ABSTRACT: Aim: The aim of this experiment was to compare the cardiac autonomic activity and BMI in the different phases of menstrual cycle using Heart rate variability. Context Heart rate variability (HRV) in women has been related independently to endogenous sex hormones, hormone replacement therapy, menopause, menstrual cycle, body mass index (BMI), and physical conditioning .It has become a popular non invasive tool for assessing the activities of autonomic nervous system. Objectives: The aim of this experiment was to compare the cardiac autonomic activity and BMI in the different phases of menstrual cycle using Heart rate variability Design: Prospective comparative study during one menstrual cycle. Study setting A total of 54 female students were selected. The selected students were divided into two Group I (BMI < 20) and Group II (BMI >20) the ECG recording were taken during the 3 phases of menstrual cycle. The analog ECG signal were conveyed through an A/D converter to PC and were analyzed .The frequency domain analysis was done in which the LF,HF and LF/HF components were studied. Results: In group II (BMI>20)) there was a significant increase in the LF/HF ratio in the luteal phase of menstrual cycle when compared to other phases of the menstrual cycle. In group I (BMI < 20) there was no statistically significant difference in LF/HF ratio in the three phases . Conclusion: Woman between 16-25 years having less BMI had more parasympathetic activity than those with greater BMI. In women with greater BMI cardiac autonomic activity had a predominant sympathetic profile.
The heart is an organ under the influence of the autonomic nervous system for the maintenance of homeostasis, and, in this respect, one of its main characteristics is the constant modification of its rate on beat-to beat basis1. Also, it should be emphasized that heart rate is a variable that can be measured in a noninvasive manner with minimal error using simple and low-cost equipment. Today, with the aid of digital computers, it has become possible to study beat-to-beat heart rate variability (HRV) obtained from the R-R intervals in the electrocardiogram (ECG) recordings2, 3, 4. This method has proved to be of great clinical usefulness to evaluate the balance of sympathetic and parasympathetic regulation in several pathological conditions5,6,7,8,9,10. HRV has proved to be a more sensitive tool for the detection of autonomic balance than mean heart rate11
Power spectral analysis of heart rate variability (HRV) has been used as a sensitive index of autonomic nervous activities12,13,14,15. In humans, power spectral analysis of R-R interval variability has revealed that there are two major spectral components: the high frequency (HF) component at the respiratory frequency and the low frequency (LF) component at .03 to .15 Hz. The HF component corresponds to the respiratory sinus arrhythmia and is modulated solely by the parasympathetic nervous system14,16,17 ,whereas the LF component corresponds to blood pressure oscillations occurring around .1 Hz, (i.e., the Mayer waves) and is jointly modulated by the sympathetic and parasympathetic nervous systems 18,19. In addition, the LF/HF ratio is also a useful parameter that reflects the balance of autonomic nervous activities 20, 21.
Heart rate variability (HRV) in women has been related independently to endogenous sex hormones, hormone replacement therapy, menopause, menstrual cycle, body mass index (BMI), and physical conditioning22. Physiological effects of menstrual cycle on the autonomic function have been extensively examined23. Several studies have found variation of symphatho-vagal activities during the menstrual cycle but the results are inconsistent24, 25. Few studies have evaluated neurocardiac parameters during the various phases of menstrual cycle26; this would be useful and highly relevant for cardiovascular evaluation of women at higher risk to develop heart disease, thus permitting early intervention.
BMI has traditionally been used to identify individuals who are most likely to be overweight or obese. It is calculated by dividing the weight (kg) by height (meters) squared. Generally high value indicates excessive body fat and consistently relates to increased health risks and mortality27. There is a well recognized relationship between autonomic nervous system function and body habits28. A decrease in parasympathetic nervous system mediated HRV in obesity may in part explain the mortality and morbidity that are associated with the obese state29. Gonadal steroids play a major role in the distribution of body fat. At the onset of puberty women increase their body fat relative to their muscle mass. The distribution of body fat is important clinically in determination of obesity, visceral central adiposity is associated with a greater risk of metabolic & cardiovascular disorders including insulin resistance, type2 diabetes, hypertension & coronary heart disease30.Obesity can be determined by BMI30.
