and fruit and vegetable intake were found to be associated with reduced breast cancer risk. However, calcium intake (>3 times a week) was associated with increased risk of breast cancer.12 From all the nutrients only association between calcium intake and risk of BC was investigated.  Therefore, this study aimed to explore the association between energy and macro- and micronutrients intake and the BC risk among Jordanian women.

Materials and Methods

Study Design and Sample Recruitment

A case-control design was used to determine macro- and micronutrient intake as a risk factor linked to BC among Jordanian women. Two hundred women who were lately (last three months) diagnosed with BC were recruited in the study. All BC patients were taken from King Hussein Cancer Center (KHCC) and Al-Basheer Hospital. These two hospitals are the main facilities in Jordan that offer cancer therapy. The control group was included from the community (the employees and visitors of KHCC and Al-Basheer Hospital, patients’ accompanying persons as well as women who visited KHCC to perform mammogram). To become eligible, the women in the control group underwent either a clinical examination or mammogram to ensure that they were BC free. The researcher asked all the participants in controls group if they had undergone a prior clinical examination or mammography during the last year before participating in the study. The ratio was 1:1 cases to control and they were matched by age and marital status.

For cases, the inclusion criteria were recent diagnosed with BC (last three 3 months), age 20 -65 years, being Jordanian and able to communicate verbally. For both of cases and controls who were critically or terminally ill, hospitalized, unable to talk verbally, complaining from any other cancer and chronic disease that requires following specialized diet and being pregnant or lactating were excluded from the study.

Data were collected in the outpatient departments in both hospitals. Each hospital provided a private room where the interviews took place. The study was conducted according to the ethical standards of the responsible committee on human experimentation and in compliance to the Helsinki declaration of 1975, as revised in 1983. The proposal was approved by the Institutional Review Board of both hospitals. Written consent form was completed and obtained before starting data collection. Patients’ information was kept confidential. Only the investigator knew patients’ names and gave them number (ID). All used questionnaires were labeled with patient ID.

Data Collection

In-person interview was conducted to collect the requested data and the questionnaires were completed and finalized by a trained researcher. Measurement of weight and height was taken and body mass index (BMI) was calculated by dividing weight in kilograms on height in meters square.

Personal Information Sheet

This sheet contained questions related to some socioeconomic status including age, family income/month, marital status, education, employment, smoking status, and family members diagnosed with cancer.

Dietary Assessment

Nutrients intake was assessed using Arabic validated food frequency questionnaire (FFQ).13 This FFQ included 109 questions on food and beverages. Response categories provided in the FFQ were “<1/month, 2-3/month, 1-2/week, 3-4/week, 5-6/week, 1/day, 2-3/day, 4-5/day, or 6/day”. Food lists in the modified FFQ questions were classified based on types of foods: 21 items of vegetables; 16 items meat such as red meat (lamb and beef), chicken, fish, cold meat and others; 21 items of fruits and juices; 9 items of milk and dairy products; 8 items of cereals; 4 items beans; 4 items of soups and sauces; 5 items drinks; 9 items of snacks and sweets; 14 items of herbs and spices. After completing the FFQ, the selected frequency category was converted to a weekly intake. For calculating the nutrient intake, software (ESHA Food Processor SQL version 10.1.1; ESHA, Salem, OR, USA) that calculates macro- and micronutrients intake based on the foods intake was used. Standardized food prototypes (Nasco Company, USA) and standard cups and spoons were used to help participants in estimating the portion size of the main foods (cereals, fruits and vegetable, oils, meats and dairy products) included in the FFQ precisely.

