NONINVASIVE METHODS IN CARDIOLOGY 2020 Edited by: Cornélissen G., Siegelová J., Dobšák P. Brno 2020 © 2020 Masaryk University ISBN 978-80-210-9715-5 Under the auspices of Prof. MUDr. Martin Bareš, Ph.D., Rector of Masaryk University, Brno Prof. MUDr. Martin Repko, Ph.D., Dean of Faculty of Medicine Masaryk University Brno Reviewed by: Prof. MUDr. Kamil Javorka, DrSc. Jessenius Faculty of Medicine in Martin Comenius University in Bratislava Slovak Republic Contents Scientific Project and International Cooperation between Masaryk University and University of Minnesota, University of Graz and University of Paris............................................ 5 Jarmila Siegelova Chronobiologic Analyses of Weeklong around-the-clock Records of Simultaneously Monitored Blood Pressure and Activity................................................................................................................ 19 Germaine Cornelissen, Zainab Farah, Denis Gubin, Lyazzat Gumarova, Linda Sackett-Lundeen, Thomas Kazlausky, Kuniaki Otsuka, Jarmila Siegelova, Larry A Beaty Some Lessons Learned from a 43-year Record of Self-Measurements by a Physician-Scientist........ 27 Linda Sackett Lundeen1 Larry A Beaty, Jarmila Siegelova2 Yoshihiko Watanabe, Germaine Cornelissen Tenth Anniversary of Departure for ever of Professor Dr. Jean-Paul Martineaud............................. 43 Jarmila Siegelová In Memoriam Jean-Paul Martineaud.................................................................................................. 45 Bernard Levy Falls and Falls-Related Injuries: Do Circadian Rhythms and Melatonin Play Important Roles?........ 47 Nandu Goswami, Carolina Abulafia, Daniel Eduardo Vigo, Maximilian Moser, Germaine Cornelissen, Daniel Cardinali Best Time of Exercise According to Circadian Rhythm..................................................................... 49 Jarmila Siegelova, Leona Dunklerova, Petr Dobsak, Alena Havelkova, Vanat M., Singh R.B., Germaine Cornelissen Efficacy and Safety of Intra-Dialytic Exercise Training in Hemodialysis Patients ............................ 79 Petr Dobsak, Petr Filipensky, Petra Palanova-Vitkova, Veronika Mrkvicova, Jana Pernicova, Pavel Vank, Michaela Sosikova, Michal Pohanka, Jarmila Siegelova, Miroslav Soucek Night-to-Day Blood Pressure Ratio During Seven-Day Ambulatory Blood Pressure Monitoring...... 93 Jarmila Siegelova, Leona Dunklerova, Alena Havelkova, Jiri Dusek, Michal Pohanka, Petr Dobsak, Germaine Cornelissen. Non-Invasive Methods in Experimental Cardiology – Benefits and Drawbacks................................113 Tibor Stračina, Marie Nováková NONINVASIVE METHODS IN CARDIOLOGY 2020 5 Scientific Project and International Cooperation between Masaryk University and University of Minnesota, University of Graz and University of Paris Prof. MUDr. Jarmila Siegelova, DrSc. Department of Physiotherapy, Faculty of Medicine Masaryk University Scientific projects after Velvet Revolution in the Czech Republic are possible with the cooperation between Masaryk University and University of Minnesota, University of Graz and University of Paris. Cooperation with University of Minnesota, USA Cooperation with Professor Franz Halberg and with professor Germaine Cornélissen, Dr. Othild Schwartzkopff, Halberg Chronobiology Center of the University of Minnesota, USA and with Brno team including professor Bohumil Fiser, Jiri Dusek, M.D. and professor Jarmila Siegelova started in 1988. The common studies of circadian variability of cardiovascular variables and baroreflex sensitivity were published in many papers as the result of this common work and our Brno team participated in international projects Womb to Tomb, later BIOCOS, under the direction from Halberg Chronobiology Center from Minnesota. Franz Halberg, M.D., Dr. h.c. (Montpellier), Dr. h.c. (Ferrara), Dr. h.c. (Tyumen), Dr. h.c. (Brno), Dr. h.c. (L’Aquila), Dr. h.c. (People’s Friendship University of Russia, Moscow), Professor of Laboratory Medicine and Pathology, Physiology, Biology, Bioengineering and Oral medicine 5.6.1919 – 9.6.2013 NONINVASIVE METHODS IN CARDIOLOGY 2020 6 In the years 1991 – 1995 we solved the project the Czech project from IGA belonging to Ministry of Health of the Czech Republic Pathogenesis and treatment of essential hypertension, chronobiology of blood pressure in health and disease (Patogeneze a léčba esenciální hypertenze, chronobiologie krevního tlaku ve zdraví a nemoci, IZ342). The results of this project were discussed repeatedly with Professor F. Halberg and Professor G. Cornélissen from University of Minnesota. The document of cooperation is shown in original. NONINVASIVE METHODS IN CARDIOLOGY 2020 7 NONINVASIVE METHODS IN CARDIOLOGY 2020 8 NONINVASIVE METHODS IN CARDIOLOGY 2020 9 Cooperation with University Graz, Austria The international cooperation started in 1990 with Professor Thomas Kenner from the Department of Physiology in University in Graz (Austria), where the original studies of heart rate variability, baroreflex sensitivity and chronobiology have been realized and included in the common international project of analysis of cardiovascular control in physiology and pathophysiology that was signed later in 1993, as it is shown in the document. From the year 1990 Prof. Kenner and Brigitte Kenner visited Masaryk University Brno three times a year and were active in all scientific activities organized by us in Masaryk University Brno. *29.9.1932 - †22. 