It is questionable that whether in general population low HRV is a consequence of a disease or an indicator of an underlying mechanism for future disease. Several studies suggest that, there are definite changes in the HRV in the different phases of the menstrual cycle31 but studies are lagging correlating the cardiac autonomic activity, menstrual cycle and BMI in young females in different phases of menstrual cycle. The present study aims to describe the HRV and assesses its association with BMI, and menstrual cycle in healthy young women in time domain and frequency domain method in different phases of menstrual cycle
Materials and Methods: A total of 54 female students studying their MBBS Course in Kasturba Medical College Bejai, Mangalore were selected . The selected students were in the age group 18-25 years who were having regular ,28-day menstrual cycles for at least 6 months prior to this study. After detailed enquiry of the medical history of the subjects, those with history of smoking, alcoholism, medical illness were excluded. Subjects on oral contraceptive pill, hormonal replacement therapy, drugs that alter the cardiovascular functions were also excluded from the study. Informed written consent was obtained from all participants, and the experiment protocol was approved by Ethics committee of the college.
Experimental protocol: The selected students were divided into two group based on their BMI values, Group I (n=25; n denotes the number of individuals in each group) consisted of BMI<20 and Group II (n=29) consisted of individuals having BMI>20.
The ECG recording were taken during the following 3 phases
Menstrual phase (M) – 1st to 5th day of bleeding ,
Follicular phase (F) – 6th day to 14th day of menstrual cycle.
Luteal phase (L) – 15th day to 28th day or the next menstrual bleeding.
Heart rate Variability was recorded using Digital data Acquisition system, HRV soft 1.1
VERSION, AIIMS, NEW DELHI. A high quality ECG recording was taken under standardized condition to minimize artifacts. The ECG signal was first analogally recorded & then digitally converted and analyzed in the frequency domain.
The experiments were carried out in the morning in fasting state. Subjects refrained from caffeinated beverages for at least 12 hours prior to the experiments and had completed their evening meal by 9 P.M. they were also instructed to avoid strenuous physical activity from the previous evening. The recordings of ECG of all subjects were done by the same person of our team in order to avoid any inter–observer error. To quantify heart rate, the analog ECG signal was obtained using lead II to obtain a QRS complex of sufficient amplitude and stable base line. ECG signals were conveyed through an A/D converter to PC and were analyzed offline after visual checking of abnormal ECG. Heart rate variation during normal breathing for a period of 5 minutes was recorded, with subject supine, awake and resting. In the present study ,in the frequency domain analysis the two main frequency components that is the low frequency (LF) components (0.04 to 0.15Hz) and the high frequency (HF) components (0.15 to 0.4 Hz) was measured. We have also evaluated and analyzed the ratio LF/HF32,33,34,35.
