Circadian heart rate and blood pressure variability in...

Appendices

INFORMATION SHEET

1 Name of the

investigators

: Nishtha Vaidya

2 Name of

organization

: Pt. Ravishankar Shukla University

Chronobiology Laboratory

School of Life Sciences

Raipur – 492 010

3 Introduction : I am Nishtha Vaidya pursuing Ph.D. in the Chronobiology

Laboratory, School of Life Sciences, Pt. Ravishankar

Shukla University, Raipur – 492 010.

4 Purpose of

research

: Blood pressure variability (BPV) is one of the recognized

risk factor for different types of cardiovascular diseases.

Through this research work we will investigate the blood

pressure variability in normotensive and hypertensive

subjects.

5 Voluntary

participation

: Your participation in this work is entirely voluntary. It is

your choice to participate or not.

6 Procedure and

protocol

: The participants will wear a device named ABPM for 2-4

days. Questionnaires for assessment of Morningness-

Eveningness preference will be administered to each

participant.

7 Duration : 2-4 days

8 Side effects : None

9 Risk : None

10 Benefits : By this research work I am going to give you information

about your 24-h blood pressure profile.

11 Confidentiality : The information collected will be kept confidential.

12 Sharing the

result

: The result that we get from this work will be definitely

shared with you.

13 Right to refuse

or withdraw

: If you do not wish to participate then you have the right to

refuse or withdraw.

14 Whom to

contact

: If you have any queries then you can contact:

Chronobiology Laboratory, School of Life Sciences, Pt.

Ravishankar Shukla University, Raipur – 492 010

Phone: 09826654829; 09826551089

Email: [email protected]; [email protected]

Appendix-I

mailto:[email protected]


CONSENT LETTER

I am willing to participate in the research work carried out by the research

scholar (Bioscience) of the School of Life Sciences, Pt. Ravishankar

Shukla University, Raipur. She has already given me all information

about their research work. They are using non-invasive device for the said

purpose. My participation in this study is entirely voluntary.

Name of the investigators: Nishtha Vaidya

Name of the organization: Chronobiology Laboratory

School of Life Sciences

Pt. Ravishankar Shukla University

Raipur – 492 010

Name of Participant:

Address:

Telephone no.:

Email ID:

Signature of Participant:

Thumb impression of Participant

(if illiterate):

Date:

Place:

Name of Witness:

Signature of Witness:

Date:

Appendix-II

Subject Code:

Biographical Data Sheet

Name:

Sex: M/F

Age (Y):

Date of birth: DD/MM/YY

Height (cm):

Weight (kg):

Marital status: M/UM

Married since (Year):

Children (No.):

Job/Profession/Designation:

Length of Service (Y):

Education:

Full postal address:

Office:

Home:

Phone Office: Home:

Email

Work type: Diurnal Nocturnal Shift work On call shift

Blood Group: A B AB O

Rh + Rh -

Are you a smoker? Yes No

If yes, how frequently?

Regularly Quite often Occasionally

Do you take alcohol? Yes No



Do you take

sleeping pills? Yes No



Major health problem,

if any? Hypertension

Hypotension Heart disease

Diabetes

Asthma Any other

Date: Signature of the subject

UGC-DRS-SAP-Chronobiology School of Life Sciences, Pt. RSU, Raipur, 0771-2262631

Appendix-III

List of Publications & Reprints

List of Publications and Reprints

Ph.D. Thesis submitted by Nishtha Vaidya 222

Research publications:

Sultana R., Vaidya N., Parganiha A. and Pati A.K. (2007). Dichotomy in human

population based on variability in peak spread of rest- activity rhythm in respect

of internal phase reference point. Biological Rhythm Research, 39 (2): 109-121.

Vaidya N., Pati A.K and Parganiha A. (2011). Circadian variability and nocturnal

dipping pattern in blood pressure in young normotensive subjects. Biological

Rhythm Research. (Accepted).

Pande B., Rathod G., Vaidya N., Nag C., Parganiha A. and Pati A. K. (2011). Non-

auditory effect of community noise on human interval timing: an exploration.

Biological Rhythm Research. (Submitted after revision).

Papers presented in Symposia/Conferences:

National Symposium

Vaidya N. and Parganiha A. (2005). “Rest-activity rhythm and 120-second time

estimation in apparently healthy human subjects” XVII National Symposium of

Chronobiology, Department of Zoology, Banaras Hindu University, Varanasi,

October 1-3, p – 32.

Vaidya N. Parganiha A. and Pati A.K. (2006). “Characteristics of rest-activity rhythm

in apparently healthy human subjects: analyses of time series at different epoch

length”. XVIII National Symposium of Chronobiology, Department of Zoology,

North-Ester Hill University, Shillong, November 8-10, p – 20.

Vaidya N. (2007). “Circadian rhythm in blood pressure and related variables in

apparently healthy subjects as function of gender, age and body surface area”

XIX National Symposium of Chronobiology, Department of Animal Behaviour

and Physiology, School of Biological sciences, Madurai Kamaraj University,

Madurai, Tamil Nadu, December 7-9, p – 42.

Vaidya N. and Parganiha A. and Pati, A.K. (2008)."Gender-linked variability in

dipping patterns in Diastolic blood pressure" 78th

Annual Session and

Symposium on “Novel Approaches for Bio-medical Research” organized by

Panjab University, Chandigarh, November 21-23, p – 17.

Vaidya N. Parganiha A. and Pati A.K. (2008). “Circadian rhythm and dippind pattern

in blood pressure in human subjects”. XX National Symposium of

Chronobiology, School of Life Sciences Pt. Ravishankar Shukla University

Raipur, December 27-29, p – 45.

Vaidya N. (2009). “Circadian blood pressure variability and nocturnal dipping pattern

in human subjects”. 7th

Chhattisgarh Young Scientist Congress, Indira Gandhi

Krishi Vishwavidyalay, Raipur, Feb. 28 Fab-01 March, p – 23.

Vaidya N. (2009). “Blood pressure and heart rate rhythm as function of gender and

nocturnal dipping pattern” 79th

Annual Session and National Symposium on



“Science and Technology and the Young (Career, Creativity, Excitement)”

organized by Calcutta University, Kolkata, December 14-17, p – 35.

Vaidya N. Parganiha A. and Pati, A.K. (2010). “Circadian blood pressure and heart

rate variability in young southeast Indian population” XXI National

Symposium on Chronobiology, P.G. Department of Zoology, Jamshedpur Co-

operative College, Jamshedpur, Kolhan University, February 2-4. p – 35.

Vaidya N. (2010). “Characteristics of blood pressure and heart rate rhythm in younger

and older adults” 8th

Chhattisgarh Young Scientist Congress, Pt. Ravishankar

Shukla University, Raipur, Chhattisgarh, April 9-10, p – 127.