Physical Activity Levels Assessment

The physical activity recall (PAR) questionnaire was completed to determine the physical activity level (PAL) among participants on weekly basis as described by Sallis et al. (1985).14 Physical activity recall was measured using a metabolic equivalent (MET) score and the total METs was estimated. In PAR calculations, it is necessary to take into consideration the frequency, intensity, time, and type of the physical activity. The 7-day PAR is a structured interview that depends on participant’s recall of time spent engaging in physical activity (PA) over a seven day period. Different levels of PA such as aerobic exercise, gardening, work-related activities, recreation, walking, and leisure-time activities were covered in PAR.14 We measured PA using a metabolic equivalent (MET) score.14 The number of hours spent in sleep and different activity levels are obtained and converted into Kilocalories. Time spent in sleeping (1 MET), light (1.5 METs), moderate (4 METs), hard (6 METs), and very hard (10 METs) activities for the past 7 days are multiplied by their respective MET values and then summed. An estimate of total kilocalories of energy expenditure per day was calculated.14

Statistical Analysis

To find the differences between continuous variables of cases and controls, T-test was used. Data are presented as Mean±SD for the continuous variables and percentages for categorical ones. To calculate odds ratio OR and CI, logistic regression was used, while for p-trend linear regression was performed. Age (continuous), marital status (categorical), total energy intake (continuous), BMI (continuous), physical activity level (continuous), education level (categorical), occupation (categorical), lactation (categorical) health problem (categorical), number of pregnancy (categorical), hormonal replacement therapy (categorical) and family history (categorical) for all participants were considered as possible confounding factors.5 The significance level was set at p≤0.05. All statistical analyses were done using SPSS version 22.0 (IBM SPSS Statistics for Windows, IBM Corporation).

Results and Discussion

This study aimed to evaluate the association between energy and macro- and micronutrients intake and the risk of BC. Table 1 shows participants’ socio-demographic, anthropometric measurements, health and lifestyle characteristics.15 Average age for cases was 48.9±0.63 years and 47.5±0.59 years for control. BMI, education level, employment status, smoking and family history of BC, were all significantly different as BC cases compared to the controls.15

Table 1: Characteristics of the Study Participants.15

Variables Treatments p-value
Cases (n=200) Controls (n=200)
Mean ± SEM
Age (y) 48.9 ± 0.63 47.5±0.59 0.106
Height (cm) 159.3±0.42 161.7±0.41 0.001
Weight (Kg) 75.7±1.05 73.2±0.95 0.080
BMI (kg/m²) 29.8±0.39 27.9±0.36 0.001
Number of pregnancy 4.6±0.23 3.40.20 0.001
Number of miss carriage 1.1±0.11 0.90±0.09 0.226
Duration of lactation (months) 8.2±0.54 7.7±0.55 0.599
Physical activity (METs) 1425.4±133.3 3464.7±1205.6 0.019
N (%)
Marital Status
Married 154 (77.0) 156(78.0) 0.789
Single 26 (13.0) 29(14.5)
Divorce 8 (4.0) 5(2.50)
Widow 12 (6.0) 10(5.0)
Education Level
Illiterate 12(6.0) 5(2.5) 0.001
School 52(26.0) 12(6.0)
High school 65(32.5) 46(23.0)
Diploma 47(23.5) 65(32.5)
Bachelor 14(7.0) 55(27.5)
Master degree 7(3.5) 12(6.0)
Doctorate degree 3(1.5) 5(2.5)
Work Status
Yes 53(26.5) 89(44.5) 0.001
No 147(73.5) 111(55.5)
BMI categories
Under weight 0(0.0) 1(0.5) 0.013
Normal weight 33(16.5) 58(29.0)
Overweight 91(45.5) 70(35.0)
Obese 76(38.0) 71(35.0)
Smoking
Yes 40(20.0) 14(7.0) 0.001
No 160(80.0) 186(93.0)
Family members diagnosed with cancer
Yes 109(54.5) 73(36.5) 0.001
No 91(45.5) 127(63.5)

Significance is at p ≤ 0.05.