Statistical analysis: The statistical analysis was done using ANOVA (Analysis of variance), student’s unpaired T test, Mannwhitney U test, Tukey’s Test. P value was taken as significant at 5 percent confidence level.(p < 0.05)
Table 1:Effect of variation in HRV in the three different phases of menstrual cycle in group I (BMI <20 )individuals (n=25)
|BMI < 20|
| 38.5 ± 6.6
| 61.4 ± 6.63
| 39.18± 16.86
|60.81 ± 16.86||0.76±0.54|
|65.95 ± 1.77
|69.70± 2.94||0.94 ±0.04|
Table 2:Effect of variation in HRV in the three different phases of menstrual cycle in group II (BMI >20 )individuals (n=29)
|42.64 ± 23.05
|61.05 ± 17.59
|0.83±0.79‡ ‡ ‡
|50.4 ± 17.37
|49.59 ± 17.37||1.23±0.66***|
|62.74 ± 14.27
|37.25± 14.27||2.03 ±1.03|
P< 0.0001‡ ‡ ‡ - Menstrual phase compared to Luteal phase
P< 0.0001*** – Follicular phase compared to Luteal phase
Table 3: – Effect of variation in BMI on HRV during Luteal phase of menstrual cycle (n=54) in Frequency domain analysis
|65.95 ± 1.77
|69.70± 2.94||0.94 ±0.04|
|BMI >20||62.74± 14.27
|41.11 ± 15.80||2.03 ±1.03 ***|
P< 0.0001*** – Comparison between BMI<20 and BMI >20
In the present study, the variations of LF, HF, and LF/HF ratio in group I (table1) showed no statistically significant difference in between the three phases of menstrual cycle. Further in group II (table II) there was a significant increase in the LF/HF ratio in the luteal phase of menstrual cycle when compared to other phases of the menstrual cycle (P<0.001 for both). Analysis of HRV during the luteal phase of the menstrual cycle in between group I and group II (table 3), showed a statistically significant increase in the LF/HF ratio (P<0.001) in group II.
Autonomic regulation of the heart in the normal woman differs during the menstrual cycle. Cardiac autonomic functions can be influenced by multiple factors36. The results of the present study support that BMI plays an important role in the womens cardiac autonomic modulation. Our results clearly demonstrated a significant difference in the autonomic nervous activity in the luteal phase of the menstrual cycle in young females with increased BMI.
Power spectral analysis of HRV has more sensitivity in assessing the slight fluctuation of autonomic activities during menstrual cycle37 .Our previous study provides findings that autonomic nervous activities fluctuate during Menstural cycle. Luteal phase of MC was associated with a significant increase in the LF component and a significant decrease in the HF component, resulting in a high LF/HF ratio. Our findings were in agreement with earlier work who observed that sympathetic nervous activities are predominant in the luteal phase as compared with follicular phase38, 39.
In this study BMI was a major determinant of cardiac autonomic nervous modulation. Woman between 16-25 years having less BMI had more parasympathetic activity than those with greater BMI. In women with greater BMI cardiac autonomic activity had a predominant sympathetic profile. LF/HF ratio is the most sensitive indicator of sympathovagal balance40 In the present study increase in BMI showed a higher increase in the LF/HF ratio in the luteal phase of the menstrual cycle, this indicated presence of sympathovagal imbalance (SVI).
In summary, this study has shown that women with increased BMI had significant changes in autonomic nerve function that included reduced parasympathetic control and elevated sympathovagal modulation. Even though these women did not come under the obese group, they are more prone to become obese. Clearly more work is needed to explore this relationship at the later age. It is well established that a lower heart rate variability is associated with increased cardiovascular morbidity. Detection of sympatho vagal inbalance at an early age based on BMI and necessary life style modification could decrease the incidence of cardiovascular diseases.
- Sumana Chatterjee, Subhrangsu Aditya, D. N. A Comparative Study between Females of Pre-Pubertal and Reproductive age groups to explore how HPG-Axis affects the autonomic Control over Cardiac Activity. Indian Journal of Biomechanics: 2009; 7(8)233-236.
- Pagani M, Lombardi F, Guzzetti S, Rimoldi O, Furlan R, Pizzineli P, Sandrome G, Malfatto G, Dell’orto S, Piccaluga E, Turiel M, Baselli G & Malliani A . Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympathovagal interaction in man and conscious dog. Circulation.1986 59: 178-193.
- Ribbert LS, Fidler V & Visser GH . Computer-assisted analysis of normal second trimester fetal heart rate patterns. Journal of Perinatal Medicine.1991; 19: 53-59.
- Silva E, Catai AM, Trevelin LC, Guimarães JO, Silva Jr LP, Silva LMP, Oliveira L, Millan LA, Martins LEB & Gallo Jr L. Design of a computerized system to evaluate the cardiac function during dynamic exercise. Physics in Medicine and Biology.1994; 33: 409.
