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Asian Pacific Journal of Cancer Prevention : APJCP logoLink to Asian Pacific Journal of Cancer Prevention : APJCP
. 2022 Jul;23(7):2469–2476. doi: 10.31557/APJCP.2022.23.7.2469

Modifiable (Sleeping Pattern and Stress) and Non-Modifiable Risk Factors Associated with Breast Cancer: A Matched Case-Control Study in Delhi, India

Gayatri Vishwakarma 1,*, Anurag Mehta 2, Mumtaz Saifi 3, Disha Garg 1, Deepika Paliwal 2
PMCID: PMC9727361  PMID: 35901355

Abstract

Background:

An utmost increase of breast cancer burden during the last several decades was reported in Asian countries. Findings from literature confirm that risk factors of breast cancers can be modifiable and non-modifiable in nature.

Objective:

The present study is designed to identify specific modifiable and non-modifiable risk factors associated with breast cancer.

Methods:

A matched case-control study was conducted considering 187 cases as women diagnosed with breast cancer and 187 hospital-controls as women without having breast cancer visiting the hospital. Other than standard risk factors, stress is measured using Perceived Stress Scale (PSS) and stress is measured using Pittsburgh Sleep Quality Index (PSQI). Several modifiable and non-modifiable risk factors were assessed using conditional logistic regression to find out significant association with breast cancer. Results: Regular multi-vitamin uptake (OR = 3.38; 95%CI = 1.69 – 6.77; p-value = 0.001), poor sleep (OR = 11.29; 95%CI = 4.36 – 29.25; p-value < 0.001), irregular sleep (OR = 34.11; 95%CI = 10.03 – 115.92; p-value < 0.001) and severe stress (OR = 6.74; 95%CI = 3.06 – 14.81; p-value < 0.001) were found to be the highest odds ratio among all modifiable risk factor of breast cancer. Also, age at first childbirth less than 30 years (OR = 0.44; 95%CI = 0.25 – 0.78; p-value = 0.005) was found protective against breast cancer.

Conclusion:

In our study, stress, sleeping pattern, and regular multi-vitamin uptake were found to be significant modifiable risk factors of breast cancer. None of the non-modifiable risk factors were found to be significantly associated with the risk of breast cancer.

Key Words: Risk factor, breast cancer, stress, sleeping pattern, case-control study

Introduction

India has the world’s most bio-diverse regions. Much of Indian biodiversity is intricately related to the socio-cultural practices of the land. Unfortunately, due to population explosion, climate change and lax implementation of environmental policies, the incidence of cancer is increasing in India. Cancer is the second foremost cause of mortality in Indians followed by cardiovascular disease(The Times of India, Feb 2020). The current scenario and data from the various cancer registries show various patterns in different sites of cancer. Urban Indian women are having the most commonest cancer like breast cancer however it is the second commonest cancer among the rural women Agarwal and Ramakant (2008).

There is no central cancer registry to provide comprehensive nationwide data. However ICMR-PBCR data says breast cancer is the commonest cancer among women in urban registries where it constitutes > 30% of all cancers in females. NCRP’s latest report (2012 – 2014) revealed that Nagpur (31.9), Ahmedabad (31.5), Pune (31.4), and Bhopal (31.2) are the cities with highest relative proportion of leading breast cancer are being reported followed by Chennai (30.7) and Aurangabad (30.6) (National Cancer Registry Programme (NCRP) 2014: Annual report).

India is undergoing a period of dramatic social and economic changes. A Systematic review and meta-analysis of breast cancer showed a protective effect of ever breastfeeding against hormone receptor-negative breast cancers, and this effect seems to be several times stronger than what had been suggested by studies of all breast cancers without stratification by receptor status. Women with the highest risk of receptor-negative breast cancers, such as African-American women and BRCA1 carriers, can potentially benefit more from breastfeeding. Similarly, other case-control studies on breast cancer reported several risk factors such as overall poor health awareness, socio-economic status, environmental factors etc. A meta-analysis on Indian case-control studies revealed that family history of breast cancer, never breastfeeding, nulliparity, age at menarche (<13 years), age at menopause >50 years, first pregnancy age >25 years, BMI more than 25 years, postmenopausal status and never married are risk factors of breast cancer for women in India Vishwakarma et al.,(2019).

