Obesity and Height in Urban Nigerian Women with Breast Cancer

Obesity and Height in Urban Nigerian Women with Breast Cancer CLEMENT A. ADEBAMOWO, BMCHB (HONS), TEMIDAYO O. OGUNDIRAN, MBBS, ADENIYI A. ADENIPEKUN, MBBS, RASHEED A. OYESEGUN, MBBS, OLADAPO B. CAMPBELL, MD, EFFIONG U. AKANG, MBBS, CHARLES N. ROTIMI, PHD, AND OLUFUNMILAYO I. OLOPADE, MBBS

PURPOSE: To examine the relationship between obesity, height, and breast cancer in an urban Nigerian population. METHODS: Between March 1998 and August 2000, we conducted a case-control study of hospitalbased breast cancer patients (n  234) and population-based controls (n  273) using nurse interviewers in urban Southwestern Nigeria. RESULTS: The study did not find a significant association between obesity (BMI  30) and breast cancer among all women (OR  1.51, 95% CI  0.87–2.62) pre- (OR  1.21, 95% CI  0.56–2.60) and post-menopausal breast cancer patients (OR  1.82, 95% CI  0.78–4.31) in multivariate logistic regression analysis, while increasing height was positively associated with the risk of breast cancer among all women (OR  1.05, 1.01 – 1.08), pre- (1.06, 1.01–1.10) and post-menopausal women (1.07, 1.01–1.13) for each cm. Age, irregular period, and early age of onset of periods were also found to be significantly associated with breast cancer risk. CONCLUSION: This study failed to demonstrate an association between breast cancer risk and obesity while showing that height is positively associated with risk of breast cancer in urbanized Nigerian women. Ann Epidemiol 2003;13:455–461. © 2003 Elsevier Inc. All rights reserved. KEY WORDS:

Body Mass Index, Obesity, Height, Breast Cancer, Nigeria, Women.

INTRODUCTION Breast cancer is now the most common cancer in Nigeria (1). Some of this increase in incidence may be due to demographic changes such as longer life expectancy, better reporting of disease and improved access to clinical care. Previous studies have suggested that there are significant differences between African and Caucasian populations in the clinical and pathological characteristics of breast cancer (2). Breast cancer patients in Africa tend to present at an early age, with large tumors, multiple nodal involvement and have poorer clinical and pathological prognostic fac-

From the Division of Oncology, Department of Surgery, University College Hospital, Ibadan, Nigeria (A.C.A., O.T.O.); Radiotherapy Department, University College Hospital, Ibadan, Nigeria (A.A.A., R.O., O.C.); Department of Pathology, University College Hospital, Ibadan, Nigeria (E.U.A.); National Human Genome Center, Howard University, Washington D.C. (C.R.); Section of Hematology/Oncology, Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago IL (O.I.O.); Department of Nutrition, Harvard School of Public Health, Boston MA (C.A.A.). Address correspondences to: Dr. Clement Adebayo Adebamowo, Department of Nutrition, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA. Tel.: (617) 277-7607; Fax: (617) 4322435; E-mail: [email protected] Received August 15, 2001; revised June 17, 2002; accepted June 24, 2002. © 2003 Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010

tors compared to Caucasian patients. These characteristics of breast cancer patients in Africa are somewhat similar to those of the African-American experience but are in contrast to that of non-Hispanic Whites in the United States of America (1–3). Despite these differences, there has been remarkably little research done on breast cancer in Africa and many of the publications from Africa about the disease are case reports or retrospective series from patients’ clinical records (4–10). These are likely to be incomplete and lacking in information about epidemiological factors of interest since the data was collected primarily for clinical purposes, and hence did not allow for the exploration of the multiple risk factors associated with a complex disease such as breast cancer. In this paper we report a case-control study of the association between breast cancer, body mass index, and height in an urbanized African population.