12.2018 Prof. Dr. Thomas Kenner, M.D., Dr. h.c. mult. Dr. h. c., Universität Jena, 1990 Dr. h. c., Semmelweis University Budapest, 1998 Dr. h. c., Masaryk University Brno, 2000 Rector (president) Karl-Frances-Universitat, Austria 1989-1991 Dean of Medical School, Karl-Fraces Universitat, Austria, 1991-1997 NONINVASIVE METHODS IN CARDIOLOGY 2020 10 In the year 1997 – 1999 we solved the project the Czech project from IGA belonging to Ministry of Health of the Czech Republic Determination of baroreflex power in untreated hypertensive patients and after ACE therapy - inhibitors and Ca antagonists (Určení výkonnosti baroreflexu u neléčených NONINVASIVE METHODS IN CARDIOLOGY 2020 11 hypertoniků a po terapii ACE - inhibitory a Ca antagonisty, IZ 4313). The results of this project were discussed repeatedly with Professor F. Halberg and Professor G. Cornélissen from University of Minnesota, Professor T. Kenner, University Graz and Professor J.P. Martineaud, University Paris. In the years 1995 – 1997 we solved the project also another Czech project from IGA belonging to Ministry of Health of the Czech Republic Sleep apnea and cardiovascular disease syndrome (Syndrom spánkové apnoe a kardiovaskulární choroby, IZ 1818-4). The results of this project were discussed repeatedly with Professor F. Halberg and Professor G. Cornélissen from University of Minnesota, Professor T. Kenner, University Graz and Professor J.P. Martineaud, University Paris. Cooperation with University of Paris, France The international cooperation continued with Professor Jean-Paul Martineaud and Professor Dr. Etienne Savin, Medical Faculty, Lariboisiere Hospital, University of Paris (France) and was very intensively developed. There are common original studies of aortic compliance and blood flow regulation in cerebral arteries, baroreflex sensitivity in healthy subjects and patients with essential hypertension. Prof. Jean Paul Martineaud, M.D.R, *27.3.1931-†29.11.2010 Professor of Physiology, University Paris VII-Denis Diderot, France (1968-1995) Head, Service d´explorations fonctionnelles de l´hôpital Lariboisiere (1968-1995) NONINVASIVE METHODS IN CARDIOLOGY 2020 12 NONINVASIVE METHODS IN CARDIOLOGY 2020 13 NONINVASIVE METHODS IN CARDIOLOGY 2020 14 With Professor Martineaud, Paris, we solved the project the project from Ministry of Education of France together with Professor Jean-Paul Martineaud, Professor Etienne Savin, Dr. Philipe Bonnin and from the Czech part Professor Jarmila Siegelova, Professor Bohumil Fiser and Jiri Dusek, M.D. in the years 1997 – 1998. In the years 1999 – 2004 we solved the project from Ministry of Education of the Czech Republic Research Plan Early diagnosis of cardiovascular diseases (Výzkumný záměr Časná diagnostika kardiovaskulárních chorob, MSM141100004). The results of this project were discussed repeatedly with Professor F. Halberg and Professor G. Cornélissen from University of Minnesota, Professor T. Kenner, University Graz and Professor J.P. Martineaud, University Paris. In the years 2005 – 2011 we solved the project from Ministry of Education of the Czech Republic Research Plan Early diagnosis and therapy of cardiovascular diseases (Výzkumný záměr Časná diagnostika a léčba kardiovaskulárních chorob, MSM0021622402). The results of this project were discussed repeatedly with Professor F. Halberg and Professor G. Cornélissen from University of Minnesota, Professor Kenner, University Graz and Professor J.P. Martineaud, University Paris. In the years 1995 – 1997 we solved the Czech project from IGA belonging to Ministry of Health of the Czech Republic Sleep apnea and cardiovascular disease syndrome (Syndrom spánkové apnoe a kardiovaskulární choroby, IZ 1818-4). The results of this project were discussed repeatedly with Professor F. Halberg and Professor G. Cornélissen from University of Minnesota, Professor T. Kenner, University Graz and Professor J.P. Martineaud, University Paris. In the years 2004 – 2006 we solved the Czech project from IGA belonging to Ministry of Health of the Czech Republic New methods in the rehabilitation of patients with compensated heart failure (Nové metody v rehabilitaci pacientů s kompenzovaným srdečním selháním, NR7983). The results of this project were discussed repeatedly with Professor F. Halberg and Professor G. Cornélissen from University of Minnesota, Professor Kenner, University Graz and Professor J.P. Martineaud, University Paris. In the years 2009 - 2011 we solved the Czech project from IGA belonging to Ministry of Health of the Czech Republic “Increasing the effectiveness of rehabilitation due to combined aerobic training supplemented by electromyostimulation in patients with chronic heart failure” (Zvýšení účinnosti rehabilitace vlivem kombinovaného aerobního tréninku doplněného o elektromyostimulaci u nemocných s chronickým srdečním selháním, NS10096). The results of this project were discussed repeatedly with Professor F. Halberg and Professor G. Cornélissen from University of Minnesota, Professor T. Kenner, University Graz and Professor J.P. Martineaud, University Paris. In the years 2012 – 2014 we solved the project from Ministry of Education of the Czech Republic „Modification of the education system in the field of physiotherapy in order to increase competitiveness“ (Modifikace systému vzdělávání v oblasti fyzioterapie za účelem zvýšení konkurenceschopnosti, OPVK CZ.1.07/2.2.00/280240). The results of this project were discussed repeatedly with Professor F. Halberg and Professor G. Cornélissen from University of Minnesota, Professor T. Kenner, University Graz. In the years 2012 – 2014 we solved the project from Ministry of Education „OPTIMED - optimized teaching of general medicine, horizontal and vertical connections, innovation and efficiency for practice“ (OPTIMED - optimalizovaná výuka všeobecného lékařství, horizontální a vertikální propojení, inovace a efektivita pro praxi, OPVK CZ.1.07/2.2.00/28.0042). The results of this project NONINVASIVE METHODS IN CARDIOLOGY 2020 15 were discussed repeatedly with Professor F. Halberg and Professor G. Cornélissen from University of Minnesota, Professor Kenner, University Graz. In the years 2004 -2008 and 2008 until the present we solved the International project Rehabilitation in Internal Medicine, Tohoku University, Sendai, Japan, leader