Table 2: Macronutrients Intake Per Day for the Study Participants

Nutrients Cases (n=200)Mean ± SEM Controls  (n=200)Mean ± SEM p-Value
Energy (kcal) 2410.6 ± 52.9 2015.7 ± 49.5 0.001
Energy from Fat (kcal) 901.0 ± 25.2 704.3 ± 21.3 0.001
Energy from saturated fat (kcal) 245.3±8.6 207.1±7.1 0.001
Energy from trans-fat (kcal) 6.2±0.86 2.4±0.41 0.001
% Carbohydrate 56.9±1.4 62.2±2.2 0.045
% Protein 14.8±0.38 14.1±0.32 0.001
% Fat 36.9±0.46 34.5±0.51 0.001
% Saturated Fat 9.9±0.21 10.1±0.21 0.509
% Trans-fat 0.24±0.03 0.11±0.02 0.001
Protein (g) 80.7±1.1 71.5±14.9 0.540
Carbohydrate (g) 311.8±3.2 269.5±2.9 0.001
Starch (g) 3.4±0.20 2.6±0.20 0.031
Fiber (g) 22.5±0.5 22.2±0.50 0.720
Soluble Fiber (g) 3.4±0.12 3.5±0.13 0.750
Insoluble Fiber (g) 7.3±0.21 7.9±0.26 0.710
Sugar (g) 110.9±2.5 89.9±2.49 0.001
Fat (g) 100.5±1.23 78.5±1.16 0.001
Saturated Fat (g) 27.2±0.59 23.1±0.46 0.001
Monounsaturated Fat (g) 32.0±0.56 23.4±0.55 0.001
Polyunsaturated Fat (g) 16.7±0.38 12.08±0.29 0.001
Trans-Fat (g) 0.65±0.08 0.27±0.05 0.001
Cholesterol (mg) 245.8±9.9 198.1±8.56 0.001

Significance is at p≤ 0.05.

Table 3: Micronutrients Intakes per Day of the Study Participants

Nutrients Cases (n=200)Mean ± SEM Controls  (n=200)Mean ± SEM p-Value
Retinol (RE) 302.2 ± 18.4 298.1±34.1 0.920
β-carotene (µg) 5443.4±256.5 5810.7±603.2 0.570
Vitamin B1 (mg) 1.9±0.04 1.6±0.04 0.001
Vitamin B2 (mg) 2.2±0.04 1.9±0.04 0.001
Vitamin B3 (mg) 20.8±0.31 18.5±0.26 0.001
Vitamin B6 (mg) 1.3±0.04 1.3±0.07 0.690
Vitamin B12 (µg) 2.3±0.13 2.4±0.18 0.580
Vitamin C (mg) 121.9±4.04 153.7±28.7 0.270
Vitamin D (µg) 1.9±0.16 1.5±0.1 0.090
Vitamin E (mg) 9.3±0.20 7.4±0.26 0.001
Folate (µg)§ 197.1±5.4 159.9±5.8 0.001
Vitamin K (µg) 254.7±12.2 290.4±46.9 0.460
Calcium (mg) 662.1±21.01 638.7±15.8 0.370
Copper (mg) 1.4±0.04 1.3±0.03 0.180
Iodine (µg) 68.98±3.78 60.1±2.45 0.051
Iron (mg) 11.17±0.18 14.27±0.34 0.001
Phosphorus (mg) 972.5±15.5 862.9±12.3 0.001
Potassium (mg) 2920.5±44.6 2777.1±83.8 0.130
Sodium (mg) 2715.5±80.9 3609.3±994.1 0.370

Significance is at p≤ 0.05.

‡ Vitamin E as α-Tocopherol

§ Folate as dietary folate equivalents

The mean daily intakes of total energy, macronutrients, and micronutrients are illustrated in Table 2 and 3. The BC group reported significant higher intakes of total energy, energy from fat, saturated fat and trans-fats, percentage of protein, fat, trans-fats and carbohydrate (CHO), amount of total CHO, sugars, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated  fatty acids (PUFA), trans-fat and cholesterol (p<0.05) compared to the control group. In addition, the BC group in table 3 showed significant higher intakes of vitamins B1, B2 and B3 folate, vitamin E and phosphorus (p<0.05) when compared to the control group while the control group had higher iron intakes when compared to the BC group (p<0.05). Similar to our study finding of the association between energy intake and BC risk, García-Arenzana et al. (2014) demonstrated that high energy intake can increase the risk of having BC.16 Nevertheless, mechanisms in which energy intake can promote the development of BC may include different theories; alterations in the production of ovarian steroid hormones,17 changes in the availability of insulin growth factor-1 (IGF-1) and increasing cell proliferation18 and increasing tissue susceptibility to damaging carcinogens by an increasing DNA replication that reduces the rate of apoptosis.19

