Anthropometric factors, including A body shape index (ABSI), and the risk of breast cancer among Nigerian women: a case-control study
Samuel O Azubuike1a, Louise Hayes2b, Richard Mcnally2c and Linda Sharp2d
1Department of Public Health, National Open University of Nigeria, Abuja 900108, Nigeria
2Population Health Sciences Institute, Newcastle University, NE2 4HH Newcastle Upon Tyne, UK
a https://orcid.org/0000-0002-2794-4103
b https://orcid.org/0000-0001-6442-4150
c https://orcid.org/0000-0001-6685-6467
d https://orcid.org/0000-0001-9515-1722
Abstract
Breast cancer (BC) incidence is increasing in Nigeria, alongside rising levels of obesity and central adiposity. While body mass index (BMI) is commonly used to assess adiposity, it may inadequately capture fat distribution relevant to BC risk. This study examined the associations between multiple anthropometric measures – including BMI, waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), weight and A Body Shape Index (ABSI) – and BC risk among Nigerian women, and evaluated whether WHR and ABSI provide additional predictive value beyond BMI. A hospital-based case-control study was conducted in five public hospitals in Lagos and Abuja, Nigeria, involving 379 women with histologically confirmed BC and 403 cancer-free controls. Standardised protocols were used to obtain anthropometric measurements. WHR was calculated as WC divided by HC, BMI as weight divided by height squared and ABSI as WC/(BMI²⁄³ × height¹⁄²). Information on socio-demographic, reproductive and lifestyle factors was collected through semi-structured interviews. Multivariable unconditional logistic regression models were used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs), with analyses stratified by menopausal status and trend tests conducted across tertiles. Among premenopausal women, a high WHR was associated with an increased risk of BC (OR 2.26, 95% CI: 1.12–4.58), whereas higher BMI was inversely associated with risk (OR 0.44, 95% CI: 0.20–0.96). ABSI demonstrated a modest positive trend across tertiles among all women (p-trend = 0.038). No statistically significant associations were observed between anthropometric measures and BC risk among postmenopausal women. These findings suggest that measures of central adiposity, particularly WHR, may be more relevant than BMI for assessing BC risk among premenopausal Nigerian women. Interventions aimed at reducing visceral fat accumulation may contribute to lowering the burden of early-onset BC in this setting.
Keywords: breast neoplasms, anthropometry, body mass index, body shape index, case-control studies, Sub-Saharan Africa
Correspondence to: Samuel O Azubuike
Email: samonaz2000@yahoo.com
Published: 08/07/2026
Received: 05/01/2026
Publication costs for this article were supported by ecancer (UK Charity number 1176307).
Copyright: © the authors; licensee ecancermedicalscience. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Anthropometric measures such as obesity have been increasing across Africa, including Nigeria, since 1990 [1, 2], and are expected to rise further due to urbanisation, Westernized diets and ongoing nutrition transitions [2–5]. The incidence of breast cancer (BC) in Nigeria has been increasing since 1976, with disproportionately high mortality [6]. Previous studies in Nigeria have examined associations between anthropometric measures, including body mass index (BMI), waist-to-hip ratio (WHR), height, hip circumference (HC) and waist circumference (WC) and BC risk, but findings have been inconsistent [7–11]. Many studies also lacked adjustment for key confounders, such as physical activity (PA).
This study investigated the associations between BC risk and BMI, WHR, WC, HC, weight and, for the first time in Africa, A Body Shape Index (ABSI). ABSI is an emerging marker of adiposity derived from WC, BMI and height, and has been linked to all-cause mortality and cancer risk in U.S. and European populations [12, 13]. Unlike BMI, which does not distinguish lean mass from adiposity or reflect fat distribution [12, 14], ABSI provides a measure of adiposity independent of BMI while accounting for both general and central adiposity [12, 14, 15]. To the best of our knowledge, the role of ABSI with respect to BC risk has not been previously investigated in sub-Saharan Africa.
Material and methods
Using a case–control design, participants were recruited from four public tertiary hospitals – University of Lagos Teaching Hospital, Lagos State University Teaching Hospital, University of Abuja Teaching Hospital, Gwagwalada and National Hospital Abuja – and one secondary health facility, General Hospital Lagos Island (GHLI).