Vaidya N, Pati A. K. and Parganiha A. (2010). “Ambulatory blood pressure

monitoring: an essential tool for assessment of day-night blood pressure variation

among normotensive subjects”. 80th

Annual Session of the NASI, and

Symposium on “Climate change-research, awareness and capacity building”

organized by Jaipur National University, Jaipur, December, 2-4. p – 26.

Vaidya N. (2011). “Circadian blood pressure variability in a cohort of young subjects

from Chhattisgarh as function of gender” 98th

Indian Science Congress organized

by SRM University, Chennai, Tamil Nadu, January 3-7, p – 287.

Vaidya N. (2011). “Day-night blood pressure variations among young southeast

indian population”. 9th

Chhattisgarh Young Scientist Congress. Bastar

vishwavidyalaya, Dharampura, Jagdalpur, Chhattisgarh, Feb 28-March 01, p –

21

Pande B., Rathod G., Vaidya N., Nag C., Parganiha A. and Pati A. K. (2011). “Non-

auditory effect of noise on human interval timing”. XXII National symposium on

chronobiology. Department of Zoology, Kurukshetra University, Kurukshetra,

March 15-11. p – 21.

International Symposium

Pati A.K., Kar A., Sultana R., Vaidya N. and Parganiha A. (2009). Long sleepers tend

to be nondippers: is it a paradox? Significance of Chronobiology to the healthy

and well-being of today‟s society. The third international congress of applied

Chronobiology and chronomedicine (ICACC), Akko (Acre), Israel, May, 17-

22. p – 76.

Vaidya N., Pati A. K. and Parganiha A. (2010). “Day-night variation in blood

pressure and heart rate as function of age and dipping pattern in human subjects”

accepted for poster presentation at 26th

Conference of the International Society

for Chronobiology, (ISC), Vigo (Spain), July 5-9, p – 73 (Supported by

CCOST, Chhattisgarh, could not attend the conference).

Vaidya N., Pati A. K. and Parganiha A. (2011). “Circadian blood pressure variability

among southeast Indian population as function of age, gender and dipping

pattern”. Third world congress on chronobiology, Puebla (Mexico), May 5-9, p

– 152 (Supported by DST, New Delhi; UGC, New Delhi).



Attended Workshop/ Science Conclave:

Attended 5th

SERC School in Chronobiology, February 1-11, 2007, Department of

Zoology, Ch. Charan Singh University, Meerut, Uttar Pradesh, 250004.

Attended one day Acquaintance Program of IUAC, New Delhi at Pt. Ravishankar

Shukla University, Raipur 20th

July 2007.

Attended a Multinational Graduate Course on Basic Chronobiology with Reference to

Chronomedicine, November 2-7, 2008, Organized under the joint auspices of

The Indian Society for Chronobiology and The International Society for

Chronobiology, Held at Raipur, India.

Attended a National Workshop on „Recent Trends in Chronomedicine‟ (Under the

auspices of Indian Society for Chronobiology and sponsored by UGC-SAP,

Department of Biochemistry and Biotechnology March 6-7, 2010) organized at

the Department of Biochemistry and Biotechnology, Annamalai University,

Annamalainagar, Chidambaram, Tamil Nadu.

Attended a one day seminar on Environmental issues & challenges, March 29, 2010,

Organized by Pt. Ravishankar Shukla University Raipur.

Attended Third Science Conclave: A Congregation of nobel laureates, organized by

Indian Institute of information technology, Allahabad, 8-14 Dec. 2010.

Award:

Jurey award for Best Poster presentation on XIX National Symposium of

Chronobiology, 2007, Department of Animal Behaviour and Physiology School

of Biological Sciences, Madurai Kamaraj University, Madurai, Tamil Nadu, 7-

9 December.

Best Lab Presentation award on Vth

SERC School of Chronobiology, 2007,

Department of Zoology, Ch. Charan Singh University, Meerut, Uttar Pradesh,

01-11 February.

Pt. Ravishankar Shukla University research scholarship (JRF) award for the year

2007-2008.

Award for Senior Research Fellow (SRF), Council of Scientific and Industrial

Research (CSIR), Human Recource Development Group, New Delhi, 2010.

PLEASE SCROLL DOWN FOR ARTICLE

This article was downloaded by: [INFLIBNET India Order]On: 22 October 2009Access details: Access Details: [subscription number 909878480]Publisher Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Biological Rhythm ResearchPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t713734219

Dichotomy in human population based on variability in peak spread of rest -activity rhythm in respect of internal phase reference pointRazia Sultana a; Nishtha Vaidya a; Arti Parganiha ab; Atanu Kumar Pati a

a School of Life Sciences, Pt. Ravishankar Shukla University, Raipur, India b INSERM U776, RythmesBiologiques et Cancers, Hôpital Paul Brousse, Villejuif, France

First Published on: 10 July 2007

To cite this Article Sultana, Razia, Vaidya, Nishtha, Parganiha, Arti and Pati, Atanu Kumar(2007)'Dichotomy in human populationbased on variability in peak spread of rest - activity rhythm in respect of internal phase reference point',Biological RhythmResearch,39:2,109 — 121

To link to this Article: DOI: 10.1080/09291010701324749

URL: http://dx.doi.org/10.1080/09291010701324749

Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf

This article may be used for research, teaching and private study purposes. Any substantial orsystematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply ordistribution in any form to anyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss,actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directlyor indirectly in connection with or arising out of the use of this material.

http://www.informaworld.com/smpp/title~content=t713734219

http://dx.doi.org/10.1080/09291010701324749

http://www.informaworld.com/terms-and-conditions-of-access.pdf

Dichotomy in human population based on variability inpeak spread of rest – activity rhythm in respect of internalphase reference point

RAZIA SULTANA1, NISHTHA VAIDYA1, ARTI PARGANIHA1,2, &

ATANU KUMAR PATI1

1School of Life Sciences, Pt. Ravishankar Shukla University, Raipur – 492010, India and 2INSERM

U776, Rythmes Biologiques et Cancers, Hopital Paul Brousse, Villejuif 94800, France

AbstractThe present study was aimed at ascertaining the best reference point for the peak in rest – activity rhythmin a group of apparently healthy randomly chosen young university students consisting of nine males andseven females. The rest – activity was monitored by using an actiwatch (AW64, Mini Mitter Co. Inc.,USA) at one-minute epoch length. Data were analyzed with the help of Actiware sleep software, Cosinorrhythmometry, SPSS software and Excel Analysis Toolpak. A statistically significant circadian rhythm inrest – activity was documented in all subjects, irrespective of gender. Further, the subjects were dividedinto two groups depending upon the variability in their computed peaks determined with reference to theconventional mid-night, start-sleep, mid-sleep and end-sleep timings. The peak spread in each case andin each subject was computed. Results indicate that mid-sleep and end-sleep timings produce peakspreads that are considerably shorter than the conventional phase reference timing, i.e., local mid-night.Thereafter, two distinct groups, namely ‘‘mid-sleep group’’ (MSG) with the least peak spread when themid-sleep timing was taken as phase reference and ‘‘non-mid-sleep group’’ (NMSG) with the least peakspread when either start-sleep or end-sleep timing was taken as phase reference, were identified. Furthera statistically highly significant difference between amplitudes in MSG and NMSG (p5 0.01) wasobtained. A negative relationship was obtained between fragmentation index and amplitude in NMSG(r¼70.756). We have a hunch that there could be a phenotypical variation among the humanpopulation with respect to the mechanism of phase resetting. Furthermore, the amplitude could be asignificant predictor of fragmentation index in the model that we fitted to elucidate an inverserelationship between them.