of the project Professor Masairo Kohzuki from Japan and Professor Petr Dobsak, Masaryk University. The results were presented partly in our Brno Noninvasive Methods of Cardiology. Professor Masairo Kohzuki Chairman, Department of Internal Medicine and Rehabilitation Science, Tohoku University Graduate School of Medicine, Sendai, Japan From 80th of the last century, Prof. Franz Halberg and from 1994 Prof. Germaine Cornelissen became coordinators of international chronobiology project “Womb-to-Tomb Study”, now BIOCOS (The BIOsphere and the COSmos). The chronobiological team of Masaryk University was part of both projects. On November 22, 1994 BIOCOS was described for the first time. The BIOsphere and the COSmos, BIOCOS, as the task of building a novel transdisciplinary spectrum was pursued, and further periods of decades, centuries, and thousands and millions of years were documented. Much of the evidence was provided very successfully by Germaine Cornelissen, PhD, Professor of Integrative Biology and Physiology at the University of Minnesota, so that the new periodicities were dubbed the Cornélissen-series at an international meeting in Ekaterinburg, Russia. NONINVASIVE METHODS IN CARDIOLOGY 2020 16 Professor Germaine Cornelissen, PhD, director of Halberg Chronobiology Center Professor of Integrative Biology and Physiology University of Minnesota, USA In the thirty years of the duration of international cooperation and every year Congresses of Noninvasive methods in cardiology in Masaryk University, Brno, the number of members of the international project team increased in our Republic with Professor Petr Dobsak, who organized cooperation with Japan Universities, Assoc. Professor Michal Pohanka, Assoc. Professor Jiri Jancik, Dr. Jitka Svobodova, Dr. Hana Svacinova, Dr. Pavel Vank, Dr. Michaela Sosikova, Dr. Alena Havelkova, Mgr. Petra Palanova, Mgr. Veronika Mrkvicova, Mgr. Leona Dunklerova, Professor Marie Novakova, Mgr. Jana Svacinova. The congresses and symposia in Masaryk University were visited every time from abroad by famous scientific personalities - Prof. Franz Halberg and Prof. Germaine Cornelissen from University of Minnesota, USA, Prof. Thomas Kenner, Rector of University and Dean of Medical Faculty, University of Graz, Austria and Prof. Jean-Paul Martineaud, Medical Faculty, Hopital Lariboisiere, Paris, France, Prof. Dr. Etienne Savin, Hopital Lariboisiere, University Paris, France, Professeur Jean-Eric Wolf, C.H.U. du Bocage, Dr. Jean-Christophe Eicher, C.H.U. du Bocage, University Dijon, France, Professor Kou Imachi, M.D., Ph.D., T.U.B.E.R.O., Tohoku University, Sendai, Japan, Professor Masahiro Kohzuki, M.D. Ph.D., Tohoku University, Sendai, Japan, Professor Yambe Tomoyuki, M.D. Ph.D., Tohoku University, Sendai, Japan. In the last year there were in our meeting also new co-workers of Prof.T. Kenner, namely Prof. Dieter. Platzer, University Graz, Prof. Nandu Goswami, Prof. Maxmilian Moser, University Graz, Prof. Daniel Schneditz, University Graz, Mgr. Bianca Brix, University Graz. NONINVASIVE METHODS IN CARDIOLOGY 2020 17 Assoc. Prof. PD Dr. med. Nandu Goswami Chairman of Dept. of Physiology Medical University of Graz, Austria All the scientists mentioned above in the last 30 years and the chronobiologic staff of Masaryk University presented and discussed scientific results in USA, France, Italy, Austria, Japan, Canada and other countries. For the future, in the next years we plan further scientific work on the projects with University Minnesota, USA, under leading personality of Professor G. Cornélissen, for example the project BIOCOS, with Medical University Graz, Austria, under Assoc. Professor Goswami, with Tohoku University Sendai, Japan under Professor Kohzuki and with other personalities from abroad. NONINVASIVE METHODS IN CARDIOLOGY 2020 18 NONINVASIVE METHODS IN CARDIOLOGY 2020 19 Chronobiologic Analyses of Weeklong around-the-Clock Records of Simultaneously Monitored Blood Pressure and Activity Germaine Cornelissen1 , Zainab Farah1 , Denis Gubin2 , Lyazzat Gumarova3 , Linda Sackett-Lundeen1 , Thomas Kazlausky4 , Kuniaki Otsuka5 , Jarmila Siegelova6 , Larry A Beaty1 1 Halberg Chronobiology Center, University of Minnesota, Minneapolis, MN, USA 2 Department of Biology, Medical University, Tyumen, Russia 3 Al-Farabi Kazakh National University, Almaty, Kazakhstan 4 Ambulatory Monitoring, Inc., New York, USA 5 Tokyo Women’s Medical University, Daini Hospital, Tokyo, Japan 6 Masaryk University, Brno, Czech Republic Correspondence: Germaine Cornelissen Halberg Chronobiology Center University of Minnesota, Mayo Mail Code 8609 420 Delaware St. S.E. Minneapolis, MN 55455, USA TEL +1 612 624 6976 FAX +1 612 624 9989 E-MAIL corne001@umn.edu Website: http://halbergchronobiologycenter.umn.edu/ Support: Halberg Chronobiology Fund University of Minnesota Supercomputing Institute A&D (Tokyo, Japan) Abstract Among the many different factors that influence blood pressure, activity was once thought to be the major determinant of the circadian variation in blood pressure. Whereas the endogenous nature of the circadian rhythm in blood pressure is no longer disputed, there is great interest in monitoring activity concomitantly with blood pressure. Herein, we reanalyze a dataset on weeklong ABPM records obtained concomitantly with actigraphy from 20 clinically healthy young adults. The purpose of this investigation is to review different approaches available for the characterization of the circadian variation in physiological variables such as blood pressure, heart rate, and activity. Topics covered include rhythm detection, the estimation of rhythm parameters, and the visualization of their waveform. Methods to examine how circadian rhythms of different variables may relate to each other are also discussed. NONINVASIVE METHODS IN CARDIOLOGY 2020 20 Introduction Most, if not all, physiological