Findings from literature confirm that risk factors of breast cancers can be modifiable (BMI, age at first pregnancy, breastfeeding, OCP, parity, dietary pattern, residence, sleeping patter, and marital status) Gajalakshmi and Shanta (1991); Mathew et al., (2008); Datta et al., (2009); Gajalakshami et al., (2009); Jayalekshmi et al., (2009); Saxena et al., (2009); Singh et al., (2011); Singh and Jangra (2013); Datta et al., (2014); Devi et al., (2015); Mohite et al., (2015); Gathani et al., (2017); Sambyal et al., (2015); Yilmaz (2020) and non-modifiable (FH of breast cancer, menopausal status, age at menarche, and age of menopause) (Parameshwari et al., 2013; Kapahi et al., (2014); Wirth et al., 2014; Mohite et al., 2015; Sambyal et al., 2015; Gathani et al., 2017). Keeping above various risk factors in the mind a matched case-control study is conducted to find risk factors of breast cancer in the Indian scenario. Sleeping pattern and use of multi-vitamins are also significant risk factors for developing breast cancer (Larsson et al., 2010; Chan et al., 2011; Dutta et al., 2014; Erren et al., 2016; Lu et al., 2017; Trudel-Fitzgerald et al., 2017). Studies show that women who work in night-shifts continuously for more than 3-year period found a 60% higher risk for developing breast cancer (Sleep Charity, 2019). Studies reported in the literature that there is an increased risk of breast cancer in women who had experienced stressful life events and have more than 2-times more risk who experienced severe stress events Dey et al., (2009); Moreno-Smith et al., (2010), Antonova et al. (2011). The objective of the study was to identify various risk factors associated with breast cancer including demographic and clinical characteristics of women registered in a tertiary care center in Delhi.

Materials and Methods

This study is conducted in a tertiary care center of Delhi, includes women diagnosed with breast cancer at the hospital from Dec 2017 to Jan 2020. A matched case-control study was conducted considering cases as women diagnosed with breast cancer and women without breast cancer visiting the hospital i.e. hospital control. We excluded patients who were diagnosed outside the hospital, were male and were non-resident Indian. Cases and controls are defined as

CASES: All type of histo-pathologically confirmed cases of breast cancer irrespective of their stage will be included in the study diagnosed after 15 December 2017.

CONTROLS: ± 2 years age matched individuals; patients other than breast cancer in the hospital or visitors or individual outside the hospital setup.

A retrospective review of patient records was done to determine the demographic profile, clinical characteristics and treatment details. A 20-minutes questionnaire was got filled by a trained assistant in-person. Consent was taken from patients as well as controls before filling this questionnaire. The study duration was of 2.5 years to reach the proposed sample size. The expected outcomes of the study was to identify various risk factors (modifiable and non-modifiable) of breast cancer in the Indian scenario.

Composite variable for physical activity (Yoga/Sports/Swimming/Housework/walking) was created for analysis based on four dimensions i.e. Mode (Specific activity performed e.g. walking, gardening, cycling), frequency (Number of sessions per day or week), intensity (Rate of energy expenditure) and duration (Time (minutes or hours) of the activity) Strath et al., (2013). Further, it is classified into three categories i.e. very light, moderate and very hard. Another composite variable was created for sleeping patterns based on a scale developed by Fukuda 1999 and three categories are formed i.e. good sleeper, poor sleeper and irregular sleeper Fukuda et al., (1999). Duration (months and years) of use of multi-vitamins were recorded into three categories based on ingredients: (1) multi-vitamins (alone) (2) multi-vitamins with minerals and (3) stress multi-supplements (higher doses of several B vitamins and of vitamin C or selected minerals, such as selenium or zinc. Category 1 and 2 were considered as a multi-vitamin supplements as risk factors. Collected data were compiled into three categories i.e. never use, regular use and occasional (used at least a month) (Neuhouser et al., 2009). The Perceived Stress Scale (PSS) is used to assess stress. The questions were asked about your feelings and thoughts during the last month on a five-point scale (0 – never, 1– almost never, 2 – sometimes, 3 – fairly often, 4 – very often) Cohen et al. (1983). Individual scores are ranging from 0 to 40 with higher scores indicating higher perceived stress. For data analysis, we considered three categories of stress i.e. scores ranging from 0-13 would be considered low stress, scores ranging from 14-26 would be considered moderate stress and scores ranging from 27-40 is considered high perceived stress.