Keywords: Rest – activity, peak, phase reference point, phenotypical variations

Introduction

Almost all physiological processes exhibit rhythms – outputs of the biological clock (oscillator)

mechanism. Biological clocks estimate local time cues for resetting themselves to carry along

with the environment. The process of resetting is termed entrainment and the environmental

stimuli that provide temporal information are called zeitgebers (Carl et al. 2003). The light –

dark cycle is known as the most dominant zeitgeber, generated by rotation of the earth.

Correspondence: Atanu Kumar Pati, School of Life Sciences, Pt. Ravishankar Shukla University, Raipur – 492 010, India.

Tel: þ91-771-2262631 (Office); þ91-771-3292885 (Lab.). Fax: þ91-771-2262583. E-mail: [email protected]

Biological Rhythm Research

April 2008; 39(2): 109 – 121

ISSN 0929-1016 print/ISSN 1744-4179 online � 2008 Taylor & Francis

DOI: 10.1080/09291010701324749

Downloaded By: [INFLIBNET India Order] At: 12:29 22 October 2009

In addition, social cues, timings of food availability, temperature cycle etc. also provide

temporal input to the biological clocks (Piccione & Caola 2002). Entrainment is attainable

when the oscillator is driven by a strong zeitgeber or an oscillator is weak enough to be driven

by zeitgeber (Roenneberg et al. 2005). In order to reset, the clock modifies its period and phase

(Aschoff 1960, 1981). Studying biological clocks in entrained conditions is important for

learning genetic differences or phenotypical variation in the physiology of biological clocks

(Roenneberg et al. 2003).

Entrainment is a complex mechanism. There are many unanswered questions that need to

be solved, like why and how entrainment occurs? What is the need for entrainment if

biological clocks are self-sustained? Why are some organisms arrhythmic and do not require

rhythms for environmental adaptation?

Every organism entrains differently with respect to the standard environmental time giver.

Many masking factors hinder the expression of circadian rhythm. These masking effects are

defined as the stimuli that directly affect the rhythms without entraining the main oscillator

(Roenneberg et al. 2005). The mechanisms of clock entrainment are complicated in humans,

especially since they have artificial lifestyles. One should choose a robust rhythm, that is least

affected by masking factors, as an ideal marker for studying circadian clock function in

humans. Alternatively demasking could be carried out by employing appropriate techniques

(Rao & Sharma 2002; Roenneberg et al. 2005).

Interindividual differences in various physiological processes, namely sleep – wake,

melatonin and body temperature could be attributed to several factors such as age, gender

and morningness – eveningness etc. (Vink et al. 2001).

In humans, interindividual differences have been reported in the occurrence of preferential

activity, named chronotype (Achari & Pati, 2007). It is always self-reported by the subjects

(Kudielka et al. 2006). Humans are divided into three main chronotypes: morning type,

evening type and intermediate type. ‘‘Early birds’’ or morning type (MT) personalities show

maximum vigilance during morning hours and ‘‘night owls’’ or evening type (ET)

personalities show the same at evening hours. Those who are neither MT nor ET are called

intermediate type (IT) personalities (Vink et al. 2001). The exact reason for this variation is

not yet known. For the time being, these differences should always be considered during

chronobiological analysis.

An oscillator can be responsive to stimuli at some times but not always. Moreover, the peak

is the time point where the masking effects are profound and it may not change significantly

with zeitgeber time (Roenneberg et al. 2005). It is defined as ‘‘the measure of timing; the lag from

a defined reference time point (acrophase reference) of the crest time in the function appropriately

approximating a rhythm; the phase angle of the crest, in relation to the specified reference time point’’

(Halberg et al. 2003).

Phase reference point is defined as a time point selected by the investigator as a reference for

the estimation of the peak timing of a rhythm. Conventionally, local mid-night is taken as the

phase reference point for circadian rhythm as it is the beginning of the day (Halberg et al.

1977).

There is no hard and fast rule for obeying this convention. For example if one can go to

Norway situated at the northern hemisphere at about 58 – 708 latitude, then one can find

summers with 18.5 – 24.0 hours of MLD (maximum duration of light) and winters with 5.5 –

0.0 hours of MLD. There is tremendous seasonal variation. Such variations are also true to a

higher or lesser extent for each and every place of this earth. In respect of such variability, can

we accept local mid-night (00:00 hour) as the phase reference point? We should look for some

110 R. Sultana et al.


better options, since selection of an appropriate phase reference point is crucial in

chronobiological studies (Halberg et al. 2003).

The object of this study is to investigate rest – activity rhythm and to ascertain the best phase

reference point for the peak in this rhythm in randomly chosen apparently healthy young

university students.

Materials and methods

Subjects

Sixteen subjects (graduate or postgraduate students), consisting of nine males and seven

females, were randomly chosen for this study. Among the female subjects, four were morning

type and three were intermediate type (57% MT and 43% IT). Among males, four were

morning type and five were intermediate type (44% MT and 56% IT). All of them were

young, aged between 22 and 26 years. They were presumably healthy as they did not self-

report any major health or sleep disorders.

Determination of chronotype

Horne and Ostberg’s (1976) questionnaire was employed to ascertain each subject’s

chronotype. Based on the scores, subjects can be classified into three groups, namely

morning type (MT: score 32 – 23); intermediate type (IT: score 22 – 16) and evening type

(ET: score 22 – 16), respectively.

Actigraphy

Actiwatch (AW64, Mini Mitter Co. Inc., USA), a non-invasive device, was used to monitor

rest – activity rhythm in each subject. It is an activity monitor for long-term monitoring of

gross motor activity in human subjects. It has an accelerometer that is capable of sensing any

motion with a minimum resultant force of 0.01 g. It has an on-board memory (64 KB) with

maximum sample rate of 32 Hz.

Each subject wore an actiwatch in his/her non-dominant hand for 4 – 7 days continuously.