In the current study, CHO and sugar intake is significantly associated with the risk of BC. Similarly, Romieu et al. (2011) revealed that CHO intake was positively associated with an increased risk of developing some types of classified BC among postmenopausal women.20 CHO may affect BC risk by elevated circulating insulin concentrations either directly, by stimulating insulin receptors in breast tissue, or indirectly, through the mitogenic effects of insulin-like growth factor-I (IGF-I).21 However, a case-control study performed by Sulaiman et al. (2014) showed that dietary CHO intake has no association with BC risk, but a significant increased risk of BC among premenopausal and postmenopausal women was observed with increased sugar intake.22 Similar findings were also documented by Wong, (2015) who stated that high sugar intake precisely sugar comes from sweetened beverages may increase the risk of BC.23

The present study cases reported a consumption of protein in grams higher than the controls but the difference was insignificant. High protein intake may influence the risk of BC by elevating IGF-1 level which plays important roles in tissue growth and tumor progression. However, foods that are major sources of protein differ widely in their nutrient profiles and may have different effects on BC risk.24 Farvid et al. (2016) documented that higher intake of total red meat was associated with an increased risk of BC.24 However, higher consumption of other protein sources such as legumes, fish, poultry, eggs and nuts were not related to BC overall. The authors also suggested that substituting red meat with a combination of legumes, fish, poultry and nuts may lessen the risk of BC, but according to hormonal status.24

Table 4: Association between energy and energy sources and BC risk among participants.

Nutrients †Q1 Q2 Q3 Q4
Energy (kcal)
Number of case 31 54 49 66
Number of control 69 46 50 34
*OR (95%CI) 1 1.91 (0.86-4.21) 2.46 (1.11-5.45) 4.86 (2.13-11.12)
P-trend 0.001
Energy from fat
Number of case 35 45 53 67
Number of control 65 55 46 33
*OR (95%CI) 1 1.21 (0.55-2.68) 1.83 (0.81-4.13) 5.91 (2.56-13.64)
P-trend 0.001
Energy from Saturated fat
Number of case 48 42 50 60
Number of control 52 58 49 40
*OR (95%CI) 1 0.67 (0.30-1.49) 1.22 (0.58-2.59) 2.19 (1.01-4.76)
P-trend 0.220
Energy from trans fat
Number of case 47 55 26 72
Number of control 53 59 59 28
*OR (95%CI) 1 0.89 (0.43-1.84) 0.43 (0.19-0.97) 2.63 (1.18-5.84)
P-trend 0.001
% of Carbohydrates
Number of case 50 52 54 44
Number of control 50 47 46 56
*OR (95%CI) 1 0.78 (0.37-1.65) 0.88 (0.41-1.89) 0.69 (0.32-1.49)
P-trend 0.384
% of Fat
Number of case 38 46 54 62
Number of control 62 53 46 38
*OR (95%CI) 1 1.92 (0.90-4.11) 3.13 (1.45-6.77) 2.39 (1.08-5.26)
P-trend 0.004
% of Trans-Fat
Number of case 48 43 39 70
Number of control 52 55 62 30
*OR (95%CI) 1 0.70 (0.33-1.49) 0.57 (0.27-1.20) 2.01 (0.92-4.37)
P-trend 0.001

* Adjusted for Energy, Age, Martial statues, education, work, income, physical activity, smoking, family history, health problem, number of pregnancy, lactation, hormonal replacement therapy.