METHOD Case Ascertainment All consecutive cases of breast cancer seen at presentation and later confirmed histologically in the Departments of Surgery, and Radiotherapy of the University College Hos1047-2797/03/$–see front matter PII S1047-2797(02)00426-X

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pital, Ibadan, Nigeria (UCH) from March 1998 to August 2000 were eligible for this study. This hospital serves an immediate population of 3 million people and is a referral center for other hospitals in the region. Based on referral patterns, the majority of the cases of breast cancer diagnosed in the region would probably be seen at the UCH. Cases were recruited at first presentation, and after obtaining informed consent, patients were interviewed about their risk factors when they were last well; their height and weight was measured by a trained nurse practitioner who also completed the questionnaires. There were 312 cases; of these, 73 indicated that they had lived predominantly in a rural area and were excluded to reduce the possibility of systematic error due to differences in the exposure of interest between cases and controls (11). There were 5 refusals, and no further information is available from them, leaving 234 cases for analysis. Control Ascertainment During the period of case recruitment, a population-based control recruitment strategy was instituted. A stable, socioeconomically diverse community adjoining the hospital was randomly selected by ballot from a list of all the communities in the area. It is our belief that if anyone in any of these communities were to develop breast cancer, the person will attend the Oncology Clinics of the University Teaching Hospital, Ibadan. A census register of the people living in the community was obtained and a meeting was arranged with the Head and elders of the community, at which the purpose of the study was explained to them. After they consented, meetings were held in the local gathering places at which the purpose of the research was explained to members of the community. Names were then randomly selected from the community register and the people were invited to visit a clinic set up in the community for the study. Inclusion criteria for the controls were: females, age above 18 years, absence of any type of cancer at recruitment, predominant urban residence for most of their lives, and ability to give informed consent. After explaining the project to the potential participant, a complete physical examination was done. A trained nurse then interviewed the participants, measured their height and weight and completed the questionnaires. 278 subjects were eligible, 273 were interviewed; there were 3 refusals and 2 people were not recruited on account of a diagnosis of cancer of the head of pancreas in one and colorectal cancer in the other. Data Collection and Analysis The information obtained from the cases and controls included age, self-reported social status based on median family income, and other baseline demographic information. Obstetric and gynecological history, including age of onset

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of menarche, menstrual cycle history, whether the periods had usually been regular or not, age at onset of menopause (natural or otherwise), history of previous breast disease, smoking, drug and alcohol use history was recorded. Other information obtained included first-degree family history of breast cancer, history of any use of estrogen-containing contraceptives, and where they had lived most of their lives—whether in a rural or urban setting. The body mass index (weight in kg/height in m2) was used as a measure of obesity (obese  BMI  30). Statistical analysis of the data was done with STATA ver. 7.0 (12) for all the women and separately for pre- and post-menopausal women. Univariate analysis was used to identify statistically significant associations at p-value  0.10 (13). These were then used to purposefully build multivariable logistic regression models, starting with continuous age and adding other variables in sequence starting with those having the lowest p-values. With the addition of a variable, the coefficients of the existing variables in the model were examined for any change in value that was more than 10% of their previous value to determine whether the new variable was a confounder. In addition, we examined the p-value of all the variables to see whether they were significantly associated with outcome at a p-value of 0.05. At the end of this process, the variables that were initially eliminated were re-introduced one by one to see whether they might have a joint association with any of the other variables that made it into the model. Automated logistic regression models were then run and compared with the purposeful model; the results were essentially the same.

RESULTS The duration of symptoms among the cases ranged from 1 to 180 months with a mean (SD) of 18.0 (21.5) and median of 10 months. Cases generally had advanced disease at presentation, as there is no routine mammographic screening for breast cancer in this population. Only 0.43% patients had tumor less than 2 cm in diameter, 19.66% had tumor of 2 to 5 cm, 24.36% had tumor of 5 to 10 cm, while the tumor was greater than 10 cm in diameter in 43.59% of the patients. The size of the tumor was not known in 11.99% of the patients. All Women The distribution according to baseline characteristics is shown in Table 1. The mean (SD) age of the cases was 45.5 (11.1) and ranged from 27 to 75, while among the controls, the mean age was 42. 2 (14.1) and ranged from 18 to 80. There was no significant difference in the distribution of number of pregnancies, age at first full-term pregnancy, body mass index, family history of breast cancer, menopausal status, breast-feeding history, use of oral contracep-

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TABLE 1. Predictors of breast cancer among all waomen, Nigeria, 1998–2000, means, (standard deviation), numbers (N), percentages (%), age adjusted odds ratio, 95% confidence interval, and p-value for cases and controls Cases N  234 mean (SD) N(%)

Controls N  273 mean (SD) N(%)

