- Research note
- Open Access
Long-term effects of aromatase inhibitors on body mass index among postmenopausal breast cancer survivors in Africa: observational cohort study
BMC Research Notes volume 16, Article number: 37 (2023)
this study was conducted to assess the impact of AIs on body mass index and high sensitivity as prognostic predictors to be incorporated into point of care technology (POCT) testing in postmenopausal breast cancer women after a 24 month follow up in Africa. An observational cohort study was conducted; including 126 female BC patients with stages ranging from 0-III initially subjected to AIs and subsequently followed up for 24 months. Multiple imputation model was conducted to predict missing data.
Random effects model was used to monitor the changes over the time. The study revealed stronger statistically association between BMI and homocysteine (p = 0.021, 95%CI: 0.0083 to 0.1029). Weight and total body fat were strongly associated after 24 months follow up. Hs-CRP was associated with BMI (p = 0.0001), and hs-CRP was associated with other biomedical markers such as calcium (p = 0.021, 95% CI: 0.01 to 0.10), phosphate (p = 0.039, 95%CI: 0.01 to 0.10), and ferritin (p = 0.002, 95%CI: 0.02 to 0.08) and calcium. The patients subjected to AIs are likely to develop cardiovascular adverse events. POCT of care strategy which include clinical, biomedical and genetic predictor’s measurement is required to improve BC survivorship.
Obesity and mediators of inflammation have been identified as the most important risk and predictive factors in postmenopausal breast cancer survivors using aromatase inhibitors (AIs) . According to Bardia et al., (2012), the 10-year predicted recurrence risk for cardiovascular disease (CVD) equals or exceeds that of BC in postmenopausal women . CVD-related endocrine therapies impact on health-related quality of life (HRQOL) in postmenopausal BCS [3,4,5,6,7,8,9]. Maintaining patient adherence is a critical situation experienced among BCS subjected to long-term endocrine therapies . Failure to follow-up and missing data in cancer survivors is well acknowledged; and as a result, there is increased morbidity and mortality . Given that the missing data containing known and unknown reasons are common in cancer survivors, using historical patient information stored in clinical databases is currently applied in computational science to predict patient outcomes using modeling approaches [12,13,14,15]. Common retrospective data collection platforms are characterized by incomplete useful data, and these challenges may lead to limitations in statistical analyses of patient prognoses . Researchers have recommended different statistic approaches, including advantages and disadvantages in order to estimate patient outcomes using mathematical modeling based on assumptions of both baseline data and literature . Data is lacking on the effects of AIs on clinical markers (e.g. BMI) and inflammatory markers (e.g. hs-CRP) in postmenopausal survivorship as a result of BC treatment strategies in the African setting. Therefore, this study was conducted to assess the impact of AIs on hs-CRP and BMI, as prognostic markers to be incorporated in POCT in postmenopausal BCS in Africa over a 24-month follow-up.
Methods and study design
A prospective observational cohort study non comparison group was conducted in this study. However, unlike a true experiment, a quasi-experiment does not rely on random assignment. Instead, subjects are assigned to groups based on non-random criteria. A longitudinal study can be used with different treatment strategies without control group. But the stratification was used in this study to minimize selection biases and effects of different treatment modalities. Breast cancer patients are likely to take different treatment strategies, using placebo is not ethically advised due to complexity of the breast cancer and associated comorbidities. This justify the absence of control group. Tygerberg Hospital is a tertiary hospital where many BC patients are referred from urban and rural primary care clinics for specialized BC surgery or radiation therapy. A prospective cohort study was performed in parallel with ongoing generation of a BC biobank and genomics database/registry developed under reference number N09/08/224. Inclusion and excluded criteria for postmenopausal women (aged 45–80 years) were documented following Stage 0-III ER-positive breast cancer, subjects with available data in genomics database and the National Health Laboratory Service (NHLS) were selected. Eligible patients were stratified based on BMI, hs-CRP, waist circumference and breast cancer strategies. Clinical and biomedical profiles of 126 BC patients fulfilled the eligibility criteria. The primary outcome was the multiple imputations of the clinical and biomedical survival outcomes based on the existing baseline data. The secondary outcome included development of a prediction model to examine the effects of AIs on BMI, hs-CRP, and other inflammatory markers after 24 months of AIs use based on the mean change of the above markers over time. Detailed of methodology and statistical analysis were published elsewhere . This study was approved by Health by the HREC of Stellenbosch University . Descriptive and analytical statistics were conducted as appropriate and STATA version 16 was used for analysis  and others published articles [19,20,21,22,23].