Lagos, located in southern Nigeria, is the largest city in sub-Saharan Africa, with an estimated population exceeding 12.5 million in 2016 [16]. Abuja, in northern Nigeria, is the Federal Capital Territory and one of the fastest-growing cities globally, with a population of over 3.5 million in 2016 [17]. These cities were selected to enhance the external validity of the study due to their marked ethnic and socioeconomic diversity [18].
In Nigeria, over 86% of BC cases are diagnosed in tertiary health facilities, which also serve as referral centres for private hospitals and lower levels of care because of better diagnostic capacity and specialised personnel. A secondary health facility was therefore included to improve the generalisability of the study findings.
Recruitment of participants
Eligible cases were women aged 20–80 years with a histologically confirmed diagnosis of invasive BC who attended oncology clinics in the participating hospitals between October 2016 and May 2017. Women whose diagnostic results were more than 18 months before the interview date were excluded.
Controls were recruited from outpatient ophthalmology clinics in the same hospitals during the same period. The ophthalmology departments provide comprehensive preventive, curative and rehabilitative eye care services and therefore attract patients from diverse socioeconomic backgrounds. Eligible controls were female ophthalmology patients or female relatives (visitors) of patients [19], aged 20–80 years, with no personal history of BC or benign breast disease. Where a patient’s close relative was selected as a control, the patient was excluded and vice versa, to minimise similarity in exposure patterns between cases and controls.
All participants were assessed by collaborating physicians as physically and psychologically fit to participate. These physicians were not involved in interviewing to minimise interviewer bias.
The study objectives and procedures were explained to potential participants during clinic hours. Trained interviewers comprising doctors, nurses and graduates of related disciplines – subsequently approached interested individuals in the clinic waiting areas to confirm eligibility and obtain consent. Cases were recruited sequentially according to their listing in the daily oncology clinic attendance register, in line with departmental regulations that restricted patient contact to clinic hours only. As access to attendance registers was not permitted in ophthalmology clinics, controls were approached based on order of arrival and seating position in the waiting areas.
Data collection procedures
All eligible participants who provided written or oral informed consent were interviewed face-to-face using a semi-structured questionnaire. The questionnaire was developed specifically for this study, drawing on previously validated instruments and adapted to the local context. Information was collected on demographic, socioeconomic, reproductive and lifestyle variables. The questionnaire was pretested among 17 participants at GHLI, and necessary modifications were made before data collection.
Interviewers were selected to ensure proficiency in English, relevant local languages and Nigerian Pidgin English. They received 2–4 hours of training before the study, including recorded mock interviews, covering effective interview techniques and standardised anthropometric measurements. Completed questionnaires were reviewed daily for completeness and accuracy, and where feasible, participants were re-contacted to address missing or inconsistent information.
Measurement of anthropometric variables
For cases, weight and height were measured by trained interviewers using a calibrated weighing scale and a mobile stadiometer. In each center, measurements were assigned to one interviewer to minimise interobserver bias. A minimum of two measurements were obtained for each subject, and where there were variations (> 0.5 kg), a third measurement was taken. The weight was measured with the scale at the zero reading [20, 21]. Participants removed their shoes, outer coats and any other external objects. They were measured with both feet on the platform, with their arms naturally hanging downward, looking forward and remaining still. Readings were taken to the nearest 0.1 kg. For the height measurement, participants removed their shoes and head ties where possible. The body’s mid-axillary line was parallel to the stadiometer while the head position was in the Frankfort plane. The headpiece was lowered until it touched the crown of the head, compressing the hair. The measurement was read to the nearest 0.1 cm.
The WC and HC of all participants were measured to the nearest 0.1 cm using a SECA 203 ergonomic tape. The WC was taken at a position slightly above the umbilicus (midpoint between the lower margin of the least palpable rib and the top of the iliac crest). The HC was measured at the widest portion of the buttocks. The WHR was obtained by computing the ratio of the WC and HC. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m2). ABSI was computed using the formula ABSI =WC/(BMI2/3 height1/2) [12, 13].