Odds ratio

95% CI

P-value

45.50 (11.06) 5.11 (2.39)

42.25 (14.14) 4.80 (2.90)

1.02 0.98

1.01, 1.0 0.90, 1.06

0.01* 0.56

15.26 (2.03) 23.34 (4.23)

15.74 (2.46) 22.97 (3.94)

0.89 1.02

0.81, 0.96 0.97, 1.07

0.01* 0.40

Yes No Yes No Yes No Missing Yes No Yes No

160.12 (7.83) 64.82 (14.80) 25.10 (5.60) 19 (8.12) 215 (91.88) 104 (46.43) 120 (53.57) 15 (6.40) 215 (91.20) 4 (1.70) 61 (26.07) 173 (73.93) 212 (93.4) 15 (6.4)

158.38 (8.13) 61.19 (13.27) 24.20 (5.20) 22 (8.06) 251 (91.94) 89 (33.09) 180 (66.91) 2 (0.70) 271 (99.30)

1.01, 1.07 1.00, 1.03 0.99, 1.06 0.55, 2.00

0.01* 0.02* 0.22 0.89

41 (15.02) 232 (84.98) 226 (93.8) 15 (6.2)

1.04 1.02 1.02 1.05 1.00 1.51 1.00 9.90 1.00 — 1.85 1.00 0.82 1.00

Yes No Low Middle High

89 (39.38) 137 (60.62) 106 (45.89) 112 (41.33) 1 (0.37)

89 (32.60) 184 (67.40) 158 (58.30) 113 (48.92) 12 (5.19)

0.74 1.00 1.00 1.50 17.89

Predictor Age (years) Total number of pregnancies Age at menarche (years) Age at first full term pregnancy (years) Height (cm) Weight (kg) BMI (unit/kg/m2) Family history of breast cancer Postmenopausal (natural) Ever had irregular period

Obesity (BMI  30) Ever breast Fed (among parous women) Ever used Contraceptive pill Social status

0.87, 2.62 2.20, 43.80

0.14  0.01*

1.18, 2.90

0.02*

0.87, 3.00

0.13

0.51, 1.08

0.12  0.01*

1.05 – 2.15 2.29 – 139.60

* Indicates statistical significance at p  0.05.

tives between cases and controls. However, cases were significantly taller, heavier, younger at onset of menarche, more likely to have had irregular periods, and be of a lower social status compared with controls. In multivariate logistic regression analysis, adjusted for age, age at onset of menarche, regularity of period and social status, increasing height was positively associated with breast cancer risk (OR  1.05, 95% CI  1.01–1.08) per cm, while obesity was not, either with dichotomous BMI (BMI  30) (OR  1.24, 95% CI  0.70–2.19) or as continuous variable (BMI unit/ kg/m2) (OR  1.00, 95% CI  0.96–1.04) (Table 2).

tives and social status between cases and controls. However, cases had an earlier age of onset of menarche, later age at first full-term pregnancy and were more likely to have ever had irregular periods compared to controls. In multivariate logistic regression analysis, adjusting for these variables, increasing height (per cm) was positively associated with breast cancer risk (OR  1.05, 95% CI  1.01–1.10) while obesity was not, either with dichotomous BMI (BMI  30) (OR  1.21, 95% CI  0.56–2.60) or as continuous (BMI unit/kg/m2) (OR  1.01, 95% CI  0.95 – 1.06) (Table 2).

Premenopausal Women Table 3 shows the distribution according to baseline characteristics among premenopausal women. The mean (SD) age (in years) of the cases, 38.3 (6.55) was significantly higher than that of controls, 34.2 (8.80). There was no significant difference in the distribution of number of pregnancies, body mass index, weight, height, family history of breast cancer, breast-feeding history, use of oral contracep-

Postmenopausal Women The mean (SD) age (years) of postmenopausal cases, 53.6 (9.55) was significantly lower than that of controls 58.4 (7.92). Table 4 also shows that cases were taller and more likely to be obese than controls; however data was too sparse to evaluate breast-feeding history and social status among this category of women. There was no significant

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TABLE 2. Odds ratio and 95% confidence intervals for different models of body mass index, and height for pre- and post-menopausal women, Nigeria, 1998–2000 All womena