A convenience sample of 126 participants was considered for this analysis. Descriptive analysis was reported previously . About 56 (44.44%) participants received more than 4 treatment options, 39 (30.95%) received at least 4 treatment options and 31 (24.60%) received less than 4 treatment options. The mean and standard variation by treatment options were provided. These including height, BMI, hs-CRP, weight, phosphate, homocysteine, ferritin, TBF and hip circumference. Table 1 provides the baseline characteristics of the postmenopausal breast cancer survivors. Random linear effects model revealed stronger statistically association between BMI and homocysteine (p = 0.021, 95%CI: 0.0083 to 0.1029). Weight and total body fat were strongly associated after 24 months follow up. In addition, hs-CRP was associated with BMI (p = 0.0001), and hs-CRP was associated with other biomedical markers such such as calcium (p = 0.021, 95% CI: 0.01 to 0.10), phosphate (p = 0.039, 95%CI: 0.01 to 0.10), and ferritin (p = 0.002, 95%CI: 0.02 to 0.08). There was statically significant correlation between cholesterol, BMI, phosphate, and hypertension after 24-month follow-up. Table 2 provides the outputs of the mean changes of inflammation markers at baseline and after month 24. The correlation between BMI, TBF, weight, hs-CRP, homocysteine, ferritin and calcium between baseline and after 24 months of follow-up. Hypertension was associated with BMI, weight and homocysteine after 24-month follow-up. Table 3 provides the effects of AIs on inflammatory markers at baseline and month 24th using multiple imputation model. Details of the analyses are published as PhD thesis on Website of Stellenbosch University. https://scholar.sun.ac.za› bitstream › handle › m.
This study have contributed to external validity of the findings reported in literature . The study revealed a stronger statistical association between BMI and homocysteine, while weight and total body fat were strongly associated after the 24-month follow-up. Hs-CRP was associated with BMI (p = 0.0001), and hs-CRP was associated with other inflammatory markers such as calcium, phosphate, and ferritin.
These findings are supported in literature [25, 26]. In addition, the mean changes after 24 months of baseline BMI, hs-CRP and estimated BMI and hs-CRP at month 24 were not statistically significant. Lack of statistically significant changes may be related to the small sample size used to build these models . The findings of the present modeling are supported by a Women’s Health Study in which 27,919 postmenopausal women were followed-up for 10 years . In this large study, a total of 892 women developed invasive breast cancer . Moreover, a study conducted to assess the effects of AIs on CVD adverse events occurrence during a one-year follow-up in postmenopausal BCS showed no significant changes on hs-CRP, cholesterol levels, and blood pressure observed between intervals versus control groups. However, the controversy was identified in other study after 5 years of AIs therapy . Clinicians should consider referring the highest risk patients for careful clinical and biochemical assessment to prevent long-term adverse events [7, 29].
The association between BC and hs-CRP are document including different biomedical and genetic pathways [14, 30,31,32,33]. This may include cardiovascular toxicity due to inhibition of CYP19A1 [34, 35] as well as the link between hs-CRP and AIs [36, 37].