variables undergo predictable circadian variations [1]. Circadian rhythms are genetically anchored [2, 3], including that of blood pressure, which was long thought to be no more than a direct response to activity [4]. The endogenous nature of the circadian rhythm in blood pressure is apparent from its persistence during continued bedrest [5, 6], from its ability to free-run [7, 8], and more recently from the discovery of clock genes in the periphery as well as in the suprachiasmatic nuclei [4]. Many factors affect blood pressure [9]. Among them, activity plays an important role and can be easily monitored. Interest in measuring activity concomitantly with blood pressure stems in part from the merit of defining more precisely the active and resting spans, which may differ greatly among individuals. Herein, we re-analyze a dataset of weeklong ABPM and actigraphy records from clinically healthy young adults [10], with the aim to illustrate different approaches to characterize the circadian variation in variables such as blood pressure, heart rate, and locomotor activity. Subjects and Methods Study participants were 20 clinically healthy volunteers (14 women and 6 men), 20 to 54 years of age (mean ± SD: 26.5 ± 9.2). They were students and researchers, with mostly a sedentary work schedule, following mostly similar regular diurnal sleep-wake schedules. Four were overweight and one was obese. On the average, body mass index (BMI) ranged from 18.2 to 36.4 (mean ± SD: 22.7 ± 4.6). Each study participant provided concomitant weeklong records of blood pressure and activity. Blood pressure and heart rate were automatically measured around the clock at 30-min intervals by ambulatory blood pressure monitoring (ABPM), using the TM-2421 device from A&D (Tokyo, Japan). Wrist activity was recorded every minute using the MicroMotion Logger from AMI (Ardsley, NY). We use the zero-crossing mode (ZCM) to assess activity. ZCM measures movement frequency, which is represented by the number of times the voltage fluctuations of the analog signals exceed a predetermined threshold value. In addition to ZCM, the device also measures wrist temperature, light exposure, and sleep (0 or 1, representing awake or asleep, respectively). Of the 20 participants, 15 completed the 7-day/24-hour monitoring. Records from the other 5 were shorter, covering approximately 6 days. Blood pressure and heart rate measurements were taken at the hour and half-hour. Occasional missing values were linearly interpolated. Records that were slightly shorter than 6 or 7 full days were extrapolated in order for the records to cover an integer number of days. When gaps exceeded 90 minutes, interpolation was done by averaging data obtained at the same clock hour on other days. Data from the MicroMotion Logger were averaged over consecutive 30-minute intervals, and assigned to the midpoint, which matched the times of blood pressure and heart rate measurements. A template was prepared in Excel where the 30-min pre-processed data from both devices were entered in a specified cell range. In the same Excel sheet, formulae were entered to approximately compute the autocorrelation function of each variable, as well as the cross-correlation function of pairs of variables. Simple Pearson product moment correlation coefficients were computed instead of the exact autocorrelation and cross-correlation formulae. While not exact, they provide a good first NONINVASIVE METHODS IN CARDIOLOGY 2020 21 approximation of these functions. Plots of each autocorrelation and cross-correlation function were prepared in separate Excel charts. This template was saved, so that it could be copied onto another file and data from a different study participant entered in the specified cell range to replace those of the template file. This way, the autocorrelation and cross-correlation functions are automatically computed and all corresponding graphs are generated without effort. The pre-processed data were analyzed by least squares spectra [11, 12], using a fundamental period of 7 days and a frequency range from one cycle in 7 days to one cycle in 1.1 hour. Another sheet in the template Excel file accommodates the results from the least squares spectra in specified cell ranges for each variable. Noise levels are estimated, and plots are prepared of each spectrum in separate Excel charts. Results from least squares spectra from study participants were entered into the designated cell ranges of copies of the template Excel file to automatically obtain all plots. While it would have been preferable to use a fundamental period of 6 days instead of 7 days for those records that only covered 6 days, results related to the circadian variation are not affected by the choice of a 7-day fundamental component for all 20 records. Population-mean cosinor spectra were computed by averaging results from the individual least squares spectra. Since spectral analyses of all variables showed prominent about 24-hour and 12-hour components, 2-component models were used to reconstruct the circadian patterns of each variable. Stability (IS) and fragmentation (IV) are two indices that have been proposed to characterize the circadian variation in activity [13, 14]. IS is a signal-to-noise measure, calculated as the ratio between the variance of the average 24-hour pattern around the mean and the overall variance. IV estimates the intra-daily variability and gives an indication of the fragmentation of the rhythm (i.e., the frequency of transitions between rest and activity) and is calculated as the ratio of the mean squares of the difference between consecutive hours (first derivative) and the mean squares around the grand mean (overall variance). IS and IV are calculated based on hourly averages. IS and IV were computed from all study participants. The Student’s t test was used to compare the MESOR and