Confounding variables
Variables were considered confounders if they had been consistently identified as such in existing literature. These included family history of BC, ethnicity, income, educational attainment, urbanicity, menopausal status, parity, age at first birth, lifetime duration of breastfeeding, age at menarche, oral contraceptive use, PA and alcohol consumption.
Anthropometric variables entered into the models were weight, height, WC, HC, BMI and WHR. For these variables, inclusion was guided by statistical criteria: variables with likelihood ratio test p-values <0.20 or those that changed the effect estimate by more than 10% upon removal from the model were retained as covariates.
Statistical analysis
All anthropometric variables (with the exception of BMI and WHR) were divided into three approximately equal-sized categories (tertiles) based on their distributions among controls. For BMI, the WHO cutoff points for normal weight, overweight and obesity (<24.99, 25.00–29.99 and ≥30.00 kg/m2) were used. Notably, the values approximately coincided with our cutoff points for BMI when categorised into tertiles. For the WHR, a 0.85 lower cutoff was applied, which is consistent with the WHO recommendation for defining women with a substantially increased risk of metabolic disorders [22]. While the upper categorical values of BMI (≥30 kg/M2), HC (≥105 cm) and body weight(≥77 kg) were used as indicators of general obesity, the upper values of WHR (≥0.90) and WC (≥91.40 cm) and ABSI (≥0.077) were used as indicators of central obesity – fat accumulation around the abdominal or visceral regions of the body. The ABSI is considered a measure of abdominal and overall distribution of adiposity. Notably, there is currently no consensus on the appropriate cutoffs for defining central (abdominal) obesity or general obesity among the African population [22]. We analysed data for all women and subgroups comprising women of reproductive age (< 50 years), women aged ≥50 years, premenopausal women and postmenopausal women, consistent with previous indigenous and international studies [23–25].
The distributions of these anthropometric variables were compared between the patients and controls. Categorical data were compared using chi-square (χ2) tests. The associations between the anthropometric variables of interest and BC were modeled using unconditional binary logistic regression. Unconditional binary logistic regression is recommended to be more appropriate for frequency-matched data than for individually matched data, for which conditional logistic regression is considered more appropriate [26, 27]. The analysis was performed with the Statistical Product for Service Solutions version 25. Multicollinearity was assumed to exist if the tolerance value for each continuous variable was >0.1 and the variance inflation factor was <10 [28].
The relationship between BC risk and each anthropometric variable was investigated using three models adjusted for relevant explanatory variables selected based on existing literature. In the first model (the minimally adjusted model), age (as a continuous variable) and study site were included as a priori covariates, regardless of statistical significance. This approach was employed to account for the frequency matching by age between cases and controls and to enhance the external validity of the findings.
The relevant potential confounders were adjusted in the second model (core confounder-adjusted model) if they met the criteria for adjustment. These variables selected a priori were education (nonformal/primary, secondary, postsecondary, first degree/higher national diploma-HND and > first degree), income (< ₦18,000[$51.4]; ₦18,000- ₦49,000[$140]; ₦50,000[$142.9] - ₦100,000[$285.7]; > ₦100,000), parity (continuous variable), age at first birth (continuous variable), menopausal status (premenopausal and postmenopausal), total months of breastfeeding (continuous), age at menarche (≤ 13 years, >13 years), oral contraceptive use (Yes, No), family history of BC (Yes, No), alcohol consumption (Yes, No) and total PA (tertiles). Other relevant anthropometric covariates (weight, height, WC, HC, BMI and WHR) were adjusted in the third model (fully adjusted model). A p value <0.05 (two-sided) was considered to indicate statistical significance for trend tests across variable categories. p = values were based on the likelihood ratio test. The significance of the odds ratios (ORs) reported for the categorical variables were based on 95% confidence intervals (CIs). Although missing data were not included in the percentage calculations; however, they were reported separately to allow a transparent assessment of response completeness across variables.
Sensitivity analysis
The original study design was primarily intended to include only patients diagnosed within 1 year before enrollment and controls recruited exclusively from the ophthalmology departments of the participating hospitals. To assess whether relaxing these criteria as contained in the study protocol affected the findings, a sensitivity analysis was conducted. This analysis excluded patients diagnosed between 12 and 18 months before enrollment and controls from the general outpatient department (GOPD) initially included to meet sample size requirements. Results of this sensitivity analysis are presented in Supplementary Table 1.