This model is simple, easy to interpret, scientifically acceptable, and widely available [16, 17, 38]. However, it is important to note that real world events may not correspond with the mathematical assumptions of a linear model. In this case, the research team used real patient data for inference and published studies in other settings for model validation. Multiple approaches such as Bayesian Networks (BNs), machine computational technologies are approved in cancer studies to predict survivorship parameters for their accuracy in predictive models . The findings from this study will be shared with clinicians and an additional assessment, using a large sample and population diversity will be proceeded for external validity.
This study showed a correlation between BMI, TBF, weight, hs-CRP, homocysteine, ferritin and calcium between baseline and after 24 months of follow-up. Hypertension was associated with BMI, weight and homocysteine after 24-month follow-up. Routine assessment of hs-CRP and BMI are identified independent prognostic markers of CVD related adverse events in postmenopausal breast cancer survivors using AIs. Further studies on implementation of point-of-care testing incorporating clinical and biomedical markers are needed to predict AIs-associated adverse events in postmenopausal breast cancer survivors in different African settings.
Limitations and recommendations
The analysis conducted in this study was focused only on BMI and hs-CRP as predictors of CVD risk factors in postmenopausal BCS, using baseline data from main study. Other determinants of survivorship in cancer patients , such as types of treatment, reasons for loss to follow-up and hazard survival curve analysis . These results may be affected by the small sample size and convenience sampling technique used in this study. The correlation between clinical, biochemistry and genomic predictors should be performed in a larger prospective study using a number of genes already published in literature [40, 41]. Since the number of genetic predictors may be much larger than clinical or biomedical markers, model comparisons should be performed to confirm the results of this study.
Artificial Neural Networks
body mass index
breast cancer survivors
high sensitivity C-reactive protein
Predictive Mean Matching
Total body fat
Macciò A, Madeddu C. Obesity, inflammation, and postmenopausal breast cancer: therapeutic implications. Sci World J. 2011;11:2020–36.
Bardia A, Arieas ET, Zhang Z, Defilippis A, Tarpinian K, Jeter S. Comparison of breast cancer recurrence risk and cardiovascular disease incidence risk among postmenopausal women with breast cancer. Breast Cancer Res Treat. 2012 Feb;131(3):907–14.
Lee Chuy K, Yu AF. Cardiotoxicity of contemporary breast Cancer treatments. Curr Treat Options Oncol. 2019;20:51.
Shah R, Rosso K, David Nathanson S. Pathogenesis, prevention, diagnosis and treatment of breast cancer. World J Clin Oncol. 2014;5(3):283–98.
Runowicz CD, Leach CR, Henry NL, Henry KS, Mackey HT, Cowens-Alvarado RL. American Cancer Society/American Society of clinical oncology breast Cancer Survivorship Care Guideline. J Clin Oncol. 2016;34(6):611–35.
Park NJ, Chang Y, Bender C, Conley Y, Chlebowski RT, Van Londen GJ, et al. Cardiovascular disease and mortality after breast cancer in postmenopausal women: results from the women’s Health Initiative. PLoS ONE. 2017;12(9):e0184174.
Thomson CA, Thompson PA, Wright-Bea J, Nardi E, Frey GR, Stopeck A. Metabolic syndrome and elevated C-reactive protein in breast cancer survivors on adjuvant hormone therapy. J Womens Health (Larchmt). 2009;18(12):2041–7.
De Pergola G, Silvestris F. Obesity as a major risk factor for cancer. Journal of Obesity. 2013; 2013:291546.
Meneses-Echavez JF, Correa-Bautista JE, González-Jiménez E, Río-Valle JS, Elkins MR, Lobelo F, et al. The effect of exercise training on mediators of inflammation in breast cancer survivors: a systematic review with meta-analysis. Cancer Epidemiol Biomarkers Prev. 2016;25(7):1009–17.
Walsh T, Lee MK, Casadei S, Thornton AM, Stray SM, Pennil C, et al. Detection of inherited mutations for breast and ovarian cancer using genomic capture and massively parallel sequencing. Proc Natl Acad Sci U S A. 2010;107(28):12629–33.