circadian amplitude of each variable between men and women. Linear regression assessed relationships of the circadian parameters as a function of age and BMI. A P-value below 0.05 was considered to indicate statistical significance. Results Figure 1 illustrates the autocorrelation (ACF) and cross-correlation (CCF) functions of systolic blood pressure (SBP), ZCM, and wrist temperature (Temp). The presence of a circadian rhythm in each variable can be clearly seen by the naked eye. It can also be seen from the cross-correlation functions that systolic blood pressure and ZCM are in phase, but that wrist temperature is out of phase with both systolic blood pressure and ZCM. NONINVASIVE METHODS IN CARDIOLOGY 2020 22 Figure 1. Left: Autocorrelation functions of systolic blood pressure (top), activity (ZCM, middle), and wrist temperature (bottom) of one subject. Right: Cross-correlation functions of systolic blood pressure and ZCM (top), of systolic blood pressure and wrist temperature (middle), and of ZCM and wrist temperature (bottom). Note that the prominent circadian variation in these three variables is in phase between systolic blood pressure and ZCM, but that these variables are out of phase with respect to wrist temperature. © Halberg Chronobiology Center Figure 2 illustrates the least squares spectra of these three variables corresponding to the autocorrelation and cross-correlation functions shown in Figure 1. A large spectral peak at a frequency of one cycle per 24 hours emerges from the noise level in each case. Smaller peaks at harmonics of the circadian variation are also present. Population-mean cosinor spectra summarizing results from all 20 study participants clearly detect with statistical significance the presence of spectral components at frequencies of one and two cycles per 24 hours, Figure 3. Figure 2. Least squares spectra of systolic blood pressure (left), activity (ZCM, middle), and wrist temperature (right) of one study participant. The circadian variation is prominent, as seen by the large spectral peak at a frequency of 1 cycle per 24 hours. © Halberg Chronobiology Center NONINVASIVE METHODS IN CARDIOLOGY 2020 23 Figure 3. Population-mean cosinor spectra of systolic blood pressure (left), activity (ZCM, middle), and wrist temperature (right), summarized across all 20 study participants. The 24-hour and 12-hour components are statistically significant. © Halberg Chronobiology Center Figure 4. Circadian waveform of systolic blood pressure (top), activity (ZCM, middle), and wrist temperature (bottom), reconstructed based on 2-component model, shown with the data expressed as a percentage of each record’s arithmetic mean. © Halberg Chronobiology Center The circadian patterns of systolic blood pressure, activity, and wrist temperature are reconstructed in Figure 4 based on a 2-component model, consisting of cosine curves with periods of 24 and 12 hours, derived from results of the population-mean cosinor spectra. NONINVASIVE METHODS IN CARDIOLOGY 2020 24 Discussion The stability and fragmentation indices averaged (± SD) 0.571 ± 0.152 and 0.475 ± 0.090, respectively, reflecting the relatively young population investigated herein. IS depends on the record length. It is higher in the 7-day (0.620) than in the 6-day (0.426) records (t = 2.922, P=0.009). It also correlates with activity (MESOR of ZCM) (r=0.461, P=0.041), and with the circadian amplitude of ZCM (r=0.823, P<0.001). It can be viewed as reflecting the percentage variance accounted for by the circadian variation in activity. Indeed, IS correlates strongly with the percentage rhythm of the circadian rhythm of ZCM, whether it is approximates by a single 24-hour component (r=0.890, P<0.001) or a 2-component model consisting of cosine curves with periods of 24 and 12 hours (r=0.916, P<0.001). Anticipated gender differences are detected, despite the relative small sample size of this population. Women have a lower blood pressure than men (SBP: 112.8 vs. 129.4 mmHg, t = 3.996, P<0.001; DBP: 67.9 vs. 76.4 mmHg, t = 3.533, P-0.002). Women have also a smaller circadian amplitude of blood pressure as compared to men (SBP: 10.2 vs. 16.3 mmHg, t = 4.760, P<0.001; DBP: 7.9 vs. 11.3 mmHg, t = 2.748, P=0.013). Linear regression analyses as a function of age, BMI, and also accounting for gender find that the MESOR of heart rate is higher in women than in men (t = 2.441, P=0.027); that it decreases with advancing age (t = 3.742, P=0.002); and that it increases with BMI (t = 2.559, P=0.021). The model accounts for 57% of the total variance (F = 7.076, P=0.003). A similar model shows that the circadian amplitude of heart rate is larger in women than in men (t = 2.654, P=0.017) and that it decreases with advancing age (t = 4.183, P<0.001), accounting for 61% of the total variance (F = 8.379, P=0.001). The acrophase of wrist temperature occurring during the night deserves some comment. Core temperature usually peaks in the afternoon, like activity, heart rate, and blood pressure. Differences in the circadian acrophase between distal skin temperature and body temperature are mainly related to counterbalanced physiologic processes of heat production and heat dissipation. Skin temperature measured on limbs corresponds mainly to distal vasodilation and heat transfer. Its circadian acrophase occurs approximately 90 to 120 minutes after the circadian acrophase of melatonin. Rectal, oral, and axillary temperatures are a closer approximation of core temperature and peak in the late afternoon or evening. They correspond to distal vasoconstriction and parallel heating of internal organs. For these reasons, the circadian acrophase is inverse to that of melatonin [13-15]. To summarize, the circadian rhythm of blood pressure, heart rate, activity and temperature accounts for a sizeable portion of the overall variance. These variables can easily be monitored around the clock. A number of different approaches are available to characterize the circadian variation in these variables and to explore how they are related to each other. Organizing the data in a systematic way in Excel facilitates the automatic analysis and graphic visualization of the results when a given procedure needs to be applied repeatedly to different sets of data that follow a specific protocol. References 1. Halberg F. Chronobiology. Annu Rev Physiol 1969; 31: 675-725. 