In this cross-sectional study, Mexican women between 30 and 67 years old who attended the Oncology Service at the Regional General Hospital No. 251 of the Mexican Institute of Social Security (Metepec, Mexico State, Mexico), from March 2019 until July 2019 were invited to participate in this study. The BCS group included women who were in their post-cancer follow-up appointment. The inclusion criteria included the following: a) previous diagnosis of breast cancer; b) women who had undergone breast cancer surgery (lumpectomy or mastectomy) anywhere from one year to five years earlier and women who did not require surgery; b) no evidence of metastases, c) absence of respiratory and cardiovascular diseases, diabetes mellitus, thyroid dysfunction, hypertension; d) normotensive; e) capacity to stand up unaided, and f) capacity to answer a clinical interview, including a familiar history of cancer. The exclusion criteria consisted of the following: a) women under anticancer medication (e.g., tamoxifen 21) or any other medication; b) women undergoing chemotherapy or radiotherapy and c) super-obese women (BMI>50 kg/m2) and d) smoking. The elimination criteria involved women who presented RR time series with an error greater than 5% were excluded from the study.
Moreover, the control group included women without diagnosis of cancer with similar characteristics to the BCS group (ethnicity, age, weight, BMI, and height). In all controls, the presence of chronic diseases or pharmacological treatment was excluded by history and standard medical examination.
The sample size estimation was based on the study of Romanholi Palma et al. 13 and was determined using the G*Power software 22. We considered an 80% test power, an alpha error of 5% for a one-tailed test. The minimum sample size was determined to be 14 participants per group.
Electrocardiogram recording and preprocessing
On the day of the study, all participants arrived in the Oncology service, having avoided caffeinated or alcoholic beverages. Electrophysiological recordings were performed between 08:00 and 12:00 am to account for circadian rhythms of the heartbeat. All participants were asked to relax and record in a standard seated position at rest 23. The first lead (DI) of the electrocardiogram (ECG) was recorded for 5 minutes by using an ECG sensor model EKG-BTA (Vernier®, Beaverton, Oregon, USA) for NI Elvis II (Texas Instruments®, Dallas, Texas, USA) and superficial disposable electrodes. Electrocardiographic data were acquired with a PC at a sampling rate of 1000 Hz using the Biosignal Logger and Player software (National instruments®, Austin, Texas, USA).
Raw ECG recordings for both BCS and Control groups were then processed using previously validated algorithms to generate RR time series 24. All the RR time series were reconditioned by a filtering approach and tested in line with previous studies to exclude ectopic beats 25. All these calculations were obtained using Matlab® software (the MathWorks, Inc. Natick, Massachusetts, USA).
Heart rate variability (HRV) assessment
We assessed HRV according to methodological standards proposed by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology for HRV 26. We used the Kubios software version 3.1 (Kuopio, Finland)  to analyze RR time series. The following linear (time-domain) indexes were included: R-R interval (RRave), the standard deviation of the RR time series of normal sinus beats (SDNN, a biomarker of global HRV), the root mean square of successive differences (RMSSD), and the percentage of pairs of successive RR intervals that differ by more than 50 ms, these last two biomarkers are associated to the cardiac parasympathetic function 26.
Linear (frequency-domain) indexes were also reported: normalized low-frequency (LFnu: 0.04–0.15 Hz) and high-frequency (HFnu: 0.15–0.4 Hz). HFnu index is a parasympathetic modulation indicator, while LFnu is a general indicator of both the sympathetic and parasympathetic modulation branches of the ANS 27. By default, a 4 Hz interpolation was set in the Kubios software. The spectrum for the selected RR time series was computed with Welch's periodogram method (FFT spectrum). The default value for window width was 256 seconds, and the default overlap was 50 % 28.
Furthermore, we performed a nonlinear analysis of HRV. We considered the Poincaré indexes SD1, SD2, and SD1/SD2. SD1 is an index of short-term variability and reflects parasympathetic activity, while the SD2 index measures the long-term variability and reflects the overall variability. The (SD1/SD2) ratio represents the balance between long- and short-term HRV 29. The quantitative analysis of a plot involves fitting an ellipse to the Poincaré plot, which corresponds to the length of the minor (SD1) and major (SD2) axes. Besides that, we measured the short-term fractal exponent (α1), corresponding to a period of 4 to 11 beats, and the long-term fractal exponent (α2), corresponding to periods longer than 11 beats 30. When α = 0.05, there is no correlation, and the time series shows white noise behavior; if α = 1.5, the time series resemble Brownian motion, and if it is 0.5 < α < 1.5, there are positive correlations. If α ≈ 1.0 indicates a fractal-like behavior, if it reaches values above 1.0, the system tends to be less complex and linear 31.
Finally, to assess the regularity/irregularity of the RR time series, we also estimated the sample entropy (SampEn) calculated with m = 2 and r = 0.2, as described by Richman & Moorman 32.
Body composition estimation
Bioelectrical impedance analysis (BIA) is a non-invasive, low-cost, useful, and validated tool for estimating body composition 33. The analysis is achieved by measuring the bioimpedance of an electrical current transmitted to the body through electrodes placed on the feet 34. On the day of the study, a body composition analyzer, which employed BIA, was used to estimate body composition (BC-533 InnerScan Body Composition Monitor®, Tanita Corp., Itabashi-Ku Tokyo, Japan). Firstly, the heights of all subjects were measured and recorded. Then, participants were weighed, and body composition values were indirectly estimated using the device. The following body composition measures were collected for the BCS and Control groups: body fat percentage, body water percentage, muscle mass, bone mass, predicted daily calorie intake (DCI), metabolic age, and visceral fat rating (VFR). Particularly, VFR is given as a specific rating: (0–59 points). Ratings from 1 to 12 points indicate that the subject has a healthy level of visceral fat, while ratings from 13 to 59 points indicate that the subject has an excess level of visceral fat. The visceral fat rating has been widely applied in medical research as an indirect visceral fat amount in females 35 and mixed-gender groups 36. We calculated the normalized visceral fat rating (nVFR) by dividing the visceral fat rating by the bodyweight of each participant.
This study was approved by the Research Ethics Committee No. 1503 from the Regional General Hospital No. 251 of the Mexican Institute of Social Security (IMSS). The Federal Commission for Protection Against Health Risks (COFEPRIS) authorizes this committee (authorization no. 17 CI 15 104 037) and the National Research National Commission of Bioethics in Mexico (CONABIO, authorization no. 15 CEI 002 2017033). This protocol was registered under the code R-2019-1503-012. All volunteers in this study signed an Informed Consent Form when they agreed to participate, and all methods were performed in accordance with the relevant guidelines and regulations.
The statistical analysis was done with GraphPad Prism version 8.00 for Windows (GraphPad Software, La Jolla California USA). Descriptive results were presented as median (25th–75th percentile) for quantitative variables and frequency (percentage) for categorical variables. A Shapiro-Shapiro-Wilk test was used to assess the normality of distribution. However, the data did not appear to have a normal distribution. Thus, the continuous variables were compared using one-tailed Mann-Whitney's U tests, and categorical variables were evaluated by Fisher's exact test. Associations between body composition and HRV measures (BCS group) were evaluated by Spearman's correlation coefficient. For all tests, results of p<0.05 were considered significant.