Haney K, Tandon P, Divi R, Ossandon MR, Baker H, Pearlman PC. The role of Affordable, Point-of-Care Technologies for Cancer Care in Low- and Middle-Income Countries: a review and Commentary. IEEE J Translational Eng Health Med. 2017;5:2800514.
Protani M, Coory M, Martin JH. Effect of obesity on survival of women with breast cancer: systematic review and meta-analysis. Breast Cancer Res Treat. 2010;123(3):627–35.
MacCiò A, Madeddu C. Obesity, inflammation, and postmenopausal breast cancer: therapeutic implications. ScientificWorldJournal. 2011;11:2020–36.
van Hellemond IEG, Geurts SME, Tjan-Heijnen VCG. Current status of extended adjuvant endocrine therapy in early stage breast Cancer. Curr Treat Options Oncol. 2018;19(5):26.
Amir E, Seruga B, Niraula S, Carlsson L, Ocaña A. Toxicity of adjuvant endocrine therapy in postmenopausal breast cancer patients: a systematic review and meta-analysis. J Natl Cancer Inst. 2011;103(17):1299–309.
Boughorbel S, Al-Ali R, Elkum N. Model comparison for breast cancer prognosis based on clinical data. PLoS ONE. 2016;11(1):e0146413.
Nur U, Shack LG, Rachet B, Carpenter JR, Coleman MP. Modelling relative survival in the presence of incomplete data: a tutorial. Int J Epidemiol. 2010;39(1):118–28.
Milambo JP, Muambangu. (Stellenbosch: Stellenbosch University, 2021-12). Assessment of point-of-care testing for prediction of aromatase inhibitor-associated side effects in obese postmenopausal breast cancer patients screened for cardiovascular risk factors ï»¿ https://scholar.sun.ac.za/handle/10019.1/100763 (accessed 12th, May, 2022)
Wang WL. Mixture of multivariate nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values. Test. 2019. vol. 28(1), pages 196–222, 2019
Harrison XA, Donaldson L, Correa-Cano ME, Evans J, Fisher DN, Goodwin CED, et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ. 2018;6:e4794.
Kleinke K. Multiple imputation under violated distributional assumptions: a systematic evaluation of the Assumed Robustness of Predictive Mean matching. J Educational Behav Stat. 2017;42(4):371–404.
Nguyen CD, Carlin JB, Lee KJ. Model checking in multiple imputation: an overview and case study. Emerg Themes Epidemiol. 2017;14:8.
Hong T, Liu A, Cai D, Zhang Y, Hua D, Hang X, et al. Preoperative serum C-reactive protein levels and early breast cancer by BMI and menopausal status. Cancer Invest. 2013;31(4):279–85.
Baatjes K, Peeters A, McCaul M, Conradie MM, Apffelstaedt J, Conradie M, Kotze MJ. CYP19A1 rs10046 pharmacogenetics in postmenopausal breast Cancer patients treated with aromatase inhibitors: one-year follow-up. Curr Pharm Des. 2020;26(46):6007–12.
Shabaruddin FH, Fleeman ND, Payne K. Economic evaluations of personalized medicine: existing challenges and current developments. Pharmacogenomics and Personalized Medicine. 2015;8:115–26.
Agarwala R, Barrett T, Beck J, Benson DA, Bollin C, Bolton E, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018;46(Database issue):D8–D13.
Asegaonkar SB, Asegaonkar BN, Takalkar UV, Advani S, Thorat AP. C-Reactive Protein and Breast Cancer: New Insights from Old Molecule.International Journal of Breast Cancer. 2015.