Significant at p≤ 0.05

Table 4 shows the OR and 95%CI for energy and energy sources, after adjusting for potential confounders. The study results revealed that increasing the intake of total energy and percentage of fat in third quartiles and forth quartiles were significantly associated with BC with a p-trend= 0.001. A significant direct trend (p-trend= 0.001) in BC risk was detected in our study for energy from fat (especially in forth quartiles with OR 5.91(95%CI: 2.56-13.64)) and energy from trans-fat (especially in forth quartiles with OR 2.63 (95%CI: 1.18-5.84)). Additionally, percentage of trans-fats showed a significant trends (p<0.05) in BC risk.

Table 5: Association of macronutrients with BC risk among participants.

Nutrients †Q1 Q2 Q3 Q4
Carbohydrate (g)
Number of case 22 40 53 85
Number of control 78 59 47 15
*OR (95%CI) 1 3.18 (1.31-7.76) 5.43 (2.30-12.80) 34.28 (13.06-89.95)
P-trend 0.001
Starch (g)
Number of case 41 49 52 58
Number of control 58 52 48 41
*OR (95%CI) 1 1.94 (0.92-4.11) 2.09 (0.97-4.52) 2.07 (0.98-4.36)
P-trend 0.033
Sugar (g)
Number of case 29 38 66 67
Number of control 71 61 34 33
*OR (95%CI) 1 3.35 (1.44-7.78) 8.03 (3.51-18.36) 6.80 (2.92-15.80)
P-trend 0.001
Fat (g)
Number of case 19 31 60 90
Number of control 81 68 40 10
*OR (95%CI) 1 1.99 (0.85-4.67) 8.13 (3.62-18.24) 34.11 (12.18-95.53)
P-trend 0.001
Saturated Fat (g)
Number of case 30 50 52 68
Number of control 69 50 48 32
*OR (95%CI) 1 2.67 (1.35-5.27) 3.39 (1.66-6.92) 5.32 (2.61-10.85)
P-trend 0.001
Monounsaturated Fat (g)
Number of case 17 34 65 84
Number of control 83 66 34 16
*OR (95%CI) 1 2.18 (1.00-4.75) 10.28 (4.71-22.46) 28.93 (11.78-71.05)
P-trend 0.001
Polyunsaturated Fat (g)
Number of case 22 37 56 85
Number of control 78 62 44 15
*OR (95%CI) 1 2.05 (0.99-4.25) 4.10 (1.98-8.49) 20.85 (9.03-48.16)
P-trend 0.001
Cholesterol (mg)
Number of case 41 41 52 66
Number of control 58 59 48 34
*OR (95%CI) 1 0.89 (0.46-1.73) 1.39 (0.71-2.71) 1.76 (0.88-3.51)
P-trend 0.005

Significant at p<0.05

* Adjusted for Energy, Age, Martial statues, education, work, income, physical activity, smoking, family history, health problem, number of pregnancy, lactation, hormonal replacement therapy.

Table 6: Association of micronutrients and BC risk among participants.