The Pittsburgh Sleep Quality Index (PSQI) is a systematized measure of sleep quality that consists of 19 items reflecting sleep quality, sleep latency, sleep duration, habitual sleep efficiency, use of sleeping medications, sleep disturbance, and daytime dysfunction. The scores range from 0 to 21 and for data analysis we categorized it into three categories to better understand the pattern that a score >5 is considered as a poor sleeper, 1-3 irregular sleeper and 0 is for good sleeper.

The total sample size needed for this matched case-control study was 374 (187 cases and 187 controls). It was calculated using nMaster v2.0 software with an exposure of 30% in the control group, anticipated odds ratio (OR) of 2 for 90% power and 5% level of significance. In matched case/control study designs, useful data come from only the discordant pairs of subjects. Matching of cases and controls on a confounding factor of age increases the efficiency of a case-control study.

Statistical Analyses

Descriptive statistics summary were presented using mean ± standard deviation (SD) or median with inter-quartile range (IQR) for quantitative variables. Summary of categorical variables were represented with frequencies with corresponding percentages. Association between two categorical factors was analyzed using Fisher’s exact test or the chi-square test while Pearson’s/Spearman correlation analysis was performed to find association between two quantitative variables depends upon normality of data. A univariate logistic regression analysis was performed to evaluate significant factors associated with breast cancer followed by multivariable logistic regression analysis to calculate adjusted OR with 95% CI. The backward stepwise elimination (Conditional) method was used as removal testing which is based on the probability of the likelihood-ratio statistic. The p-value threshold was set at 0.05 and SPSS v23.0 was used for statistical analysis.

Missing data management: Complete-case analysis may be biased and insufficient where exclusion of a case or control leaves a matched set. Multiple imputation (MI) method is used to deal with missing observations which use matching variables that imputes missing values assuming age and time of questionnaire completion Seaman et al., (2015).

Results

The study population comprised of 187 cases (recruitment rate of 78%) and 187 controls (recruitment rate of 75%). Most women had more than a high school education (75.4%), were home maker/unemployed (71.9%), were married (97.9%), and refrained from tobacco use (99%). Average age of cases was recorded 50.8±11.6 years and of controls 49.3±10.9 while average BMI for cases and controls was 26.7±4.8 and 25.6±3.6 respectively (Table 1).

Table 1.

Study Population Characteristics

Characteristics Cases (N=190) Control (N=187) Total (N=377)
Age in years, Mean (SD) 50.8 (11.6) 52.3 (11.9) 51.6 (11.8)
Age in years, n (%)
<40 years 36 (18.9) 34 (18.2) 70 (18.6)
40 – 60 years 112 (58.9) 98 (52.4) 210 (55.7)
> 60 years 42 (22.1) 55 (29.4) 97 (25.7)
BMI, Mean (SD) 26.7 (4.8) 25.6 (3.6) 26.1 (4.2)
Education, n(%)
Illiterate/<5yrs of school 34 (17.9) 19 (10.2) 53 (14.1)
Middle school 11 (5.8) 29 (15.5) 40 (10.6)
High school/Inter/Diploma 40 (21.1) 40 (21.4) 80 (21.2)
Graduate/Post-graduate 82 (43.2) 79 (42.2) 161 (42.7)
Professional degree 23 (12.1) 20 (10.7) 43 (11.4)
Marital Status, n(%)
Married/separated/ divorced 171(90.0) 169(90.4) 340 (90.2)
Widowed 12(6.3) 17(9.1) 29 (7.7)
Unmarried 7(3.7) 1(0.5) 8 (2.1)
Occupation, n(%)
Unemployed 135 (71.1) 129 (69.0) 264 (70.1)
Professional 52 (27.4) 50 (26.7) 102 (27.1)
Skilled worker 3 (1.6) 6 (3.2) 9 (2.4)
Unskilled worker 0 (0.0) 2 (1.1) 2 (0.5)
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Table 2 shows the results of univariate conditional logistic regression analysis for socio-demographic factors and modifiable risk factors of breast cancer. In univariate analysis, among socio-demographic variable, BMI for obese (OR = 1.58; 95%CI = 1.02 – 2.46; p-value = 0.041), marriage age between 18 – 24 year (OR = 0.54; 95%CI = 0.33 – 0.90; p-value = 0.019), breastfeeding (OR = 2.05; 95%CI = 1.04 – 4.04; p-value = 0.039), semi-vegetarian diet (OR = 0.30; 95%CI = 0.09 – 0.96; p-value = 0.043), regular multi-vitamin uptake (OR = 3.39; 95%CI = 1.87 – 6.13; p-value <0.001), very light physical activity (OR = 16.30; 95%CI = 2.07 – 128.66; p-value = 0.008), poor sleep (OR = 11.34; 95%CI = 4.72 – 27.22; p-value < 0.001), irregular sleep (OR = 24.41; 95%CI = 8.48 – 70.25; p-value < 0.001), mild-to-moderate stress (OR = 2.62; 95%CI = 1.31 – 5.22; p-value = 0.007) and severe stress (OR = 7.73; 95%CI = 3.83 – 15.50; p-value < 0.001) were found to be significant risk factor of breast cancer.