2. Halberg F, Cornelissen G, Halberg E, Halberg J, Delmore P, Shinoda M, Bakken E. Chronobiology of human blood pressure. Medtronic Continuing Medical Education Seminars, 4th ed. Minneapolis: Medtronic Inc.; 1988. 242 pp. 3. Xu Y, Pi W, Rudic RD. Old and new roles and evolving complexities of cardiovascular clocks. Yale Journal of Biology & Medicine 2019; 92 (2): 283-290. NONINVASIVE METHODS IN CARDIOLOGY 2020 25 4. Pickering TG, Harshfield GA, Kleinert HD, Blank S, Laragh JH. Blood pressure during normal daily activities, sleep, and exercise. Comparison of values in normal and hypertensive subjects. JAMA 1982; 247 (7): 992-996. 5. Reinberg A, Halberg F, Ghata J, Gervais P, Abulker Ch, Dupont J, Gaudeau Cl. Rythme circadien de diverses fonctions physiologiques de l’homme adulte sain, actif et au repos (pouls, pression artérielle, excrétions urinaires des 17-OHCS, des catécholamines et du potassium). Test du cosinor. Association des Physiologistes, Grenoble, 19-21 juin 1969. J Physiol (Paris) 1969; 61 (Suppl. 2): 383. 6. Stadick A, Bryans R, Halberg E, Halberg F. Circadian cardiovascular rhythms during recumbency. In: Tarquini B. (Ed.) Social Diseases and Chronobiology: Proc. III Int. Symp. Social Diseases and Chronobiology, Florence, Nov. 29, 1986. Bologna: Società Editrice Esculapio; 1987. pp. 191-200. 7. Halberg F, Good RA, Levine H. Some aspects of the cardiovascular and renal circadian system. Circulation 34: 715-717, 1966. 8. Brockway B, Hillman D, Halberg F. Circadian desynchronization of telemetered rat blood pressure (BP) from heart rate (HR) models clinical precedents. Chronobiologia 1990; 17: 165-166. 9. Gubin DG, Cornelissen G. Factors that must be considered while solving the problem of adequate control of blood pressure. Journal of Chronomedicine 2019; 21 (2): 8-13; https://doi. org/10.36361/2307-4698-2019-21-2-8-13. 10. Gumarova L, Farah Z, Cornelissen G. Interrelationships of hemodynamics and activity rhythms. Abstract, CardioPalooza 2017, University of Minnesota 11. Cornelissen G. Cosinor-based rhythmometry. Theoretical Biology and Medical Modelling 11: 16, 2014. 12. Gierke CL, Corneélissen G. Chronomics analysis toolkit (CATkit). Biological Rhythm Research47: 163-181, 2016. 13. Sarabia JA, Rol MA, Mendiola P, Madrid JA. Circadian rhythm of wrist temperature in normalliving subjects: A candidate of new index of the circadian system. Physiology & Behavior 2008; 95 (4): 570-580. https://doi.org/10.1016/j.physbeh.2008.08.005 14. Bracci M, Ciarapica V, Copertaro A, Barbaresi M, Manzella N, Tomasetti M, Gaetani S, Monaco F, Amati M, Valentino M, Rapisarda V, Santarelli L. Peripheral skin temperature and circadian biological clock in shift nurses after a day off. Int J Mol Sci 2016; 17 (5): 623. doi: 10.3390/ ijms17050623. PMID: 27128899; PMCID: PMC4881449. 15. Martinez-Nicolas A, Madrid JA, García FJ, Campos M, Moreno-Casbas MT, Almaida-Pagan PF, Lucas-Sanchez A, Rol MA. Circadian monitoring as an aging predictor. Scientific Reports 2018; 8, 15027. https://doi.org/10.1038/s41598-018-33195-3 NONINVASIVE METHODS IN CARDIOLOGY 2020 26 NONINVASIVE METHODS IN CARDIOLOGY 2020 27 Some Lessons Learned from a 43-year Record of SelfMeasurements by a Physician-Scientist Linda Sackett Lundeen1 , Larry A Beaty1 , Jarmila Siegelova2 , Yoshihiko Watanabe3 , Germaine Cornelissen1 1 Halberg Chronobiology Center, University of Minnesota, Minneapolis, MN, USA 2 Department of Physiotherapy, Department of Sport Medicine and Rehabilitation, Faculty of Medicine, Masaryk University, St. Anna Teaching Hospital, Brno, Czech Republic 3 Tokyo Women’s Medical University, Daini Hospital, Tokyo, Japan Correspondence: Germaine Cornelissen Halberg Chronobiology Center University of Minnesota, Mayo Mail Code 8609 420 Delaware St. S.E. Minneapolis, MN 55455, USA TEL +1 612 624 6976 FAX +1 612 624 9989 E-MAIL corne001@umn.edu Website: http://halbergchronobiologycenter.umn.edu/ This article is dedicated to the memory of two outstanding pioneers and mentors of Chronobiology: Erhard Haus M.D., Ph.D. and Franz Halberg, M.D., Dr. multi Support: Halberg Chronobiology Fund University of Minnesota Supercomputing Institute A&D (Tokyo, Japan) Abstract The study participant, a pathologist and scientist-chronobiologist self-measured systolic (S) and diastolic (D) blood pressure (BP), heart rate (HR) and oral temperature (Tb) for 43 years, spanning from 1965 to 2013 and from 38 to 86 years of age, with some times of no data collection, the longest between 1966 and 1971. The number of samples varied mostly between 4 and 12 samples per day. Mean arterial pressure (MAP), pulse pressure (PP), and pulse pressure product (PPP) were calculated from SBP, DBP, and HR. In the spring of 1973, he was diagnosed with Essential Hypertension (EH) and was started on anti-hypertensive medication(s). His intention for collecting these data was originally to look at changes during intercontinental flights and later became to monitor his treatment of EH. It was of interest to evaluate the circannual variation in these variables, to look for changes over time and changes in response to efficacy of the medications for the control of his EH. Introduction Chronobiology analysis is very important in the evaluation of blood pressure. Much work has been done on blood pressure rhythms from neonates (Halberg et al., 1986) to centenarians (Ikonomov et al., NONINVASIVE METHODS