ABC | Volume 112, Nº5, May 2019

Original Article Gonzaga et al Cardiac autonomic modulation in breast cancer Arq Bras Cardiol. 2019; 112(5):555-563 can be avoided and/or prevented. Autonomic modulation analysis can be used for this purpose since the autonomic nervous system (ANS) is one of the components involved in the etiology and consequences of cardiovascular disorders caused by the treatment of breast cancer. 8,9 The use of autonomic modulation as a predictor of cardiovascular risk in women with breast cancer was evidenced by Lakoski et al., 9 who identified autonomic dysfunctions in these women characterized by increased sympathetic modulation and decreased parasympathetic modulation, suggesting a higher risk of CVD in women with breast cancer. As can be observed, the risk of CVD in women with menopausal breast cancer may be related to a reduction in autonomic modulation and worsening of the lipid profile, which can be precipitated by both menopause and the use of AI. 10,11 These women are also more prone to weight gain after chemotherapy and, consequently, to suffer changes in visceral adiposity, leading to changes in lipid profile and insulin resistance. 3 In addition, elevated levels of inflammation have been observed in cancer patients, 12 a condition also responsible for lower survival in these patients. 12,13 However, the correlation between autonomic modulation and these factors has not been explored. Therefore, investigating the autonomic modulation of women with breast cancer in menopause who use AI and the relationship with cardiovascular biochemical variables could improve the targeting of future treatments and quality of life of women with breast cancer. In this context, this study aimed to evaluate the cardiac autonomic modulation of postmenopausal women using AI to treat breast cancer, as well as its relationship with the following cardiovascular biochemical variables: fasting glycemia, triglycerides, HDL cholesterol, and C-reactive protein (CRP). Methods This is a cross-sectional study, carried out fromMarch 2015 to July 2016, in a city in the southeastern region of Brazil. A total of 348 women, who were treated for breast cancer and registered in the records of the Oncology Pharmacy of the city's Regional Hospital, were analyzed. The medical records of these patients were analyzed and only women who were using AI were invited to participate in the study, totalling 124 women. Postmenopausal women without breast cancer were invited and recruited through radio, television, and local newspapers, totalling 189 women. The inclusion criteria of the study were: aged between 50 and 80 years; being in the menopause, defined by the self report of absence of the menstrual cycle in the previous 12 months; signing the informed consent form to participate in the study, and not having participated in supervised physical exercise for at least six months immediately prior to the study. Specifically for women with breast cancer, in addition to all the criteria mentioned above, they were required to present stages I to IIIa of breast cancer, 14 certified by doctors through the medical records. ThestudywasapprovedbytheInstitution'sEthicsandResearch Committee (Protocol No. 6727715.1.0000.5402/2015) and registered in the ClinicalTrials.gov Platform with the identifier NCT02804308. Experimental draw The experimental design of the present study included two groups of women with different characteristics: one with and one without breast cancer. According to the inclusion criteria specific to this study, the convenience sampling consisted of 48 postmenopausal women, who were distributed as follows: 33 without breast cancer and 14 survivors of breast cancer under treatment with AI. The selection of the participants in this study can be better visualized in figure 1. On the first day, all the women participating in the study answered questionnaires related to sociodemographic information (with questions related to age, education level, marital status, occupation, children, and self-reported diseases - cardiac, respiratory, metabolic, musculoskeletal). After answering the questionnaires, the volunteers underwent an evaluation of body composition using DEXA equipment - Dual Energy X-ray Absorptiometry, brand Lunar DPX-NT. Subsequently, the volunteers received a referral to the clinical analysis laboratory for blood sample collections, heart rate variability (HRV) assessments were scheduled, and the guidelines provided. For HRV analysis, the heart rate was recorded beat-to-beat in the morning (8 am to 11 am) in a quiet environment with a temperature between 21ºC and 24ºC and relative air humidity of 40-60% and the series of RR intervals obtained were used for the calculation of HRV indices. Body composition Body composition was measured using DEXA Dual Energy X-ray Absorptiometry, brand Lunar DPX-NT, General Electric Healthcare, Little Chalfont, Buckinghamshire, UK, software version 4.7. The following outcome variables were collected: percentage of body fat (%), lean mass (kg), fat mass (kg), and total bone mineral density (g/cm 2 ). Analysis of HRV For the analysis of HRV, initially, the volunteers were instructed not to consume alcoholic beverages and/or ANS stimulants such as coffee, tea, soda, and chocolate, and not to perform any type of intense physical exercise during the 24 hours preceding the evaluation. The Polar S810i heart rate monitor (Polar, Finland), previously validated equipment for recording heart rate and its use for calculation of HRV 15 indices, was used to record heart rate. The equipment pick-up strap was positioned on the distal third of the sternum and the heart rate receiver on the volunteer's wrist. During the uptake, the volunteers were instructed to remain silent, awake, at rest, and breathing spontaneously for 30 minutes in the supine position. For the analysis of the HRV indices, 1000 RR intervals were obtained from the most stable section of the trace, which was subjected to digital filtering in the proprietary software of the cardio-frequency meter, Polar Pro Trainer 5 version 5.41.002, complemented by manual filtering for elimination 556