- Tiller WA, McCraty R & Atkinson M . Cardiac coherence: a new noninvasive measure of autonomic nervous system order. Alternative Therapies in Health and Medicine.1996; 2: 52-65.
- Lindqvist A. Noninvasive methods to study autonomic nervous control of circulation. Acta Physiologica Scandinavica, 588 (Suppl):1990;1-10707.
- Tsuji H, Larson MG, Venditini FJ, Manders ES, Evans JC, Feldman CL & Levy D. Impact of reduced heart rate variability on risk for cardiac events. Circulation.1996; 94: 2850-2855.
- Reis AF, Bastos BG, Mesquita ET, Romêo Filho LJM & Nóbrega ACL . Disfunção parassimpática, variabilidade da freqüência cardíaca e estimulação colinérgica após infarto agudo do miocárdio. Arquivos Brasileiros de Cardiologia.1998; 70: 193-200.
- Kleiger RE, Stein PK, Bosner MS & Rottman JN . Time-domain measurements of heart rate variability.1995; In: Malik M & Camm AJ (Editors), Heart Rate Variability. Futura Publishing Company, Inc., New York
10. Barbosa PR, Barbosa J & Sá CAM . Influência da idade, sexo e doença coronária sobre a modulação autonômica do coração. Arquivos Brasileiros de Cardiologia.1996; 67: 325-329.
11. Tsuji H, Larson MG, Venditini FJ, Manders ES, Evans JC, Feldman CL & Levy D. Impact of reduced heart rate variability on risk for cardiac events. Circulation.1999; 94: 2850-2855.
12. Sayers B McA: Analysis of heart rate variability. Ergonomics.1973; 16(l):17-32.
13. Pomeranz B, Macaulay RJB, Caudill MMA, et al: Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol.1985; 248:H151-H153.
14. Pagani M, Lombardi F, Guzzetti S, et al: Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res.1986; 59:178-193.
15. Malliani A, Pagani M, Lombardi F, Cerutti S: Cardiovascular neural regulation explored in the frequency domain. Circulation.1991; 84(2):482-492.
16. Eckberg DL: Human sinus arrhythmia as an index of vagal cardiac outflow. J Appl Physiol.1983; 54(4):961-966.
17. Akselrod S, Gordon D, Madwed JB, Snidman NC, Shannon DC, Cohen RJ: Hemodynamic regulation: Investigation by spectral analysis. Am J Physiol.1985; 249:H867-H875.
18. Baselli G, Cerutti S, Civardi S, et al: Spectral and crossspectral analysis of heart rate and arterial blood pressure variability signals. Comp Biomed Res.1986; 19:520-534.
19. Baselli G, Cerutti S, Civardi S, Malliani A, Pagani M: Cardiovascular variability signals: Towards the identification of a closed-loop model of the neural control mechanisms. IEEE Trans Biomed Eng.1988; 35(12):1033-1046.
20. Lombardi F, Sandrone G, Pernpruner S, et al. Heart rate variability as an index of sympathovagal interac-tion after acute myocardial infarction. Am J Cardiol 1987; 60: 1239-1245.
21. Rimoldi O, Pierini S, Ferrari A, Cerutti S, Pagani M, Malliani A: Analysis of short term oscilliations of R-R and arterial pressure in conscious dogs. Am J Physiol 1990, 258: H967-H967.
22. Leicht, A.S, Hirning, D.A, Allen, D.A, Heart rate variability and endogenous sex hormones during the menstrual cycle in young women, Experimental Physiology. 2000, 88(3):441-446
23. Granot, M, Yarnitsky, D. MD, Itskovitz‐Eldor, J,Granovsky, Y, Peer, E, Zimmer, E. Z. Pain Perception in Women With Dysmenorrhea. Obstetrics & Gynecology. 2001; 98(3): 407-411
24. Stoney CM, Owens JF, Mathews KA, et al. Infleunces of the normal menstrual cycle on physiologic functioning during behavioural stress. Psychophysiology 1990; 2: 125-135.