Results
Recruitment result and distribution of participants characteristics
A total of 379 cases and 403 controls were recruited. Among these, 62 cases (16.4%) were diagnosed between 12 and 18 months before the interview, while 16 controls (4%) were recruited from the GOPD. Differences in the proportions of participants with various characteristics between cases and controls are reported in Supplementary Table 2. The unadjusted result showed a significant difference in the proportion of the anthropometric measures between cases and controls with regard to ABSI and WHR (Table 1). However, women in the upper WHR category had a significantly greater risk of BC than women in the lowest WHR category did (Table 1).
Table 1. Relationship between anthropometric factors and BC risk (unadjusted analysis).
Table 2. Adjusted associations between anthropometric variables and BC risk among women of all ages (multiple regression).
Overall associations (all women)
According to Table 2, after adjusting for potential confounders, differences in weight and WC were not significantly associated with BC risk. There was no statistically significant difference in BC risk between taller women (≥1.64 m) and shorter women (<1.58 m). Women in the upper tertile of HC had a significantly reduced risk compared with those in the lower tertile (OR 0.51, 95% CI: 0.27–0.95; p for trend = 0.005). High BMI (≥30 kg/m²) was not associated with increased risk in the overall population. High WHR showed a nonsignificant increased risk after full adjustment (OR 1.50, 95% CI: 0.92–2.45). Trends across ABSI categories were significant in minimally adjusted models (p = 0.041) and remained significant when BMI was included (p = 0.038).
Premenopausal women
Among premenopausal women (Table 3), high BMI (≥30 kg/m²) was associated with a significantly reduced BC risk compared with BMI <25 kg/m² (OR 0.44, 95% CI: 0.20–0.96). Women with high WHR (>0.9) had a significantly increased risk compared with low WHR (<0.85) (OR 2.36, 95% CI: 1.12–4.58). HC in the upper tertile was associated with reduced risk compared to observations in the lowest tertile (OR 0.39, 95% CI: 0.16–0.97; p for trend = 0.009). These patterns were consistent among younger women (<50 years: OR 0.32, 95% CI: 0.13–0.77) (Supplementary Table 3).
Postmenopausal women
Among postmenopausal women (Table 4), high BMI was not significantly associated with BC risk (OR 1.25, 95% CI: 0.58–2.69). A similar observation was made among older women (Supplementary Table 4). Height was significantly associated with risk only when categorised in quartiles (Q4 versus Q1: OR 2.31, 95% CI: 1.01–5.32; Supplementary Table 5). WHR did not show a statistically significant association (OR 1.27, 95% CI: 0.65–2.51), and trends in ABSI were not significant in this subgroup. HC was not significantly associated with BC among postmenopausal or older women (Supplementary Table 4).
Sensitivity analysis
Excluding patients diagnosed between 12 and 18 months and controls from the GOPD did not materially change the results, indicating that the findings are robust (Supplementary Table 1)
Discussion
Key findings
We observed that high BMI and HC were associated with a reduced risk of BC, particularly among premenopausal and younger women. In contrast, high WHR was associated with increased risk in these subgroups. Increased adult height was associated with a higher risk among postmenopausal and older women. Although the upper tertile of ABSI was not significantly associated with BC in all women, a significant effect across ABSI categories was observed. High body weight and WC were not significantly associated with risk, regardless of age or menopausal status.
Comparison with previous studies
An increased risk of BC with higher WHR has been reported in previous indigenous studies [8]. We extended these findings by showing that this association persisted after adjustment for multiple confounders, including total PA and BMI, particularly among premenopausal and younger women. The lack of association among postmenopausal women may reflect sample size limitations. Findings from other regions have been inconsistent [24, 29–33], with some prospective studies based on self-reported body size showing nonsignificant associations [30–32].
Table 3. Adjusted associations between anthropometric variables and BC risk among premenopausal women (multiple regression).
Table 4. Adjusted associations between anthropometric variables and BC risk among postmenopausal women (multiple regression).