Nutrients †Q1 Q2 Q3 Q4
Vitamin B1 (mg)
Number of case 43 40 46 71
Number of control 57 59 54 29
*OR (95%CI) 1 0.72 (0.37-1.42) 1.05 (0.54-2.03) 3.01 (1.50-6.06)
P-trend 0.001
Vitamin B2 (mg)
Number of case 39 27 54 80
Number of control 60 73 46 20
*OR (95%CI) 1 0.62 (0.30-1.26) 1.71 (0.88-3.32) 5.21 (2.47-10.95)
P-trend 0.001
Vitamin B3 (mg)
Number of case 31 37 65 67
Number of control 69 63 34 33
*OR (95%CI) 1 2.30 (1.13-4.67) 4.82 (2.36-9.84) 4.97 (2.45-10.09)
P-trend 0.001
Vitamin D (µg)
Number of case 52 37 57 54
Number of control 47 63 43 46
*OR (95%CI) 1 0.48 (0.24-0.94) 1.13 (0.58-2.21) 0.82 (0.42-1.61)
P-trend 0.488
Vitamin E (mg)‡
Number of case 24 41 60 75
Number of control 76 59 39 25
*OR (95%CI) 1 1.56 (0.76-3.18) 4.20 (2.04-8.61) 8.20 (3.80-17.7)
P-trend 0.001
Folate (µg)§
Number of case 29 52 60 59
Number of control 71 47 40 41
*OR (95%CI) 1 2.55 (1.27-5.12) 4.01 (1.98-8.10) 2.66 (1.34-5.29)
P-trend 0.001
Phosphorus (mg)
Number of case 30 41 56 73
Number of control 70 58 44 27
*OR (95%CI) 1 2.08 (1.04-4.17) 3.18 (1.58-6.41) 5.50 (2.66-11.37)
P-trend 0.001
Iron (mg)
Number of case 87 61 34 18
Number of control 13 39 66 81
*OR (95%CI) 1 0.20 (0.09-0.46) 0.07 (0.03-0.17) 0.03 (0.01-0.07)
P-trend 0.001

Significant at p≤0.05.

* Adjusted for energy, age, martial statues, education, work, income, physical activity, smoking, family history, health problem, number of pregnancy, lactation, hormonal replacement therapy.

‡ Vitamin E as a-Tocopherol

§ Folate as dietary folate equivalents

Additionally, table 5 represents ORs for the intake quartile of macronutrients, after adjusting for confounders, and the risk of BC. The ORs of the quartiles of carbohydrate, sugar, fat, saturated fat, PUFA and MUFA intake as g/day were significantly associated with the risk of BC in at least two quartiles (p-trend= 0.001). As noted in table 5, a significant direct trend in BC risk was found for cholesterol (p-trend= 0.005) but none of the quartiles are statistically significant. Comparable results were highlighted by Thiébaut et al. (2007) who reported the presence of direct association between fat intake and the risk of postmenopausal invasive BC.25 Moreover, a positive association of SFA intake with BC has been documented in several case-control studies.26-28 Regarding the intake of MUFA and its association with the risk of BC, Khodarahmi and Azadbakht  (2014) has shown a positive role of MUFA in the pathogenesis of BC.28 In addition, Saadatian-Elahi et al. (2004) reported a significant increase of BC risk with increasing MUFA intake.29 While several cohort studies showed an inverse association of MUFA intake and BC risk,30,31 others reported no association MUFAs intake and BC risk.32 Also, Michels et al. (2018) illustrated that MUFA intake was unrelated to BC risk in cis-form, however, they found that high serum level of trans-MUFA.33 With regards to PUFA intake and BC risk, a similar result was obtained by Chajès et al. (2011).34 The authors documented that PUFA intake increased the risk of BC significantly. However, Anna et al. (2012) found that the total PUFA was not associated with BC.35

Regarding cholesterol results, similar direct association between cholesterol and BC risk has been reported by Farvid et al. (2014).36 Farvid et al. (2014) found that cholesterol intake was associated with higher risk of BC, but this association was attenuated after accounting for intake of red meat.36