Table 2.

Risk of Breast Cancer associated with Socio-Demographic Characteristics and Modifiable Risk Factors

Characteristics Cases Control Univariate Regression Multivariable Regression#
(N=190) (N=187) OR (95%CI) P-value OR (95%CI) P-value
BMI, n(%)
Underweight 9 (5.0) 6 (3.2) Ref -
Normal 52 (29.1) 74 (39.8) 0.74 (0.26 – 2.15) 0.583
Obese 118 (65.9) 106 (57.0) 1.58 (1.02 – 2.46) 0.041
Age in years, n(%)
<40 years 36 (18.9) 34 (18.2) Ref -
40 – 60 years 112 (58.9) 98 (52.4) 0.93 (0.54 – 1.59) 0.782
> 60 years 42 (22.1) 55 (29.4) 1.39 (0.75 – 2.57) 0.299
Education, n(%)
Illiterate/<5yrs of school 19 (10.9) 19 (10.2) Ref -
Middle school 11 (6.3) 29 (15.5) 2.64 (0.89 – 7.81) 0.044
High school/Inter/Diploma 40 (22.9) 40 (21.4) 1.00 (0.46 – 2.17) 0.998
Graduate/Post-graduate 82 (46.9) 79 (42.2) 0.96 (0.47 – 1.95) 0.918
Professional degree 23 (13.1) 20 (10.7) 0.87 (0.36 – 2.08) 0.754
Marital Status, n(%)
Unmarried 7 (3.7) 1 (0.5) 7.12 (0.87 – 58.40) 0.068
Married/separated/ divorced/Widowed 183 (96.3) 186 (99.5) Ref -
Age at Marriage, n(%)
< 18 yrs 35 (18.4) 55 (29.4) Ref -
18 – 24 yrs 101 (53.2) 86 (46.0) 0.54 (0.33 – 0.90) 0.019
≥ 25 yrs 47 (24.7) 43 (23.0) 0.58 (0.32 – 1.05) 0.073
Single 7 (3.7) 3 (1.6) 0.27 (0.07 – 1.12) 0.072
Age at 1st Childbirth, n(%)
< 25 yrs 93 (56.0) 108 (62.1) Ref - Ref -
25 – 30 yrs 67 (40.4) 57 (31.8) 0.73 (0.47 – 1.15) 0.174 0.44 (0.25 – 0.78) 0.005
30 – 35 yrs 6 (3.6) 9 (5.2) 0.64 (0.44 – 3.76) 0.639 0.83 (0.25 – 2.73) 0.754
Type of Delivery, n(%)
No delivery 19 (10.0) 13 (7.0) Ref -
Normal 126 (66.3) 135 (72.2) 1.57 (0.74 – 3.30) 0.239
Cesarean 36 (18.9) 24 (12.8) 0.97 (0.41 – 2.34) 0.954
Both 9 (4.7) 15 (8.0) 2.44 (0.82 – 7.22) 0.108
Late Pregnancy, n(%)
No delivery 14 (7.4) 13 (7.0) Ref -
No 150 (78.9) 162 (86.6) 1.16 (0.53 – 2.56) 0.707
Yes 26 (13.7) 12 (6.4) 0.50 (0.18 – 1.38) 0.179
Breastfeeding, n(%)
Yes 163 (85.8) 173 (92.5) Ref -
No 27 (14.3) 14 (7.5) 2.05 (1.04 – 4.04) 0.039
Breastfeeding duration in months
(Median, IQR)		 12 (18) 18 (12) 1.01 (0.99 – 1.02) 0.116
Oral Contraceptive Pill, n(%)
Yes 11 (5.8) 5 (7.2) Ref -
No/Never 179 (94.2) 182 (97.3) 0.45 (0.15 – 1.31) 0.143
Hormone replacement therapy, n(%)
Yes 12 (6.3) 9 (4.8) Ref -
No 178 (93.7) 178 (95.2) 0.75 (0.31 – 1.82) 0.526
Diet, n(%)
Non-Veg 52 (27.4) 50 (26.7) Ref -
Semi-Veg 14 (7.4) 4 (2.1) 0.30 (0.09 – 0.96) 0.043
Vegan/ Lacto-Veg 124 (65.2) 133 (71.1) 1.12 (0.71 – 1.77) 0.641
Dysmenorrhea, n(%) 
Yes 23 (12.1) 22 (11.8) Ref -
No 167 (87.9) 165 (88.2) 1.03 (0.55 – 1.93) 0.919
Multi-vitamin uptake, n(%)
Never 131 (68.9) 87 (46.5) Ref - Ref -
Regular 20 (10.5) 45 (24.1) 3.39 (1.87 – 6.13) <0.001 3.38 (1.69 – 6.77) 0.001
Occasional (Iron/Calcium) 39 (20.5) 55 (29.4) 2.12 (1.30 – 3.47) 0.003 3.31 (1.80 – 6.06) <0.001
Physical activity, n(%)
Very hard 11 (5.8) 1 (0.5) Ref -
Moderate 83 (43.7) 123 (65.8) 7.22 (0.91 – 57.31) 0.061
Very light 96 (50.5) 63 (33.7) 16.30 (2.07 – 128.66) 0.008
Sleeping pattern, n(%)
Good sleeper 121 (63.7) 147 (78.6) Ref - Ref -
Poor sleeper 13 (6.8) 34 (18.2) 11.34 (4.72 – 27.22) <0.001 11.29 (4.36 – 29.25) <0.001
Irregular sleeper 56 (29.5) 6 (3.2) 24.41 (8.48 – 70.25) <0.001 34.11 (10.03 – 115.92) <0.001
Stress, n(%)
No stress 53 (27.9) 111 (59.4) Ref - Ref -
Mild to Moderate stress 89 (46.8) 63 (33.7) 2.61 (1.31 – 5.22) 0.007 1.64 (0.74 – 3.60) 0.221
Severe stress 48 (25.3) 13 (7.0) 7.73 (3.83 – 15.50) <0.001 6.74 (3.06 – 14.81) <0.001
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#Variable(s) entered: Multi-vitamins-uptake, Sleep type, Stress, Age at 1st childbirth.