Model A Height (cm) BMI  30 BMI  30 Model B Height (cm) Continuous BMI (units/kg/m2)

Postmenopausal womenb

No. of cases and controls

OR

95% CI

No. of cases and controls

OR

95% CI

1.05 1.00 1.24

1.01 – 1.08*

173/232 61/41

1.07 1.00 1.82

1.00 – 1.13*

73/73 31/16

1.04

1.01 – 1.08*

1.00

0.96 – 1.04

0.70 – 2.19

Premenopausal womenc No. of cases and controls

OR

95% CI

0.78 – 4.31

1.05 1.00 1.21

1.01 – 1.10*

91/155 29/25

1.07

1.00 – 1.13*

1.05

1.00 – 1.10*

1.04

0.98 – 1.11

1.01

0.95 – 1.06

0.56 – 2.60

Model A, predictor is dichotomous obesity (BMI 30). Model B is a linear model of BMI in unit/kg/m2. a Odds ratio adjusted for age, age at onset of menarche, regularity of period and social status. b Odds ratio adjusted for age, age of onset of menarche, later age at first full term pregnancy and regularity of periods. c Models adjusted for age, age at onset of menarche, and regularity of periods. Only women with natural menopause included. * Indicates statistical significance at p  0.05.

difference in the distribution of number of pregnancies, age at menarche, age at first full-term pregnancy, age at menopause, weight, ever had irregular periods, ever used oral contraceptives and family history between cases and controls. In multivariate logistic regression adjusting for variables that were significant in univariate analysis, obesity was not associated with risk of breast cancer when BMI was modeled as a dichotomous variable (Obesity  BMI  30) (OR  1.82, 95% CI  0.78–4.31) or as continuous (OR  1.04, 95% CI  0.98–1.11), while increasing height was positively associated (OR  1.07, 95% CI  1.00 1.13) (Table 4). Body mass index was further divided into 4 categories (BMI 8, 18–25, 25–30, and 30). Multivariate analysis did not show any statistically significant association between any of the four categories and breast cancer in all, pre- and post-menopausal women. A chi-squared test for trend was also not significant. The p-values for the trend test were 0.98 for all women, 0.78 for premenopausal and 0.15 for postmenopausal women.

DISCUSSION Obesity may influence the risk of development of breast cancer differently among premenopausal compared with postmenopausal women. In premenopausal women, it is associated with irregular menstrual periods and an increased number of anovulatory cycles. This results in fewer luteal phases of the menstrual cycle and may be the reason for the lower mean progesterone levels observed in obese premeno-

pausal women (2). Estrogen and progesterone induce mitoses in both normal and cancerous breast tissue, but when the level of progesterone is lower than normal, the amount of mitosis induced is small and may decrease proliferative activity in breast epithelial cells (14–17), with a lowering of the risk of breast cancer development. An alternative mechanism by which obesity may reduce the risk of breast cancer in premenopausal women is through lower average endogenous estrogen levels (18, 19). However, this parallels the lower progesterone levels found in this category of women too (14). Other endocrine and metabolic changes associated with obesity and thought to be protective against breast cancer in the premenopausal woman include hyperinsulinaemia, and increased serum testosterone levels. Hyperinsulinaemia may act directly through insulin or through the insulin growth factor I (IGF-I) to interfere with ovarian follicular maturation and sex steroid formation. It may thus lead to irregular menstrual periods and increased ovarian androgen secretion. IGF-I may also have a direct mitogenic effect on the breast ductal epithelium and act in a paracrine fashion in consonance with IGF-II, both of which are produced in breast stromal cells (20). In postmenopausal women on the other hand, obesity has been reported to be associated with an increased risk of breast cancer (21). Post-menopause, androgens are aromatized to estrogen in adipose tissue and more of this may occur in obese rather than non-obese women (22). Obese postmenopausal women also have lower mean levels of sexhormone binding globulin that may contribute to higher availability of estrone at the tissue level (20). Hyperinsulinaemia and high levels of IGF-I in this situation may lead

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TABLE 3. Predictors of breast cancer among premenopausal women, Nigeria, 1998–2000 Means (S.D.), numbers (N), percentages (%), age adjusted odds ratio, 95% confidence interval, and p-value for cases and controls Cases N  120 mean (SD) N (%)