African studies on BMI and BC risk have been contradictory. Some studies based on self-reported body size reported increased risk with obesity [34, 35], whereas studies using objective measurements reported reduced risk, consistent with our findings [7, 9]. While one prior study reported a nonsignificant reduced risk among postmenopausal women, our analysis suggested that BMI effects were largely explained by adjusted covariates [7]. International studies, including IARC reports, indicate that obesity reduces BC risk in premenopausal women but increases it in postmenopausal [36–39]. The nonsignificant increase among older women in our study may reflect the limited sample size.
Our observations on height, body weight and WC were similar to prior studies in Nigeria and internationally, though not statistically significant for all women [7, 8, 32, 40]. Adult height is a recognised risk factor for BC [41], and we observed this association among postmenopausal and older women, highlighting the need for replication in larger samples. Women in the high ABSI category showed a potential increase in BC risk, with a significant association observed only in the trend analysis. Similar associations have been reported in the UK and USA [12, 42].
Implications for the role of central obesity compared to that of general obesity
ABSI behaves similarly to WHR, reflecting central (abdominal) fat accumulation rather than general adiposity. Since BMI does not distinguish lean mass from fat or bone [14, 43], observations suggesting protective effects of obesity based on BMI, especially in premenopausal women, should be interpreted cautiously. Nevertheless, the study is the first African investigation of ABSI and BC, warranting further research. The study findings suggest that anthropometric factors influence BC risk differently by age, menopausal status and fat distribution. General obesity (BMI) may not accurately predict the risk of BC associated with obesity in our population, whereas central obesity (WHR, ABSI) may increase vulnerability, particularly among women of reproductive age [44]. Laboratory studies support central obesity as a stronger predictor than general obesity among premenopausal Black women [45]. This is particularly relevant in sub-Saharan Africa, given its predominantly young population.
Potential biological plausibility
The role of general obesity in postmenopausal BC is likely mediated by estrogen, produced primarily by adipose tissue after menopause [30, 46, 47]. Abdominal obesity may influence risk through insulin, IGF and leptin pathways, especially in premenopausal women [48]. Height likely reflects cumulative genetic and early-life nutritional exposures, supporting a life-course approach to BC prevention [41].
Strengths and limitations
Strengths of this study include objective anthropometric measurements, rigorous confounder adjustment and inclusion of participants from both southern and northern Nigeria, enhancing generalisability [7, 8]. Comparisons with national data on wealth, parity and age at first birth further support representativeness [49]. Limitations include a small sample size among older/postmenopausal women, lack of dietary data and potential reverse causation due to measurements after BC diagnosis [50]. Moreover, we did not carry out any independent investigation to confirm the BC diagnosis status of controls. Hence, we may not rule out the presence of occult or BC.
Conclusion
This study found that body fat distribution, particularly WHR, was more strongly associated with BC risk than BMI, with ABSI showing complementary value. Central adiposity was especially relevant in premenopausal women, suggesting that interventions targeting visceral fat may help reduce early-onset BC risk in Nigerian populations.
Conflicts of interest
The authors declare no conflicts of interest.
Funding
The study was partially funded by the National Open University of Nigeria (specifically for data collection).