IN CARDIOLOGY 2020 28 1991) and in many different frequencies from ultradians and circadians to infradians (circasemiseptan, circaseptan, monthly, circannual, transyears, and even solar activity cycles) (Cornelissen et al., 1994; Cornelissen et al., 1992; Nicolau et al., 1986). Blood pressure and oral temperature have been used to study jet lag (Halberg et al., 2007, Haus et al., 1981) and Shiftwork (Halberg J et al., 1989) for many years. This unusual long time series provides a unique opportunity to study multi-frequency rhythms, trends, efficacy of medications, and possible risks of adverse effects, among others. This kind of information cannot be obtained from 24-hour or 7-day/24-hour records from populations. Subject and Methods The study participant (EH) was a male pathologist and chronobiologist born September 8, 1926 in Austria. He first started self-measuring his SBP, DBP, HR, and temperature when he was a Pathology Instructor and Post Doctorate at the University of Minnesota. He was 38 years old when he began collecting these measurements on April 15, 1965, continuing until July 6, 1966. The majority of the 4 to 8 daily measurements where during the waking span, with very few during the sleeping time. EH returned to performing self-measurements again on March 9, 1971 at 42 years of age, while he was the Medical Director in the Department of Anatomic and Clinical Pathology (1969-2003) at St. Paul Ramsey Hospital, St. Paul, Minnesota. During the years of measurements, he was also an Associate Professor (1961-1972) and Professor (1980-2013) in Laboratory Medicine and Pathology at the University of Minnesota, the Ramsey and Washington County Medical Examiner (1979-1985), and the Head of Pathology/Chronobiology Research at Regions Hospital (1971-2013). He continued to collect 4 to 12 measurements per day until the day of his death at 86 years of age on June 14, 2013 from a cardiac arrest at the same hospital (renamed Regions Hospital, St. Paul, Minnesota), where he was still working as a Staff Pathologist. As he aged, more samples were measured when he would wake up at night, therefore providing more measurements throughout the 24 hours. There were occasional times of interruption over the 43 years of measurements, from a few days at a time to weeks (i.e., from 3/6/79 to 3/21/79 while he had a total hip replacement; 4/19/85 - 5/7/85, and 7/19/85 - 8/6/85 during trips to help with research studies in Romania), to months at a time (i.e., from 9/3/85 to 12/9/85). Other factors that may have prevented sampling for short periods of time may include events like a broken blood pressure monitor or thermometer or being too busy. There were many trips over the 43 years of measurements, some domestic and many worldwide. These trips were not evaluated in the present analysis. When measurements started, EH was using a sphygmomanometer with a blood pressure arm cuff for SBP and DBP, a mercury oral thermometer, and did a manual wrist HR reading. At some time during the end of the 1980s, he did switch from a mercury thermometer to a digital thermometer. On September 17, 1988, he switched to a finger blood pressure monitor and being the ultimate researcher, he did some comparison measurements between the finger monitor and the sphygmomanometer cuff method between then and the end of 1988. In 1991, he changed monitors again, doing some comparisons between the old and the new monitors. It was also important to him to do comparisons between holding the BP monitor at heart level vs. his arm on the table, which he did in 1992 with his old and new monitors, and again in 1993 when he switched from a finger monitor to an automatic cuff monitor. Starting April 4, 1971, EH self-rated his mood at the same times as the other measurements each day. Diagnosis and Treatment: In the spring of 1973, EH was diagnosed with Essential Hypertension (based only on daytime values), starting Reserpine (0.1 mg mornings and nighttime) on May 15, 1973. Reserpine was replaced with Thiazide (50 mg x2) in mid-1977, followed by the addition of Propranolol NONINVASIVE METHODS IN CARDIOLOGY 2020 29 (Inderal 20 mg) on November 24, 1978 to be taken in the morning and at noon, with Thiazide (50 mg) continuing in the morning. This regimen continued when in March 1980, Propanolol (Inderal 20 mg) was taken 3x/day (morning, noon, and evening) and Thiazide (50 mg) 2x/day (morning and noon). This regimen continued with some changes in dosing and timing until February 24, 1987. Medications, dosing, and timing changed multiple times over the years. In 2012 and 2013, his regimen included the following medications (doses) and timing: in the morning: Furosemide (20 mg), Chlorthalidone (25 mg), and Metformin (1,000 mg), and in the evening: Nifedipine (90 mg), Ramipril (10 mg), Simvastatin (40 mg), Spironolactone (25 mg), Metformin (1,000 mg), and often at bedtime: Melatonin (5 mg). In February 2002, EH was diagnosed with Non-Insulin Dependent (Type II) Diabetes Mellitus (NIDDM), starting Metformin (1,000 mg) on February 15, 2002, which continued until his death. Analysis: The measured SBP, DBP and HR were used to calculate the Mean Arterial Pressure (MAP=((2xDBP)+SBP)/3), Pulse Pressure (PP=SBP-DBP), and Pulse Pressure Product (PPP=SBPxHR/100). Means, Standard Deviations (SDs), and Number of Data Points per month for SBP, DBP, HR, MAP, PP, PPP, Temperature, and Mood were calculated by a routine in R for each month of each year of the entire data span. Monthly means and SDs within each yearly span were then analyzed fitting a 1-year cosine curve to the data. The rhythmometric results at a trial period of 1 year (MESOR, Amplitude, and Acrophase), obtained each year for each variable were then analyzed by Population-Mean Cosinor. Analysis of Variance (ANOVA) was applied to visualize the yearly patterns for comparison with similar analyses performed on