Predictor Age (years) Total number of pregnancies Age at menarche (years) Age at first full-term pregnancy (years) Height (cm) Weight (kg) BMI (unit/kg/m2) Family history of breast cancer Ever had irregular period Obesity  (BMI  30) Ever breast fed (among parous women) Ever used contraceptive pill Social status

Yes No Yes No Yes No Yes No Yes No Low Middle High

38.34 (6.55) 4.41 (2.29) 15.01 (1.88) 23.80 (4.30) 160.50 (8.62) 64.25 (14.51) 24.76 (5.55) 11 (9.17) 109 (90.83) 9 (7.50) 111 (92.50) 29 (24.17) 91 (75.83) 102 (85.71) 17 (14.29) 53 (45.30) 64 (54.70) 54 (46.15) 60 (51.28) 3 (2.56)

Controls N  120 mean (SD) N (%)

Odds ratio

34.23 (8.80) 3.82 (2.69) 15.51 (2.30) 22.54 (3.70) 159.38 (7.17) 65.52 (15.28) 23.65 (4.85) 14 (7.78) 166 (92.22) 1 (0.56) 179 (99.44) 25 (13.89) 155 (86.11) 133 (73.89) 47 (26.11) 62 (34.44) 118 (65.56) 98 (54.75) 80 (44.69) 1 (0.56)

1.07 0.90 0.86 1.09 1.03 1.01 1.02 1.37 1.00 14.51 1.00 1.52 1.00 1.04 1.00 0.84 1.00 1.00 1.36 5.44

95% CI

P-value

1.04, 1.10 0.79, 1.03 0.77, 0.97 1.02, 1.16 1.00, 1.07 0.99, 1.03 0.97, 1.07 0.58, 3.22

 0.01* 0.13 0.01* 0.01* 0.07 0.19 0.54 0.48

1.81, 116.11

 0.01*

0.82, 2.82

0.18

0.50, 2.18

0.91

0.51, 1.39

0.50 0.15

0.85, 2.18 0.55, 53.62

* Indicates statistical significance at p  0.05.

to higher ovarian androgen production, which increases the amount of substrates available for local fatty tissue aromatization (21). Despite these proposed mechanisms, this study, as well as some others have not found an association between obesity and pre- or post-menopausal breast cancer. Our findings are similar to that of Adams-Campbell, et al. who studied 193 African-American breast-cancer cases and controls and did not find any association (23); neither did Hall, et al. in the Carolina Breast Cancer Study (24). These findings may be due to lack of power, selection bias or confounding in the case control studies, or failure to ascertain body mass index at an etiologically appropriate period. In contrast, studies done in predominantly Caucasian populations have demonstrated a protective effect of obesity in premenopausal women and the increased risk of breast cancer in postmenopausal obese women (20, 25). Increasing height has also been associated with a higher risk of breast cancer (26) and height is believed to reflect nutritional status in childhood and adolescence (27). Animal studies have shown a consistent association between energy restriction and reduced risk of experimentally induced and spontaneous breast cancer (28). In developing countries like Taiwan (29); among European populations who were exposed to energy restriction in the years after the Second World War (30); among African-Americans (31) and in US studies with significant representation of women who were potentially undernourished during child-

hood and adolescence (32), increasing height has been associated with increased risk of breast cancer. However, studies in affluent societies where height is more likely to be related to genetic rather than nutritional factors have also shown an association between increasing height and increased breast cancer risk (33). Therefore, the association between height and breast cancer risk is likely to be a result of more complex interaction between genetic and environmental factors (34). In contrast to our findings on body mass index, we found an association between height and the risk of breast cancer in both pre- and post-menopausal women. This is consistent with the literature, as well as the finding in this study, that age at onset of menarche, another factor that is possibly related to differential energy intake in adolescence, is also related to the risk of breast cancer. Cost, lack of infrastructure, lack of personnel and competing health problems have hampered the investigation of breast cancer as well as other chronic non-communicable diseases in Africa, and there are no large prospective cohort studies of Africans. In view of this, carefully conducted case-control studies are likely to be the main source of information about breast cancer in Africa for some time to come. In this study, cases were hospital-based while the controls were community-based, and though an attempt was made to reduce systematic differences between the cases and controls by restricting the analysis of cases to those who reported that they had spent most of their lives in the city, there may be residual error due to lack of strict