The data were collected at one-minute epoch length. Subjects were advised not to remove the

AW64 even while they were sleeping in the night. They were also instructed to keep a sleep

log and record timings of breakfast, lunch, and dinner. Soon after the end of the study, data

from the on-board memory of the AW64 were transferred to a PC by using an Actiwatch

reader via an RS-232 serial port.

Evaluation of sleep parameters

The following sleep parameters were also studied in all subjects by using Actiware Sleep

Software (Mini Mitter Co. Inc., USA).

Time in Bed (TIB). The amount of time spent in bed. It is the difference between the get up

time and bedtime.

Assumed Sleep (AS). It is the difference in time between the Sleep End and Sleep Start times.

This parameter was calculated automatically using values derived from the sleep scoring

algorithm of the Actiware Sleep software.

Dichotomy in human population: variability in peak spread 111


Actual Sleep Time (AST). Represents the amount of time, between Sleep Start and Sleep End,

scored as sleep according to the Actiware-Sleep algorithm. It is determined by the summation

of the number of epochs that do not exceed the sensitivity threshold and multiplying that

value by the epoch length in minutes. It is expressed in hours and minutes.

Actual Wake Time (AWT). Represents the amount of time, between Sleep Start and Sleep

End, which is scored as wake according to the Actiware-Sleep software. It is determined by

the summation of the number of epochs that exceed the sensitivity threshold and multiplying

that value by the epoch length in minutes. It is expressed in hours and minutes.

Sleep Efficiency (SE). An index of the amount of time in bed actually spent sleeping. It is

determined by the division of the actual sleep time by time in bed and multiplying the result

by 100.

Sleep Latency (SL). The period of time required for sleep onset after going to bed. Sleep

latency is the period between Bed Time and Sleep Start.

Fragmentation Index (FI). It is an index of restlessness. It indicates the extent to which sleep is

disturbed in an individual.

In addition, all subjects recorded their bed time, wake-up time, and meal timings in a diary

on a daily basis. The time to bed was verified from the data obtained from the Actiware-Sleep

software.

Data analyses

Data were stored in the form of a database in an Excel spreadsheet. Actiware sleep software

(version 3.0), SPSS software (version 10.0) and Microsoft Excel Analysis Toolpak were used

to analyze data.

The time series of each subject was shortened by harvesting data at 10-minute epoch length

from the original time series. Log-transformed data were subjected to Cosinor rhythmometry

(Nelson et al. 1979). Three important rhythm parameters, viz., mesor (24-average of the

rhythmic function), amplitude (half the difference between the minimum and the maximum

of the rhythmic function), and peak (time of the highest value of the rhythmic function) were

determined. An F-test was used to test the zero-amplitude hypothesis. Amplitudes and peaks

were expressed with 95% confidence limits. In addition, the peak of rest – activity rhythm in

subjects was computed daywise by taking start-sleep, mid-sleep and end-sleep timings as the

phase reference. The peak spread in each subject was determined.

Results

Table I presents biometric characteristics, such as age, height, weight, BSA and chronotype

of all 16 subjects.

Rest – activity pattern

Figure 1 shows an illustrative example of actograms for rest – activity rhythm in two randomly

selected subjects from each gender. The subjects showed day-to-day consistency in rest –

activity patterns. They showed more activity during daytime and less activity during night-

time.



Table I. Biometric characteristics of day active subjects.

S. No. Subject code Gender Age Weight (kg) Height (cm) BSA (m2) Chronotype

1 ST M 22 54 163.0 1.66 MT

2 RSIII F 24 40 150 1.3 MT

3 NV F 22 47 153 1.41 IT

4 CN F 23 35 145 1.21 MT

5 BT M 23 50 160 1.50 IT

6 AKII F 25 50 160 1.50 MT

7 AR M 26 55 167 1.61 IT

8 AMI F 21 40 150 1.30 MT

9 RSI M 25 49 163 1.50 IT

10 RSII M 23 64 170 1.74 IT

11 RSIV F 23 65 160 1.67 IT

12 AKI M 19 63 161.5 1.75 IT

13 PT M 23 60 165 1.64 MT

14 GB M 23 40 157 1.34 MT

15 DP F 23 46 153 1.4 IT

16 AMII M 22 64 163.5 1.81 MT

Figure 1. Actograms of two representative examples of a male and a female subject.



Rhythm detection and period

A statistically significant (p5 0.001) circadian rhythm was obtained for rest – activity in

all subjects (Figure 2). All subjects exhibited prominent period (t) of 24 h in rest – activity

(Table II).

Mesor, amplitude and peak

Inter-individual variation in circadian mesors (range: 2.24+ 0.03 to 3.0+ 0.03) and

amplitudes (range: 0.53 [0.42, 0.64] to 1.11 [0.76, 1.0] was noticed) (Table II).

The average peak for the rest – activity rhythm was located at 15.3 h, with a range between

13.2 (12.7, 13.6) and 18.1 (17.3, 18.9) h.

Sleep parameters

Table III shows averages for various sleep parameters, such as TIB, AS, AST, AWT, SE, SL,

and FI.

Peak spread

A statistically significant difference was obtained when averages of peak spreads between mid-

night and end-sleep (p5 0.05) as well as between mid-night and mid-sleep (p5 0.05) were

compared (Figure 3). The peak spreads varied dramatically as a function of changing

reference points in most of the subjects (Figure 4).

On the basis of the results obtained in the present study, subjects were divided into two

groups, namely MSG (mid-sleep group), i.e., this group showed the least peak spread when

mid-sleep was taken as the phase reference point, and NMSG (non-mid-sleep group), i.e., the

Figure 2. Fitted cosine curves for all 16 subjects, obtained by using the cosine function: Yti¼MþA cos (otiþØ).



subjects belonging to this group exhibited the least peak spread when either end-sleep or start-

sleep timings were taken as the phase reference points. Nine out of 16 subjects were grouped

into MSG (4 MT and 5 NT subjects) and the remaining seven subjects were included in

NMSG (4 MT and 3 NT subjects).

A statistically significant difference was obtained when the averages for amplitude (p5 0.01)

and peak (p5 0.05) of MSG and NMSG were compared (Figures 5 and 6).

A negative relationship (r¼70.756) between amplitude and fragmentation index was noticed

in NMSG (Figure 7 and Table IV).

Different models were fitted in order to ascertain the relative role of different combinations

of predictors on FI. The Model-2 (Table V) was found to be the best and showed the highest

significance level (F¼ 22.97, p¼ 0.006).

Table VI displays the regression coefficients obtained in Model-2 showing significant

relationships of FI with the amplitude as well as the mesor.