References
1. Abarca-Gómez L, Abdeen ZA, and Hamid ZA, et al (2017) Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults Lancet 390(10113) 2627–2642 https://doi.org/10.1016/S0140-6736(17)32129-3
2. Agyemang C, Boatemaa S, and Frempong GA, et al (2016) Obesity in Sub-Saharan Africa Metabolic Syndrome: A Comprehensive Textbook ed RS Ahima (Cham: Springer International Publishing) pp 41–53 [https:doi.org/10.1007/978-3-319-11251-0_5]
3. Akarolo-Anthony SN, Ogundiran TO, and Adebamowo CA (2010) Emerging breast cancer epidemic: evidence from Africa Breast Cancer Res 12(4) S8–S11 https://doi.org/10.1186/bcr2737 PMCID: 3005728
4. Akarolo-Anthony SN, Willett WC, and Spiegelman D, et al (2014) Obesity epidemic has emerged among Nigerians BMC Public Health 14(1) 455 https://doi.org/10.1186/1471-2458-14-455 PMID: 24886022 PMCID: 4060067
5. Nel JH and Steyn NP (2022) The nutrition transition and the double burden of malnutrition in Sub-Saharan African Countries: how do these countries compare with the recommended LANCET COMMISSION global diet? Int J Environ Res Public Health 19(24) 24167 [10.3390/ijerph192416791] https://doi.org/10.3390/ijerph192416791
6. Azubuike SO, Muirhead C, and Hayes L, et al (2018) Rising global burden of breast cancer: the case of sub-Saharan Africa (with emphasis on Nigeria) and implications for regional development: a review World J Surg Oncol 16(1) 63 https://doi.org/10.1186/s12957-018-1345-2 PMID: 29566711 PMCID: 5863808
7. Ogundiran TO, Huo D, and Adenipekun A, et al (2010) Case–control study of body size and breast cancer risk in Nigerian women Am J Epidemiol 172(6) 682–690 https://doi.org/10.1093/aje/kwq180 PMID: 20716701 PMCID: 2950817
8. Ogundiran TO, Huo D, and Adenipekun A, et al (2012) Body fat distribution and breast cancer risk: findings from the Nigerian breast cancer study Cancer Causes Control 23(4) 565–574 PMID: 22367701 PMCID: 4129957
9. Okobia MN, Bunker CH, and Zmuda JM, et al (2006) Anthropometry and breast cancer risk in Nigerian women Breast J 12(5) 462–466 https://doi.org/10.1111/j.1075-122X.2006.00304.x PMID: 16958966
10. Adebamowo CA, Ogundiran TO, and Adenipekun AA, et al (2003) Waist–hip ratio and breast cancer risk in urbanized Nigerian women Breast Cancer Res 5(2) R18 https://doi.org/10.1186/bcr567 PMID: 12631394 PMCID: 154145
11. Akinyemiju T, Jones K, and Gupta A, et al (2021) Association of body composition with odds of breast cancer by molecular subtype: analysis of the Mechanisms for Established and Novel Risk Factors for Breast Cancer in Nigerian Women (MEND) study BMC Cancer 21(1) 1051 [10.1186/s12885-021-08775-8] https://doi.org/10.1186/s12885-021-08775-8 PMID: 34563146 PMCID: 8464100
12. Parra -Soto S, Malcomson FC, and Ho FK, et al (2021) Associations of A Body Shape Index (ABSI) with Cancer incidence, all-cause, and at 23 sites—findings from the UK Biobank prospective cohort study Cancer Epidemiol Prevention Biomarkers
13. Krakauer NY and Krakauer JC (2012) A new body shape index predicts mortality hazard independently of body mass index PLoS One 7(7) e39504 https://doi.org/10.1371/journal.pone.0039504 PMID: 22815707 PMCID: 3399847
14. Bouchi R, Asakawa M, and Ohara N, et al (2016) Indirect measure of visceral adiposity ‘A Body Shape Index’ (ABSI) is associated with arterial stiffness in patients with type 2 diabetes BMJ Open Diabetes Res Care 4(1) e000188 https://doi.org/10.1136/bmjdrc-2015-000188 PMID: 27026809 PMCID: 4800068
15. Christakoudi S, Tsilidis KK, and Muller DC, et al (2020) A Body Shape Index (ABSI) achieves better mortality risk stratification than alternative indices of abdominal obesity: results from a large European cohort Sci Rep 10(1) 1–15 https://doi.org/10.1038/s41598-020-71302-5
16. Hoornweg D and Pope K (2017) Population predictions for the world’s largest cities in the 21st century Environ Urbanization 29(1) 195–216 [10.1177/0956247816663557] https://doi.org/10.1177/0956247816663557