Kotze MJ, Van Velden DP, Botha K, Badenhorst CH, Avenant H, Van Rensburg SJ et al. Pathology-supported genetic testing directed at shared disease pathways for optimized health in later life.Per Med. 2013
Babaei Z, Moslemi D, Parsian H, Khafri S, Pouramir M, Mosapour A. Relationship of obesity with serum concentrations of leptin, CRP and IL-6 in breast cancer survivors. J Egypt Natl Canc Inst. 2015;27(4):223–9.
Janelsins MC, Davis PG, Wideman L, Katula JA, Sprod LK, Peppone LJ, et al. Effects of Tai Chi Chuan on insulin and cytokine levels in a randomized controlled pilot study on breast cancer survivors. Clin Breast Cancer. 2011;11(3):161–70.
Ramos-Nino ME. The Role of Chronic Inflammation in Obesity-Associated Cancers.ISRN Oncol. 2013
Chan DSM, Bandera EV, Greenwood DC, Norat T. Circulating C-reactive protein and breast cancer risk-systematic literature review and meta-analysis of prospective cohort studies. Cancer Epidemiol Biomarkers Prev. 2020;17(15):5445.
Bulun SE, Chen D, Moy I, Brooks DC, Zhao H. Aromatase, breast cancer and obesity: a complex interaction. Trends Endocrinol Metab. 2012;23(2):83–9.
Hertz DL, Henry NL, Rae JM. Germline genetic predictors of aromatase inhibitor concentrations, estrogen suppression and drug efficacy and toxicity in breast cancer patients. Pharmacogenomics. 2017;18(5):481–99.
Fontein DBY, Houtsma D, Nortier JWR, Baak-Pablo RF, Kranenbarg EMK, Van Der Straaten TRJHM, et al. Germline variants in the CYP19A1 gene are related to specific adverse events in aromatase inhibitor users: a substudy of dutch patients in the TEAM trial. Breast Cancer Res Treat. 2017;18(5):481–99.
Mills RC. Breast Cancer survivors, common markers of inflammation, and Exercise: a narrative review. Breast Cancer: Basic and Clinical Research. 2017;11:1178223417743976.
Marques-Vidal P, Bochud M, Bastardot F, Lüscher T, Ferrero F, Gaspoz JM, et al. Association between inflammatory and obesity markers in a swiss population-based sample (CoLaus Study). Obes Facts. 2012;5(5):734–44.
Eisemann N, Waldmann A, Katalinic A. Imputation of missing values of tumour stage in population-based cancer registration.BMC Med Res Methodol. 2011;11.
Kim H-Y. Analysis of variance (ANOVA) comparing means of more than two groups. Restor Dent Endod. 2014;39(1):74–7.
Iyengar NM, Arthur R, Manson JE, Chlebowski RT, Kroenke CH, Peterson L, et al. 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. 2019;5(2):155–63.
Gallicchio L, Calhoun C, Helzlsouer K. Effect of aromatase inhibitor therapy on the Cardiovascular Health of black and white breast Cancer patients. Clin Breast Cancer. 2016;16(3):e23–31.
Prof Karin Batjies for improvement of data collection tools for this manuscript by using the similar database of breast cancer survivors of Tygerberg Hospital.
MJP was supported by the National Research Foundation of South Africa (Grant #: 112758).
Ethics approval and consent to participate
The research was approved by the Health Research Ethics Committee (HREC) of the Faculty of Medicine, University of Stellenbosch (Ethics Approval number S18/07/150). All the participants signed an informed form prior to the study participation.
Consent for publication
All the authors have read and approved the final manuscript of this work.
The author indicated no potential conflicts of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Milambo, J.M., Nyasulu, P.S., Akudugu, J.M. et al. Long-term effects of aromatase inhibitors on body mass index among postmenopausal breast cancer survivors in Africa: observational cohort study. BMC Res Notes 16, 37 (2023). https://0-doi-org.brum.beds.ac.uk/10.1186/s13104-023-06301-6
- Aromatase inhibitors
- High sensitivity C-reactive protein
- Body mass index
- Postmenopausal breast cancer
- And multiple imputations