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TABLE 4. Predictors of breast cancer among postmenopausal women, Nigeria, 1998–2000 means (S.D.), numbers (N), percentages (%), age adjusted odds ratio, 95% confidence interval, and p-value for cases and controls

Predictor Age (years) Total number of pregnancies Age at menarche (years) Age at first full term pregnancy (years) Age at onset of natural menopause (years) Height (cm) Weight (kg) BMI (unit/kg/m2) Family history of breast cancer Ever had irregular period Obesity (BMI  30) Ever breast fed (among parous women) Ever used contraceptive pill Social status

Cases N  104 mean (SD) N (%)

Control N  89 mean (SD) N (%)

Odds ratio

95% CI

P-value

53.62 (9.55) 5.99 (2.21)

58.42 (7.92) 6.75 (2.24)

0.94 0.89

0.09, 0.97 0.71, 1.02

 0.01* 0.10

15.58 (2.11)

16.35 (2.72)

0.89

0.78, 1.02

0.08

23.34 (4.23)

22.99 (4.20)

0.98

0.92, 1.06

0.65

47.37 (6.49)

50.02 (5.59)

0.97

0.91, 1.03

0.35

159.37 (6.66) 65.52 (15.28) 25.71 (5.86) 7 (6.73) 97 (93.27) 6 (5.77) 98 (94.23) 31 (29.81) 73 (70.19) 101 (97.12) 3 (2.88) 34 (34) 66 (66) 49 (47.12) 47 (45.19) 8 (7.69)

155.99 (9.33) 62.32 (13.66) 25.39 (5.63) 8 (8.99) 81 (91.01) 1 (1.12) 88 (98.88) 16 (17.98) 73 (82.02) 89 (100) 0 (0) 25 (28.09) 64 (71.91) 57 (64.77) 31 (35.23) 0

1.07 1.02 1.02 0.70 1.00 5.39 1.00 2.01 1.00 

1.02, 1.13 1.00, 1.04 0.97, 1.08 0.23, 2.09

0.01* 0.07 0.48 0.52

0.64, 45.63

0.09

1.00, 4.09

0.05**





0.96 1.00 1.00 1.79

0.50, 1.84

0.90

0.97, 3.19

0.06





Yes No Yes No Yes No Yes No Yes No Low Middle High

* Indicates statistical significance at p  0.05. ** Borderline statistical significance.  Values could not be computed because of zero-cell.

comparability between the two groups. It is also possible that poorer patients who may have low body mass index on account of chronic malnutrition may not have presented to hospital for diagnosis and treatment. Many of the cases had advanced breast cancer at presentation with long duration of symptoms. Obese patients are known to have poorer prognosis even after controlling for tumor size and stage of disease (35). This is particularly so among premenopausal women and it is conceivable that some of them did not present to hospital because of the short duration of survival (36). Body mass index was determined at diagnosis among the cases. London, et al. have shown that pre-clinical weight loss is not a problem in breast cancer (37); though this is consistent with our clinical experience, the majority of the cases in London’s study had early breast cancer at diagnosis in contrast to the patients included in this study, so the possibility of systematic error from this source cannot be eliminated. Family history of breast cancer is one of the strongest risk factors associated with increased risk of breast cancer (38). In this study, we did not find an association. This may be related to social and cultural phenomena peculiar to this environment. The average life expectancy for women in

Nigeria for the year 2000 is 52 years (39). Therefore many women may not be living long enough to develop and present with breast cancer. Also, a diagnosis of breast cancer still evokes fear and is not discussed with members of the family. It is therefore conceivable that some members of the population may not be aware of a diagnosis of breast cancer even in close relatives (1). In conclusion, the risk factors for breast cancer in Nigeria do not appear to be significantly different from that reported in other populations. This study suggests that many issues such as family history and menstrual irregularity require further examination to ascertain the state of affairs. The authors thank Michelle D. Holmes, MD, Dr.P.H., and Susan Collila, Ph.D., for reading the draft and making suggestions, and Ms. Funmi Oyetunji and Ms. Fisayo Adejuyigbe for conducting the interviews. Supported in part through a generous grant from the Falk Medical Research Trust, Olufunmilayo I. Olopade is a Doris Duke Distinguished Clinical Scholar.

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