Discussion

All subjects, irrespective of age and chronotype, exhibited statistically significant rhythm in

rest – activity. It appears that rest – activity is a robust parameter for the investigation of

Table II. Cosinor summary: characteristics of circadian rhythm in rest – activity of 16 healthy young subjects.

S. No.

Subject

code

Data

point

Rhythm

detection

Rhythm-

adjusted mean,

M+SE

Amplitude,

A (95% CL)

Peak, Ø in h

(95% CL) Period (h)

NMSG

1 ST 935 50.001 2.92+ 0.02 0.62 (0.55, 0.70) 16.6 (16.1, 17.0) 24

2 RSIII 723 50.001 2.76+ 0.03 0.72 (0.60, 0.84) 15.5 (14.8, 16.1) 24

3 NV 576 50.001 2.94+ 0.03 1.03 (0.92, 1.14) 15.3 (14.8, 15.7) 24

4 CN 707 50.001 2.47+ 0.04 0.76 (0.61, 0.91) 18.1 (17.3, 18.9) 24

5 BT 580 50.001 3.00+ 0.03 0.75 (0.61, 0.88) 14.6 (14.0, 15.3) 24

6 AKII 551 50.001 2.71+ 0.03 1.11 (0.76, 1.00) 13.2 (12.7, 13.6) 24

7 AR 587 50.001 3.00+ 0.03 1.10 (0.96, 1.21) 15.9 (15.5, 16.4) 24

MSG

8 AMI 579 50.001 2.24+ 0.03 0.89 (0.76, 1.00) 15.8 (15.2, 16.3) 24

9 RSI 908 50.001 2.84+ 0.02 0.87 (0.77, 0.97) 15.7 (15.2, 16.1) 24

10 RSII 587 50.001 2.80+ 0.03 0.73 (0.62, 0.84) 14.7 (14.1, 15.3) 24

11 RSIV 582 50.001 2.81+ 0.03 0.83 (0.71, 0.96) 14.8 (14.2, 15.4) 24

12 AKI 939 50.001 2.92+ 0.02 0.73 (0.63, 0.83) 14.7 (14.2, 15.3) 24

13 PT 589 50.001 2.88+ 0.04 0.84 (0.70, 0.98) 13.3 (12.7, 14.0) 24

14 GB 578 50.001 2.67+ 0.03 1.03 (0.90, 1.16) 13.9 (13.4, 14.4) 24

15 DP 578 50.001 2.85+ 0.03 0.69 (0.56, 0.83) 15.2 (14.4, 15.9) 24

16 AMII 577 50.001 3.00+ 0.03 0.53 (0.42, 0.64) 15.3 (14.5, 16.1) 24

Group summary

17 All females 4296 50.001 2.68+ 0.09 0.70 (0.66, 0.74) 15.9 (15.5, 16.2)

18 All males 6280 50.001 2.89+ 0.03 0.70 (0.65, 0.75) 14.7 (14.0, 15.5)

19 MSG 5917 50.001 2.74+ 0.07 0.85 (0.80, 0.85) 14.7 (14.3, 15.0)

20 NMSG 4659 50.001 2.83+ 0.07 0.87 (0.80, 0.94) 15.6 (15.0, 16.2)

21 All 10576 50.001 2.80+ 0.05 0.83 (0.78, 0.87) 15.3 (15.0, 15.6)



Tab

leII

I.S

leep

sum

mar

y:d

etai

lso

fsl

eep

par

amet

ers

(mea

n+

SE

)o

f1

6h

ealt

hy

you

ng

sub

ject

s.

S.

No

.S

CS

E(%

)S

L(m

in)

TIB

(h)

AS

(h)

AS

T(h

)A

WT

(h)