In multivariable conditional logistic regression analysis (Table 2), age at first childbirth less than 30 years (OR = 0.44; 95%CI = 0.25 – 0.78; p-value = 0.005) were found protective factor against risk of having breast cancer. There is 75% less risk of getting breast cancer if a woman has her first child before the age of 30 years. Regular multi-vitamin uptake (OR = 3.38; 95%CI = 1.69 – 6.77; p-value = 0.001) and occasional use of multi-vitamins (OR = 3.31; 95%CI = 1.80 – 6.06; p-value < 0.001), both found to be significant risk factor of breast cancer. There is 3-times more risk of having breast cancer if a woman is regularly taking multi-vitamins vs those who are not taking it. Poor sleep (OR = 11.29; 95%CI = 4.36 – 29.25; p-value < 0.001) and irregular sleep (OR = 34.11; 95%CI = 10.03 – 115.92; p-value < 0.001) has the highest odds ratio among all modifiable risk factor of breast cancer. Severe stress (OR = 6.74; 95%CI = 3.06 – 14.81; p-value < 0.001) was found to be significant risk factor of breast cancer. Women suffering from severe stress are at around 7-times more risk of having breast cancer as compared to those who are not having it.

Table 3 depicts association between non-modifiable risk factors with breast cancer. In univariate conditional logistic regression, none of the risk factors studied found significantly associated with breast cancer.

Table 3.