25. Weidner G, Helmeg L, et al. Cardiovascular stress re-activity and mood during the menstrual cycle. Women Health 1990; 6(3): 5- 21 .
26. Rajnee, Vinod Kumar Chawla, Raghuveer Choudhary, Bijendra Kumar, Binawara,Sunita Choudhary. Haematological and electrocardiographic variations during menstrual cycle. Pak J Physiol 2010;6(1)18-21.
27. Kishan K , Prashanth N Dixit , Ramaswamy C , Bindiya RS , ,Y.P.RaghavendraBabu , Bhagyalakshmi K , Chandana Bhargavi. Reversal of impact of BMI on anutonomic modulation in geriatric population.International journal of applied biology and pharmaceutical technology. 2011;2(2):157-159.
28. Schiel R, Beltschikow W, Kramer G, Stein G.Overweight, obesity andelevated blood pressure in children and adolescents Eur J Med Res.2006; 11(3):97.
29. Freeman. R, Weiss ST, Roberts M, Zibikowski SM, Sparrow D.(1995) The relationship between heart rate variability and measure of body habitus. Clin Auton Res :Vol 5 (5) 261-6.
30. Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet . 2005;365:1415-1428
31. Sneha Shetty, Sheila R Pai, Nayanatara AK, Ramesh Bhat, Balachandra A Shetty. Comparitive study of time and Frequency domain analysis of heart rate variability in different phases of Menstrual cycle. Journal of Chinese Clinical Medicine.2010; 5(8):469-473.
32. Akihito Uehara, Chinori Kurata, Toshihiko Sugi, Tadashi Mikami, Sakae Shouda, Diabetic cardiac autonomic dysfunction: parasympathetic versus sympathetic,” Annals of Nuclear Medicin.1999, 13 (2): 95-100.
33. Nozomi Sato, Shinji Miyake, Juntchi akatsu , Masaharu K. Power Spectral Analysis of Heart Rate Variability in Healthy Young Women During the Normal Menstrual Cycle. Psychosomatic Medicine.1995, 57:331-335 .
34. P. Cugini, M. Curione, C. Cammarota, F. Bernardini, D. Cipriani, R. De Rosa, P. Francia, T. De Laurentis, E. De Marco, A. Napoli, and F. Falluca, “Is a Reduced Entropy in Heart Rate Variability an Early Finding of Silent Cardiac Neurovegitative Dysautonomia in Type 2 Diabetic Mellitus ?,” Journal of Clinical and Basic Cardiology. 2001. 4 (4) :289-294.
35. Herbert Jelinek, Allyson Flynn, and Paul Warner, “Automated assessment of cardiovascular disease associated with diabetes in rural and remote health care practice,” The national SARRAH conference.2004,1-7.
36. Maite Vallejo, Manlio F. Márquez, Victor H. Borja-Aburto, Manuel Cárdenas , Antonio G. Hermosillo. Age, body mass index, and menstrual cycle influence young women’s heart rate variability A multivariable analysis . Clinical Autonomic Research. Volume 15, Number 4, 292-298.
37. Girder SS, Pederson CA, Stern RA, Light KC, et al. Menstural cycle and premenstrual syndrome : Modifi-ers of cardiovascular reactivity in women. Health Psy-chol 1993; 12(3): 180-192.
38. Sato N, Miyake S, Akatsu J, Kumashiro M, et al. Power spectral analysis of heart rate variability in healthy women during the normal menstrual cycle. Psychosom Med. 1995; 57: 331-335.
39. Bai X, Li J, Zhou L, Li X et al .Influence of the men-strual cycle on nonlinear properties of heart rate variability in young women. Am JPhysiol Heart Circ Physiol. 2009; 297(2): H 765-4.
40. Alberto M. Heart rate variability: from bench to bedside. Europ J Int Med 2005; 16: 12–60. 19. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Circulation 1996; 93: 1043–1065.