FI

NM

SG

1S

T6

4.9

7+

3.7

10

0.4

5+

00.1

30

7.7

1+

00

.48

06

.77+

00

.45

05

.00+

0.3

80

1.7

6+

0.2

05

4.0

0+

05

.37

2R

SII

I7

7.0

4+

1.4

51

7.2

5+

05.0

60

7.2

5+

00

.44

06

.51+

00

.20

05

.84+

0.1

80

1.1

7+

0.1

83

7.0

0+

04

.76

3N

V7

4.2

7+

2.0

03

0.0

5+

12.7

70

8.1

9+

00

.12

07

.21+

00

.33

06

.09+

0.2

40

1.3

7+

0.2

23

7.3

2+

03

.04

4C

N7

8.2

6+

1.3

80

9.2

7+

06.9

80

7.7

2+

00

.13

06

.84+

00

.26

05

.59+

0.3

20

1.4

6+

0.2

63

0.5

4+

02

.06

5B

T8

4.0

2+

1.4

01

4.0

0+

11.2

70

7.2

8+

00

.62

06

.90+

00

.58

06

.08+

0.5

00

0.8

1+

0.2

93

5.0

3+

01

.65

6A

KII

82

.65+

1.7

00

3.0

5+

02.8

70

7.7

9+

00

.24

07

.64+

00

.28

06

.43+

0.0

80

1.4

6+

0.3

43

0.3

5+

04

.26

7A

R8

1.6

0+

1.3

40

7.0

0+

04.4

50

7.2

5+

00

.12

07

.08+

00

.08

05

.95+

0.0

30

1.1

3+

0.0

74

0.0

5+

02

.51

MS

G

8A

MI

85

.57+

1.0

90

0.2

4+

00.0

50

7.9

7+

00

.11

07

.42+

00

.19

06

.82+

0.1

80

0.6

0+

0.0

32

0.5

2+

03

.24

9R

SI

72

.44+

5.6

10

0.4

3+

00.1

60

8.0

7+

00

.75

07

.60+

01

.02

05

.95+

0.9

20

1.0

5+

0.1

72

9.5

0+

04

.73

10

RS

II7

4.0

9+

1.3

60

7.7

5+

04.0

10

7.1

2+

00

.89

06

.64+

00

.16

05

.34+

0.1

60

1.2

3+

0.1

73

3.0

5+

01

.70

11

RS

IV8

0.8

2+

1.3

71

2.2

5+

10.3

50

8.5

2+

00

.19

07

.64+

00

.15

06

.89+

0.2

50

1.1

1+

0.1

43

3.1

5+

02

.79

12

AK

I7

5.2

5+

2.0

90

0.3

4+

00.0

90

7.0

0+

00

.46

06

.53+

00

.59

05

.26+

0.4

20

1.1

7+

0.1

93

2.5

4+

04

.75

13

PT

71

.48+

8.4

90

1.0

5+

01.0

50

7.0

8+

00

.14

07

.70+

00

.12

05

.65+

0.5

80

2.1

6+

0.6

74

9.4

2+

14

.80

14

GB

84

.22+

2.6

01

1.2

5+

11.2

50

7.1

0+

00

.42

06

.74+

00

.46

05

.96+

0.4

00

0.7

7+

0.0

82

5.0

5+

01

.77

15

DP

68

.75+

3.3

52

8.0

5+

18.5

90

8.7

5+

00

.59

08

.26+

00

.57

06

.03+

0.5

40

1.9

8+

0.0

55

2.5

2+

04

.48

16

AM

II5

4.3

3+

3.6

60

1.9

8+

00.3

70

8.0

6+

00

.57

05

.67+

00

.80

04

.43+

0.5

90

1.2

4+

0.2

34

0.8

8+

03

.17

Gro

up

su

mm

ary

17

All

Fem

ale

78

.19+

2.1

11

4.3

1+

02.8

80

8.0

3+

00

.13

07

.36+

00

.14

06

.24+

0.1

20

1.2

3+

0.1

53

4.4

8+

02

.43

18

All

Mal

e7

3.6

0+

3.2

00

4.9

2+

01.7

50

7.4

1+

00

.14

06

.85+

00

.20

05

.50+

0.1

80

1.2

6+

0.1

53

7.7

2+

03

.12

19

MS

G7

4.1

0+

3.1

40

7.0

4+

03.0

80

7.7

4+

00

.22

07

.13+

00

.26

05

.18+

0.2

60

1.2

6+

0.1

73

5.1

8+

03

.54

20

NM

SG

77

.54+

2.4

61

1.5

8+

03.7

90

7.6

0+

00

.13

06

.99+

00

.14

05

.85+

0.1

70

1.3

1+

0.1

13

7.7

5+

03

.02

21

All

75

.61+

2.0

20

9.0

2+

02.3

50

7.6

8+

00

.13

07

.07+

00

.15

05

.83+

0.1

50

1.2

4+

0.1

23

6.3

1+

03

.00



circadian rhythm in humans, especially since a high level of day-to-day consistency in the

pattern of variability in the rest – activity rhythm was noticed in each subject, notwithstanding

the inter-individual differences and free-living conditions under which each subject was

studied. Several authors have recommended the technique of Actigraphy to be extremely

useful and appropriate for the study of circadian rhythms and sleep disorders in both healthy

subjects and in patients suffering from various diseases (Thorpy et al. 1995; Sadeh & Acebo

2002; Pati et al. 2006.

The peak-spread in respect of end-sleep or mid-sleep timing is noticeably shorter than that of

mid-night (conventional phase reference point).

An inverse relationship was noticed between the peak spread and consistency of the rhythm,

i.e., the lower the peak-spread higher the consistency of the rhythm. Thus, end-sleep and

mid-sleep timings can serve as better anchor points for phase resetting. By using such phase

reference points, one may obtain a sharp difference between the peak spread of healthy people

and people with rhythm disorder.

At individual levels the peaks appeared to be more consistent when phase reference points

other than conventional phase reference points were taken into consideration. It was found

Figure 3. Peak spread of all 16 subjects with respect to four different reference points, namely local mid-night, start-

sleep, mid-sleep and end-sleep.

Figure 4. Inter-individual variations in peak spread (average of day-to-day peaks) of all 16 subjects as a function of

reference points.



Figure 5. Comparison of amplitudes between MSG and NMSG.

Figure 6. Comparison of peaks between MSG and NMSG.

Figure 7. Negative relationship between fragmentation index and amplitude in NMSG. The regression line is eye

fitted.



that nine out of 16 subjects showed the lowest peak spread when mid-sleep was taken as the

phase reference point, while the rest showed the same for either start-sleep or end-sleep

timings. We could divide them into two groups, such as MSG and NMSG. The subjects

Table IV. Pearson correlation between mesor, amplitude and peak in NMSG (n¼ 7).

Fragmentation index Major Amplitude Peak

Pearson correlation

Fragmentation index 1.000 0.569 70.756 0.007

Major 0.569 1.000 0.029 70.710

Amplitude 70.756 0.029 1.000 70.434

Peak 0.007 70.710 70.434 1.000

Sig. (one-tailed)

Fragmentation index – 0.091 0.025 0.494

Major 0.091 – 0.476 0.037

Amplitude 0.25 0.476 – 0.166

Peak 0.494 0.037 0.166 –

Table V. Seven different models showing ANOVA summary.

Sum square df Mean F p-value

Model-1

Regression 392.87 3 130.95 17.33 0.02

Residual 22.66 3 7.55

Total 415.54 6

Model-2

Regression 382.255 2 191.128 22.97 0.006

Residual 33.287 4 8.322

Total 415.542 6

Model-3

Regression 290.014 2 145.007 4.621 0.091

Residual 125.528 4 31.382

Total 415.542 6

Model-4

Regression 275.422 2 137.711 3.931 0.114

Residual 140.120 4 35.030

Total 415.542 6

Model-5

Regression 134.327 1 134.327 2.388 0.183

Residual 281.215 5 56.243

Total 415.542 6

Model-4

Regression 237.349 1 237.349 6.66 0.049

Residual 178.192 5 35.638

Total 415.542 6

Model-7

Regression 1.955 E-02 1 1.955 E-02 0.00 0.988

Residual 415.522 5 83.104

Total 415.542 6

Dependable variable: fragmentation index; Combinations of independent variable – Model-1: mesor, amplitude and

peak; Model-2: mesor and amplitude; Model-3: amplitude and peak; Model-4: mesor and peak; Model-5: mesor;

Model-6: amplitude; Model-7: peak.



belonging to MSG and NMSG groups were found to be independent of chronotype and

gender. The latter factor produced the least effect on the peak of the rest – activity rhythm.

A statistically significantly higher amplitude and later peak were observed in NMSG

compared to the MSG group. Furthermore, an inverse relationship was found between the

fragmentation index and amplitude in NMSG, i.e., the higher the amplitude the lower the

fragmentation index. Here, the amplitude appeared to be a significant predictor that can be

used to interpret the fragmentation index—a measure of restlessness during sleep. Most

probably, subjects belonging to NMSG exhibited a better profile consisting of large amplitude

and small fragmentation index.

We fitted different models for analyzing the relationship between the FI (dependent

variable) and rhythm parameters (independent variables). In each model, the combination of

independent variables was changed to get the best model that would show the most significant

value. Model-2 was found to be the best that included amplitude and mesor as independent

variables.

Present human society, more especially the urban population, is partially cut off from the

natural and the strongest zeitgeber, i.e., the solar clock. Use of synthetic lights in the houses

and least exposure to sunlight may affect the resetting mechanism. Earlier it was reported that

broader and later distribution of chronotypes in modern society is attributed to the absence of

a strong zeitgeber leading to a rearrangement of circadian phase (Roenneberg et al. 2003).

Further nonphotic cues may also play a role in the mechanism of phase resetting (Honma

et al. 2003; Mistlberger & Skene 2005; Caldelas et al. 2005).