Non-Modifiable Risk Factors Associated with Breast Cancer

Characteristics Cases (N=190) Control (N=187) OR (95%CI) P-value
Age in years, n(%)
< 40 7 (3.7) 3 (1.6) Ref -
40 – 60 89 (46.8) 74 (39.6) 1.94 (0.49 – 7.77) 0.349
> 60 94 (49.5) 110 (58.8) 2.73 (0.69 – 10.86) 0.154
Dysmenorrhea, n(%)
Yes 23 (12.1) 22 (11.8) Ref -
No 167 (87.9) 165 (88.2) 1.03 (0.55 – 1.93) 0.919
Menarche Age, n(%)
≤ 13 year 77 (40.5) 64 (34.2) Ref -
> 13 years 113 (59.5) 123 (65.8) 1.31 (0.86 – 1.99) 0.207
Menopausal Age, n(%)
≤ 45 year 29 (15.3) 30 (16.0) Ref -
> 45 years 95 (50.0) 108 (58.8) 1.10 (0.62 – 1.96) 0.750
Not reached to menopause yet 66 (34.7) 49 (26.2) 0.72 (0.38 – 1.35) 0.302
Miscarriage/still birth, n(%)
No 130 (68.4) 137 (73.3) Ref -
Yes 60 (31.6) 50 (26.7) 1.27 (0.81 – 1.97) 0.302
Breast Cancer Family History, n(%)
Yes 23 (12.1) 16 (8.6) Ref -
No 167 (87.9) 171 (91.4) 0.68 (0.35 – 1.33) 0.260
Family History of any type of cancer, n(%)
Yes 57 (30.0) 72 (38.5) Ref -
No 133 (70.0) 115 (61.5) 0.69 (0.45 – 1.05) 0.082
Comorbidity – Diabetes, n(%)
Yes 30 (15.8) 42 (22.5) Ref -
No 160 (84.2) 145 (77.5) 1.55 (0.92 – 2.59) 0.101
Comorbidity – Hypertension, n(%)
Yes 45 (23.7) 57 (30.5) Ref -
No 145 (76.3) 130 (69.5) 1.41 (0.90 – 1.41) 0.138
Comorbidity – Thyroid, n(%)
Yes 37 (19.5) 39 (20.9) Ref -
No 153 (80.5) 148 (79.1) 1.09 (0.66 – 1.80) 0.738
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Discussion

Our study investigated the association between breast cancer (BC) and several risk factors i.e. modifiable and non-modifiable. In the present hospital-based case-control study in patients with BC, obesity, age at first childbirth, breastfeeding, multi-vitamin uptake, sedentary lifestyle, sleeping patter and stress were significant risk factors. We did observe a significant association with increased risk of developing BC and BMI and literature support this association (Singh et al., 2011; Amadou et al., 2013; Singh and Jangra, 2013; Engin, 2017; Picon-Ruiz, 2017; Vishwakarma et al., 2019). The risk of BC was 56% less in female whose age at first child birth was less than 30 years. (Gajalakshmi et al., 1991; Rao et al., 1994; Samson et al., 2007; Meshram et al., 2009; Saxena et al., 2009; Dey et al., 2009; Jayalekshmi et al., 2009; Lodha et al., 2011; Babita et al., 2014; Rajbongshi et al., 2015; Vishwakarma et al., 2019) confirms our result finding Gajalakshmi and Shanta,1991; Samson et al., (2007); Meshram et al., 2008; Jayalekshmi et al., 2009; Saxena et al., 2009; Gajalakshmi et al., 2009; Dey et al., 2009; Lodha et al., 2011; Babita et al., 2014; Rajbongshi et al., 201; Vishwakarma et al., 2019).

Studies published early Gajalakshami et al., (2009); Parameshwari et al., (2013); Dey et al., (2009); Singh and Jangra (2013); Mohire et al., (2015) reported that late menopausal age (>50 years) was at a high risk of BC, however, we did not observe this association from our data. Many studies reported that age at marriage before 25 years may have a 45% of lower chance of developing BC. Our study did confirm such associations Gajalakshmi and Shanta (1991); Jayalekshmi et al., (2009); Kinlen (2014). Multi-vitamin use was associated with a statistically significant 3-times increased risk of breast cancer. It was also reported in the literature that there is an increased risk of BC with the regular use of multi-vitamin Chan et al, (2010); Larsson et al., (2010); Neuhouser et al., (2010); Mann (2010).