17. The United Nations The World’s Cities in 2018 [https://www.un.org/en/events/citiesday/assets/pdf/the_worlds_cities_in_2018_data_booklet.pdf] Date accessed: 05/07/20
18. New World Encyclopedia Abuja [www.new worldencyclopedia.org/entry/Abuja]
19. Azubuike SO, Hayes L, and Sharp L, et al (2022) Physical activity and the risk of breast cancer among Nigerian women Cancer Epidemiol 78 102163 [doi:10.1016/j.canep.2022.102163] https://doi.org/10.1016/j.canep.2022.102163 PMID: 35462345
20. Sánchez-García S, García-Peña C, and Duque-López MX, et al (2007) Anthropometric measures and nutritional status in a healthy elderly population BMC Public Health 7(1) 2 https://doi.org/10.1186/1471-2458-7-2 PMID: 17201919 PMCID: 1769489
21. Center for Disease Control National Health and Nutrition Examination Survey: Anthropometry Procedures Manual [https://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf]
22. WHO (2011) Waist Circumference and Waist-Hip Ratio: report of a WHO Expert Consultation, Geneva, 8-11 December 2008 (Geneva: WHO)
23. Figueroa JD, Davis Lynn BC, and Edusei L, et al (2020) Reproductive factors and risk of breast cancer by tumor subtypes among Ghanaian women: a population‐based case–control study Int J Cancer 147(6) 1535–1547 https://doi.org/10.1002/ijc.32929 PMID: 32068253 PMCID: 8380990
24. Hall IJ, Newman B, and Millikan RC, et al (2000) Body size and breast cancer risk in black women and white women: the Carolina Breast Cancer Study Am J Epidemiol 151(8) 754–764 https://doi.org/10.1093/oxfordjournals.aje.a010275 PMID: 10965972
25. Nemesure B, Wu SY, and Hennis A, et al (2009) Body size and breast cancer in a black population—the Barbados National Cancer Study Cancer Causes Control 20(3) 387–394 PMCID: 2659612
26. Rothman KJ, Greenland S, and Lash TL (2008) Modern epidemiology vol 3 (Wolters Kluwer Health/Lippincott Williams & Wilkins Philadelphia)
27. Kuo CL, Duan Y, and Grady J (2018) Unconditional or conditional logistic regression model for age-matched case‒control data? Front Public Health 6 57 https://doi.org/10.3389/fpubh.2018.00057
28. Field A (2009) Discovering Statistics Using SPSS 4th edn (Sage Publications)
29. Connolly BS, Barnett C, and Vogt KN, et al (2002) A meta-analysis of published literature on waist-to-hip ratio and risk of breast cancer Nutr Cancer 44(2) 127–138 https://doi.org/10.1207/S15327914NC4402_02
30. Bandera EV, Chandran U, and Zirpoli G, et al (2013) Body fatness and breast cancer risk in women of African ancestry BMC Cancer 13(1) 475 https://doi.org/10.1186/1471-2407-13-475 PMID: 24118876 PMCID: 3853021
31. Lahmann PH, Hoffmann K, and Allen N, et al (2004) Body size and breast cancer risk: findings from the European Prospective Investigation into Cancer and Nutrition (EPIC) Int J Cancer 111(5) 762–771 https://doi.org/10.1002/ijc.20315 PMID: 15252848
32. Palmer JR, Adams-Campbell LL, and Boggs DA, et al (2007) A prospective study of body size and breast cancer in black women Cancer Epidemiol Prevention Biomarkers 16(9) 1795–1802 https://doi.org/10.1158/1055-9965.EPI-07-0336
33. Al-Ajmi K, Lophatananon A, and Ollier W, et al (2018) Risk of breast cancer in the UK Biobank female cohort and its relationship to anthropometric and reproductive factors PLoS One 13(7) 201097 https://doi.org/10.1371/journal.pone.0201097
34. Brinton LA, Awuah B, and Nat Clegg‐lamptey J, et al (2017) Design considerations for identifying breast cancer risk factors in a population-based study in Africa Int J Cancer 140(12) 2667–2677 https://doi.org/10.1002/ijc.30688 PMID: 28295287 PMCID: 5926189
35. Adebamowo CA, Ogundiran TO, and Adenipekun AA, et al (2003) Obesity and height in urban Nigerian women with breast cancer Ann Epidemiol 13(6) 455–461 https://doi.org/10.1016/S1047-2797(02)00426-X PMID: 12875805