Our findings suggest that there could be a phenotypical variation among the human

population in respect of phase resetting properties with a special reference to internal phase

reference points. In this case it is the phase of the sleep period. This statement needs further

support from studies based on large samples. The intricate mechanism of entrainment in free-

living human subjects needs to be explored in more depth. Furthermore, we conclude that the

amplitude could be a significant predictor of fragmentation index in humans.

Acknowledgements

We are thankful to the Head, School of Life Sciences, Pt. Ravishankar Shukla University,

Raipur. This study was supported by the University Grants Commission, New Delhi, and the

Department of Science & Technology, New Delhi, through their DRS-SAP and FIST

program, respectively. We are thankful to the subjects who participated in this study.

References

Achari KV, Pati AK. 2007. Morningness-eveningness preference in Indian school students as function of gender, age

and habitat. Biol Rhythm Res 38:1 – 8.

Table VI. Correlation coefficient for Model-2.

Unstandardized

coefficients

Standardized

coefficients 95% confidence interval for B

Model-2 B SE Beta t p-value Lower bound Upper bound

Constant 715.93 17.64 70.93 0.42 764.906 33.04

Mesor 25.55 6.12 0.59 4.17 0.01 8.55 42.55

Amplitude 77.536 1.38 70.77 75.46 0.005 711.37 73.70



Aschoff J. 1960. Exogenous and endogenous components in circadian rhythms. In: Cold Spring Harbor Symp Quant

Biol 25:11 – 28.

Aschoff J. 1981. Freerunning and entrained circadian rhythms. In: Aschoff J, editor. Handbook of Behavioral

Neurobiology, Volume 4, Biological Rhythms. New York: Plenum Press. pp 81 – 93.

Caldelas I, Chimal-Monroy J, Martinez-Gomez M, Hudson R. 2005. Non-photic circadian entrainment in mammals:

A brief review and proposal for study during development. Biol Rhythm Res 36:23 – 37.

Carl HJ, Jeffery AE, Russell F. 2003. Entrainment of circadian programs. Chronobiol Int 20:741 – 774.

Halberg F, Carandente F, Cornelissen G, Katinas GS. 1977. Glossary of chronobiology. Chronobiologia 4:1 – 189.

Halberg F, Cornelissen G, Bingham C, Witte H, Ribery U, Hesse W, Petsche H, Engebretson M, Geissler HG,

Weiss S, Klimesch W, Rappelsberger P, Ketinas G, Schwartzkopff O. 2003. Chronomics: imaging in time by

phase synchronization reveals wide spectral-biospheric resonances. Neuroendocrinol Lett 24:355 – 380.

Honma K, Hashimoto S, Nakao M, Honma S. 2003. Period and phase adjustments of human circadian rhythms in

the real world. J Biol Rhythms 18:261 – 270.

Horne J, Ostberg O. 1976. A self assessment questionnaire to determine morningness-eveningness in human

circadian rhythms. Int J Chronobiol 4:97 – 110.

Kudielka BM, Federenko IS, Hellhammer DH, Wust S. 2006. Morningness and eveningness: The free cortisol rise

after awakening in ‘‘early birds’’ and ‘‘night owls’’. Biol Physiol 72:141 – 146.

Mistlberger RE, Skene DJ. 2005. Nonphotic entrainment in humans? J Biol Rhythms 22:339 – 352.

Nelson W, Tong YL, Lee JK, Halberg F. 1979. Methods of cosinor rhythmometry. Chronobiologia 6:305 – 323.

Pati AK, Parganiha A, Kar A, Soni R, Roy S, Choudhary V. 2006. Implications of the study of rest-activity circadian

rhythm in head and neck cancer patients. Biol Rhythm Res 37:497 – 505.

Piccione G, Caola G. 2002. Biological rhythm in livestock. J Vet Sci 3:145 – 157.

Rao AV, Sharma VK. 2002. A simple approach for the computation of multiple periodicities in biological time series.

Biol Rhythm Res 33:487 – 502.

Roenneberg T, Dragovic Z, Merrow M. 2005. Demasking biological oscillators: Properties and principles of

entrainment exemplified by the Neurospora circadian clock. PNAS 102:7742 – 7747.

Roenneberg T, Wirz-Justice A, Merrow M. 2003. Life between clocks: Daily temporal patterns of human

chronotypes. J Biol Rhythms 18:80 – 90.

Sadeh A, Acebo C. 2002. The role of actigraphy in sleep medicine. Sleep Med Rev 6:113 – 124.

Thorpy M, Chesson A, Derderian S, Kader G, Millman R, Potolicchio Jr S, Rosen G, Strollo Jr PJ. 1995. Practice

parameters for the use of actigraphy in the clinical assessment of sleep disorders. Sleep 18:285 – 287.

Vink JM, Groot AS, Kerkhof GA, Boomsma DI. 2001. Genetic analysis of morningness and eveningness. Chronobiol

Int 18:809 – 822.



Editor-in-Chief: W. J. Rietveld, Foundation for Chronobiology, Sbn Doormanlaan 57 , 2243 AK Wassenaar, The Netherlands. E-mail: [email protected] Co-Editor: J. M. Waterhouse, Liverpool John Moores University, Liverpool, UK

Dr. Arti Parganiha School of Life Sciences Pt. Ravishankar Shukla University Raipur, 492010 India

Tel.: 31-(0)70-5117998 Wassenaar, 2011/07/07 Ref.: BRR27WJR11 Your ref.: Dear Dr. Arti Parganiha, Your manuscript "Circadian variability and nocturnal dipping pattern in blood pressure in young normotensive subjects" has been reviewed, and I am happy to inform you that we are able to publish it in its present form in one of the forthcoming issues of Biological Rhythm Research. In due course you will receive a proof. Yours sincerely, W.J. Rietveld Editor-in-Chief

Published by TAYLOR & FRANCIS LTD

4 Park Square, Milton Park,

Abingdon, Oxfordshire OX14 4RN, UK

Biological Rhythm Research

1

Circadian variability and nocturnal dipping pattern in blood

pressure in young normotensive subjects

Nishtha Vaidya, Atanu Kumar Pati and Arti Parganiha*

School of Life Sciences, Pt. Ravishankar Shukla University, Raipur, 492010, India

Phone: +91-771-3292885; Fax: +91-771-2262583

Email: [email protected]; [email protected]; [email protected] *Corresponding author. Email: [email protected]

Total words: 6,051





2

Circadian variability and nocturnal dipping pattern in blood

pressure in young normotensive subjects

Nishtha Vaidya, Atanu Kumar Pati & Arti Parganiha

*

School of Life Sciences, Pt. Ravishankar Shukla University, Raipur, 492010, India *Corresponding author. Email: [email protected]

Blood pressure variability (BPV) is one of the recognized risk factors for different types

of cardiovascular diseases (CVDs). Several physiological, behavioral and ethnic factors

are known to modulate BP. Such studies on the population of South-East India are

altogether absent. It is worthwhile, therefore, to examine the circadian variability in

systolic blood pressure (SBP), diastolic blood pressure (DBP) and heart rate (HR) and

nocturnal dipping pattern in BP in apparently healthy human subjects as function of

gender. 60 females and 40 males voluntarily participated in the study. All subjects wore

an Ambulatory Blood Pressure Monitor (ABPM, TM 2430) for 2-4 consecutive days.