Self-reported stress is a forthcoming risk factor in our daily life. Continuing exposure to stress has been associated with negative changes in body homeostasis. In literature, psychological stress is claimed to contributing to the inception of cancer and increase mortality from a number of non-malignant ailments. Breastfeeding can not only give your baby a healthy start. But it also lowers the risk of developing breast cancer. Our study depicts that women who do not breastfeed their child could have two times more risk of BC than the women who breastfeed which is a similar finding from the literature Rao et al., 1994; Meshram et al., 2008; Gajalakshami et al., 2009; Lodha et al., 2011; Mohite et al., 2015; Vishwakarma et al., 2019). Furthermore, it was important to look at the duration of breastfeeding effect on BC. Our data did not show any significant association between the duration of breastfeeding and breast cancer. However, literature has evidence that a longer duration of breastfeeding can be more protected against breast cancer Collaborative Group on Hormonal Factors in Breast Cancer (2002).

The present study found that there is a 7-times more risk of having BC as compared to no stress and the literature confirms our results Cohen et al., (1983); Moreno-Smith et al., (2010); Antonova et al., (2011); Wang et al., (2020). Ng el al., (2017) studied stress in detail and concluded that perceived distress among BC patients is significantly associated with anxiety but not with depression. In contrast, few studies showed no association between stress and BC Robert et al., (1996); Santos et al., (2009); Schoemaker et al., (2016). Therefore this association should be viewed with caution as stress affects the risk of breast cancer is still not known, and greater studies are needed to address this issue Nielsen et al., (2006).

We found that women who has poor sleep and irregular sleep have 11-times and 24-times more risk of developing breast cancer, respectively, than those who sleep for 8 hours and has good sleeping patter. Epidemiological studies also reported the possible association of sleeping patter with BC Datta et al., (2014); Trudel-Fitzgerald et al., (2017). Conversely, evidence-based studies reported that women with a longer sleep duration may have a significantly increased risk of BC, especially ER-positive breast cancer Erren et al., (2016); Lu et al., (2017). None of the non-modifiable risk factors were found to be significantly associated with BC in this case-control study Gajalakshmi and Shanta (1991); Meshram et al., (2008); Saxena et al., (2009); Gajalakshmi et al., (2009); Parameshwari et al., (2013); Babita et al., (2014); Kapahi et al., (2014); Wirth et al., (2014).

A major strength of the present study was the optimum number of cases and controls. The questionnaire was designed to obtain a piece of complete information on modifiable and non-modifiable risk factors.

In the literature, many modifiable and non-modifiable risk factors quoted as risk factors of developing breast cancer. In our study, none of the non-modifiable risk factors were found to be significantly associated with the risk of breast cancer however among modifiable risk factors, age more than 30 years at first childbirth, regular use of multi-vitamin, irregular/poor sleep and severe stress were found to be significant risk factors of developing breast cancer.

Limitation of the study

There are few limitations in this study. One of the limitations is the recall bias and misclassification of non-modifiable risk factors. Also, dysmenorrhea, menarche age and breast cancer history in this study were self-reported and this might also bias results. In India, most of the women over age 50 years are still not able to recall their age, age at marriage and menarche age. Due to budget constraints, the study was restricted to one center for data collection. The most common reasons for not participating were death, change of address, and refusal for both cases and controls.

Abbreviations

Breast Cancer (BC), Body Mass Index (BMI), Standard Deviation (SD), Standard Error (SE), Odds Ratio (OR), 95% Confidence Interval (95% CI), Multiple imputation (MI), Inter-Quartile Range (IQR), FH (Family History), Perceived Stress Scale (PSS), Oral Contraceptive Pill (OCP), Indian Council for Medical Research - Population Based Cancer Registries (ICMR-PBCR), National Cancer Registry Program (NCRP).

Author Contribution Statement

The authors confirm contribution to the paper as follows: study conception and design: Gayatri Vishwakarma, Anurag Mehta; data collection: Mumtaz Saifi, Deepika Paliwal; analysis and interpretation of results: Gayatri Vishwakarma, Disha Garg; draft manuscript preparation: Gayatri Vishwakarma, Anurag Mehta. All authors reviewed.

Acknowledgments

None.

Conflict of interest

None

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