36. World Cancer Research Fund/American Institute for Cancer Research Diet, Nutrition, Physical Activity and Cancer: a Global Perspective [https://www.wcrf.org/diet-and-cancer/]
37. Van Den Brandt PA (2000) Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk Am J Epidemiol 152(6) 514–527 https://doi.org/10.1093/aje/152.6.514 PMID: 10997541
38. Schoemaker MJ, Nichols HB, and Wright LB, et al (2018) Association of body mass index and age with subsequent breast cancer risk in premenopausal women JAMA Oncol 4(11) e181771 https://doi.org/10.1001/jamaoncol.2018.1771 PMID: 29931120 PMCID: 6248078
39. Reeves GK, Pirie K, and Beral V, et al (2007) Cancer incidence and mortality in relation to body mass index in the Million Women Study: cohort study BMJ (Clin Res Ed) 335(7630) 1134 https://doi.org/10.1136/bmj.39367.495995.AE
40. Huang Z, Willett WC, and Colditz GA, et al (1999) Waist circumference, waist: hip ratio, and risk of breast cancer in the Nurses’ Health Study Am J Epidemiol 150(12) 1316–1324 https://doi.org/10.1093/oxfordjournals.aje.a009963 PMID: 10604774
41. World Cancer Research Fund/American Institute for Cancer Research Continuous Update Pane Project on Diet, Nutrition, Physical Activity and Breast Cancer Prevention 2017 [www.aicr.org/continuous-update-project/reports/breast-cancer-report-2017.pdf]
42. Kabat GC, Xue X, and Kamensky V, et al (2015) Risk of breast, endometrial, colorectal, and renal cancers in postmenopausal women in association with a body shape index and other anthropometric measures Cancer Causes Control 26(2) 219–229
43. Iyengar NM, Arthur R, and Manson JE, et al (2018) Association of body fat and risk of breast cancer in postmenopausal women with normal body mass index: a secondary analysis of a randomized clinical trial and observational study JAMA Oncol 5(2) 155–163 https://doi.org/10.1001/jamaoncol.2018.5327 PMID: 30520976 PMCID: 6439554
44. World Health Organization (2008) Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation, Geneva, 8-11 December 2008 (Geneva: WO) [https://apps.who.int/iris/bitstream/handle/10665/44583/9789241501491_eng.pdf]
45. Barnett JB, Woods MN, and Rosner B, et al (2001) Sex hormone levels in premenopausal African-American women with upper and lower body fat phenotypes Nutr Cancer 41(1-2) 47–56 https://doi.org/10.1080/01635581.2001.9680611
46. Anderson GL and Neuhouser ML (2012) Obesity and the risk for premenopausal and postmenopausal breast cancer Cancer Prevention Res 4(5) 515–521 https://doi.org/10.1158/1940-6207.CAPR-12-0091
47. Cleary MP and Grossmann ME (2009) Minireview: obesity and breast cancer: the estrogen connection Endocrinology 150(6) 2537–2542 [10.1210/en.2009-0070] https://doi.org/10.1210/en.2009-0070 PMID: 19372199 PMCID: 2689796
48. Harvie M, Hooper L, and Howell AH (2003) Central obesity and breast cancer risk: a systematic review Obesity Rev 4(3) https://doi.org/10.1046/j.1467-789X.2003.00108.x
49. National Population Commission (NPC) [Nigeria] and ICF International (2013) Nigeria Demographic and Health Survey 2013 (NPC and ICF International) [http://www.population.gov.ng/index.php/2013-nigeria-demographic-and-health-survey]
50. Sheean PM, Hoskins K, and Stolley M (2012) Body composition changes in females treated for breast cancer: a review of the evidence Breast Cancer Res Treatment 135(3) 663–680 https://doi.org/10.1007/s10549-012-2200-8
Supplementary Tables
Supplementary Table 1. Relationship between anthropometric factors and BC risk (Sensitivity analysis).
Supplementary Table 2. Participants’ characteristics.
Supplementary Table 3. Relationship between BC and anthropometric variables among women younger than 50 years (multiple regression).
Supplementary Table 4. Relationship between BC and anthropometric variables among women age 50 years and above (multiple regression).
Supplementary Table 5. Relationship between height and BC risk among women ≥50 years and postmenopausal women (based on quartile categorisation).