The OR and 95%CI for micronutrient after adjusted confounders are shown in table 6. The ORs for higher intakes of vitamin B3, folate and phosphorus showed a significant association the risk of developing BC with p-trend= 0.001. The study findings also showed that the intake of vitamins E, B1 and B2 had a significant trend for BC. On the other hand, iron intake showed significant protective effect against BC (p-trend= 0.001) and vitamin D showed its protective effect on the second quartile (OR 0.48 (95%CI: 0.24-0.94). These findings are on contrary to what has been documented by Kabat et al. (2008) who found that folate intake reduce the risk of common cancers parallel with B vitamins.37 However, Larsson et al. (2007) found that adequate folate intake may reduce the increased risk of BC that has been associated with moderate or high alcohol consumption, but no clear support for an overall relationship between folate intake and the risk of BC was concluded.38 However, thiamine is commonly supplemented in processed foods and readily consumed in over-the-counter vitamin and nutritional supplements in Western countries with generally high cancer incidences.39 However, like so many other nutritional correlations with cancer incidence, the dietary intake of B vitamins and folate with BC risk has provided conflicting results.40 Our data shows that vitamin E increased the risk of BC and this could be explained by the risk of food sources of vitamin E. The main sources of vitamin E are PUFA and MUFA which already have a significant association BC. Moreover, Nagel et al. (2010) demonstrated that no associations between BC risk and vitamin E had been detected in their study.41 On the other hand, no significant difference between the cases and controls in vitamin D intake has been detected, however, the risk of BC decreased significantly in the second quartile with insignificant p-trend (0.488). This is in agreement with Jamshidi-Naeini et al. (2016) who concluded that dietary intake of vitamin D was associated with decreased risk of BC.42 Kuhn et al. (2013) indicated that vitamin D may block estrogen signaling by inhibiting estrogen synthesis and by down regulating estrogen receptor expression in BC cells.43 On the other hand, higher phosphorus intake by our study cases increased the risk of BC which this could be attributed the higher intake of proteins. Proteins are considered the main sources for phosphorus. Our study showed that the dietary iron intake in controls was higher than BC cases. In addition, the risk of BC decreased by increasing the intake of dietary iron. Similar result was found by Bae et al. (2009) in their case-control study and they documented that dietary iron intake was higher in the control than BC case group.44 However, Kabat et al. (2007) did not report any association between iron intakes and BC risk.45

Although, no diet or lifestyle pattern can guarantee full protection against any disease; the potential health benefit represents a decreased likelihood that the disease will occur, not a guarantee of total protection.46 However, weight control, physical activity, and healthy nutrition may contribute in the prevention of cancer especially those types related to diet and lifestyle.46

The main strength of this study is the use of a validated, detailed and culture-sensitive FFQ to collect dietary data from our study population. Although dietary data was collected at only one time, the FFQ has been reported to be a suitable tool for estimating nutrients intake. The major limitation is the using of FFQ to assess the possible association between nutrients intake and BC risk.  Our study relied greatly on the ability participants to recall their memories to complete the questions of the FFQ. It is well-known that there are obvious differences in recall among participants, and consequently biases may exist. Like all other studies, it was difficult to take into consideration the possible effects of cooking on the bioavailability of the various nutrients. Although the sample size is small but it is representative as calculated by the statistician. In 2014, a total of 1187 of newly BC diagnosed cases in both sexes had been reported by Jordanian Cancer Registry.

Conclusions

The study revealed that the intake of carbohydrate, sugar, fat, SFA, trans fats, MUFA, PUFA and cholesterol from the macronutrients as well as phosphorus, vitamins B1, B2, B3 and E, and folic acid from the micronutrients was al higher in cases as compared to controls. Clearly, the risk of BC may increase in women on higher dietary intake of energy, carbohydrate, sugar, fats, cholesterol and some minerals and vitamins. Only iron intake was found to be associated with lower risk of BC among our study participants.

Acknowledgement

The authors would like to thank Deanship of Academic Research of The University of Jordan for supporting this project.

Author Contributions

RFT and RIM participated in conception and design of the study. RIM and LSM recruited participants. RIM collected data from hospitals. RFT, RIM and LSM interpreted the results and drafted the manuscript.

Ethical standards

This study complies with Helsinki declaration of 1975, as revised in 1983.

Funding

This research was funded by Deanship of Academic Research of The University of Jordan (Grant number 2016-2017/14).

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List of Abbreviations

Abbreviation Full Name
BC Breast Cancer
BMI Body Mass Index
CI Confidence Interval
FFQ Food Frequency Questionnaire
KHCC King Hussein Cancer Center
METs Metabolic Equivalents
MUFA Monounsaturated Fatty Acids
OR Odd Ratio
PA Physical Activity
PUFA Polyunsaturated Fatty acids
PAR Physical Activity 7-day Recall
SFA Saturated Fatty Acids


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