similar data by another chronobiologist (Sothern et al., 2004). Results Figures 1-4 show the long-term trends as chronograms in 7 of the 8 variables measured over the 43 years. There are very large long-term changes in most variables investigated. A circannual rhythm cannot be seen by the naked eye in these data. The analyses by population-mean cosinor detect a statistically significant circannual rhythm in heart rate and in the pulse-pressure product, but not in systolic or diastolic blood pressure (Figure 1). NONINVASIVE METHODS IN CARDIOLOGY 2020 30 Figure 1: Circannual variation is not discernable in chronograms due to huge trends in 43 years of selfmeasurements. A statistically significant circannual rhythm detected in heart rate (top left) and pulse-pressure product (top right), but not in systolic blood pressure (lower left) or diastolic blood pressure (lower right). Diagnosis of Essential Hypertension (EH) and Non-Insulin Dependent Diabetes Mellitus (NIDDM) denoted by arrows. There are also very large changes in the variability of all the variables self-measured over 43 years. Some of these changes may stem from many different factors (different schedules of taking the measurements, including the number of samples taken each day, changes in the devices used to take the measurements, change in medications (dosing and timing), work load, and intercontinental flights). Figure 2 shows the variability in HR, PPP, SBP, and DBP, as gauged by their monthly SDs. NONINVASIVE METHODS IN CARDIOLOGY 2020 31 Figure 2: Large variability in heart rate (top left), pulse-pressure product (top right), systolic blood pressure (lower left), and diastolic blood pressure (lower right) during 43 years of self-measurements. Diagnosis of Essential Hypertension (EH) and Non-Insulin Dependent Diabetes Mellitus (NIDDM) denoted by arrows. Despite the thorough monitoring of blood pressure two to twelve times per day, and despite the fact that this was a pathologist-scientist-chronobiologist knowledgeable in the treatment of high blood pressure who was on anti-hypertensive medication(s), one cannot say that his blood pressure was well controlled. Some trends in the monthly SDs do follow the changes in the monthly means; however, this is not consistently the case, as is shown toward the end of the record. This phenomenon is shown in both the MAP and PP in Figure 3. NONINVASIVE METHODS IN CARDIOLOGY 2020 32 Figure 3: Large increases in MAP and PP over 43 years of self-measurements despite the use of antihypertensive medications. Monthly means (top figures), monthly variation as demonstrated by the SDs (lower figures), MAP (left side), and PP (right side). Diagnosis of Essential Hypertension (EH) and Non-Insulin Dependent Diabetes Mellitus (NIDDM) denoted by arrows. Evaluating the oral temperature of EH is an excellent example of the fact that 98.6°F is not necessarily everyone’s “normal” temperature. The means of every month over the 43 years are almost all less than 98.6°F, which includes those times when he had fever during illnesses. Most temperatures were missing from August 17, 1985 until the end of the year, and no temperatures were measured from January 1, 1986 through April 28, 1987. Oral temperature is an illustrative example of the merit of analyzing data one year at a time. The circannual variation is put to the fore by expressing each year’s data as a percentage of that year’s mean value, and then averaging the relative data across all years. Temperature is higher in the summer by only about 0.1°F as compared to the winter, while the monthly SD is highest in December (Figure 4). EH also registered a self-rated value for his mood at the time of most measurements as a number between 2 and 6, with 2 being okay and 6 being very excited or stressed, with most of his mood values being 5. The results of the population-mean cosinor for the monthly means and the monthly SDs for all variables are shown in Table 1. NONINVASIVE METHODS IN CARDIOLOGY 2020 33 Figure 4: Circannual changes in oral temperature shown during 43 years of self-measurements. Monthly means (top left), monthly variations as demonstrated by the SDs (lower left), expression of each year’s data as a percentage of that year’s mean value, and then averaging the relative data across all years for monthly means (top right), and SDs (lower right). Diagnosis of Essential Hypertension (EH) and Non-Insulin Dependent Diabetes Mellitus (NIDDM) denoted by arrows on left side of figure. Table 1: Population-Mean Cosinor Results for 43 years of self-measurements. Even though there was not a statistically significant circannual rhythm by population-mean cosinor in all 43 years of self-measurements, each variable did show a statistically significant yearly variation in some, but not all years. In evaluating the percentage of occurrences when a circannual rhythm could be documented with statistical significance for each variable, it was surprising to see it was less than 50% (Figure 5). NONINVASIVE METHODS IN CARDIOLOGY 2020 34 Figure 5: A circannual rhythm is not invariably detected each year in 43 years of self-measurements. The wide distribution of acrophases over the 43 years of self-measurements for many of the variables evaluated supports the conclusion that a circannual variation is not discernable. Figure 6 shows that systolic blood pressure is not statistically significant as the 95% confidence ellipse overlaps the center and the amplitude is quite small. The acrophase estimate from the population-mean cosinor for SBP is -27° (January 28), however, by adding the acrophases for each individual year the wide distribution of acrophases supports the conclusion that a circannual variation is not discernable in systolic blood pressure. For systolic blood pressure, a yearly rhythm is documented (P<0.05) in 26% of the years, and reaches borderline significance (0.05