SBP, DBP, and HR was measured every 15- and 30-minute between 07:00-22:00 and

22:00-07:00, respectively. In addition, double product (DP) and mean arterial pressure

(MAP) were also computed. Data were analyzed using A&D, Cosinor and Spectre

software. Prevalence of extreme dipper (ED), dipper (D), and nondipper (ND) was 13%,

63% and 24%, respectively. A statistically significant circadian rhythm was validated in

all studied variables, irrespective of gender and dipping pattern in BP. However, the

rhythm detection ratio was low among nondippers. Chi-square test revealed a statistically

significant association of the frequency of prominent periods of SBP & MAP with

nocturnal dipping categories. Further, significant differences for the circadian Mesors of

SBP, DBP, HR, DP, & MAP and acrophase of DBP & MAP were noticed between males

and females. Dipping pattern produced a significant effect on the rhythm characteristics

of all studied variables. On the basis of our findings we can conclude that variability in

BP may be associated with factor gender in some extent, whereas nocturnal dipping in

BP is independent of gender. Interestingly in the present study about 24% subjects are

nondippers that may be an indication of higher risk of cardiovascular diseases among

individuals belonging to younger generation of this region. However, further extensive

study is desirable to strengthen the above conjecture.

Keywords: ABPM; blood pressure; heart rate; circadian rhythm; nocturnal dipping

pattern; double product; mean arterial pressure

1. Introduction

In free-living conditions, humans display circadian rhythms in several behavioral and

physiological variables, such as rest-activity, body temperature, physical activities, cognitive

functions, hormonal secretions and enzyme activities (Redfern and Lemmer 1997). The rhythms

in these variables exhibit 24-h period as long as they remain synchronized with the external day-

night cycle. However, under time cue less environment, these rhythms free run and show periods

very close to 24 h in both directions. There is lack of unanimity regarding endogenous nature of

3

blood pressure (BP) rhythm. Nevertheless, in humans, BP dips considerably during sleep and

remains elevated during the waking period with a characteristic surge in early morning hours

(Redon 2004).

In humans, apart from circadian rhythms other multi-frequency rhythms, namely circannual,

circaseptan and circasemiseptan have been described for BP and HR (Rawson et al. 2000;

Halhuber et al. 2002). The pattern of blood pressure variability alters in diseased persons.

Rawson et al. (2000) studied circadian and circaseptan rhythms in blood pressure and heart rate

in a depressed subject and speculated that if pineal gland is involved in the mechanism

underlying depressive disorder, then the circaseptan component of this variability needs to be

examined extensively. Nevertheless, the BP variability may play a major role in depression in

human subjects. The circadian amplitude of negative mood was positively related with averages

of systolic and diastolic blood pressure (Mitsutake et al. 2002). The onset of symptoms of most

of the CVD appears to be time dependent. Sudden cardiac death, acute myocardial infarction,

myocardial ischemia, angina pectoris, ventricular arrhythmias, stroke, cerebrovascular events

occurs during early morning hours when blood pressure rises suddenly (Redon 2004).

Moreover, variability in blood pressure has been evaluated in healthy human subjects with

reference to age, gender, and ethnicity (Harshfield et al. 1989; Suzuki et al. 1993; Driziene et al.

2008). Suzuki et al. (1993) reported increased Mesor, decreased amplitude and advancement in

acrophase of SBP and DBP with advancing age. It has been proposed that the circadian rhythm

of blood pressure should always be examined with reference to sex-, age- and race-matched

reference values (Harshfield et al. 1989; Suzuki et al. 1993; Driziene et al. 2008).

There are number of studies that link heart rate variability (HRV) with number of

pathophysiologic conditions, including increased risk of mortality (Kleiger et al. 1987; Bigger el.

1992; Singer and Ori 1995; Task Force of the European Society of Cardiology and the North

American Society of Pacing and Electrophysiology 1996). The two important factors, namely

age and gender modulate the HRV of healthy subjects (Umetani et al. 1998). HRV was found to

be decreased with the advancement of age (O’Brien et al. 1986; Shannon et al. 1987; Ori et al.

1992; Umetani et al. 1998). However, the association between HRV and gender is considerably

debatable; while HRV has been reported to be higher in females than males (Ryan et al. 1994;

Gowan et al. 1995; Huikuri et al. 1996), the opposite has also been described (Rich et al. 1988;

Van Hoogenhuyze et al. 1991; Bigger et al. 1995).

Nocturnal dipping in blood pressure is a normal phenomenon. However, in many subjects

this fall in blood pressure during sleep does not always occur (Larochelle 2002). On the basis of

the nocturnal dipping in blood pressure, human subjects can be classified into extreme dippers,

4

dippers, non-dippers, and risers. In dippers, blood pressure registers a decline of about 10-20%

during sleep hours; while in non-dippers it is only about 0-10% (Thomas et al. 2006). The

extreme dippers exhibit more than 20% dipping and the risers register increase in BP during the

period of sleep. The phenomenon of nocturnal dipping in blood pressure has prognostic

significance for stroke, cardiovascular mortality, and progression to microalbuminuria in type 1

or type 2 diabetes (Pistrosch et al. 2007). Parati et al. (1992) documented that lack of nocturnal

dipping in blood pressure among normotensive adult may make them vulnerable to

cardiovascular diseases.

Circadian variation or nocturnal dipping in blood pressure cannot be predicted on the basis

of single spot checking. Therefore, recently clinicians rely on ambulatory blood pressure

monitoring to improve the diagnosis and treatment of hypertension and cardiovascular diseases.

To the best of our knowledge, studies on rhythms in human blood pressure and heart rate using

ABPM are meager in India. In the present study, therefore, we examined circadian variability in

blood pressure (BP) and heart rate (HR), using ABPM in a population of apparently healthy

human subjects belonging to South-Eastern India. In addition, we evaluated the effect of the

factor gender on variability and nocturnal dipping pattern in blood pressure.

2. Materials and Methods

2.1. Subjects

One hundred randomly selected normotensive human subjects, consisting of sixty females (age

range: 18 – 35 y, median: 24.5 y) and forty males (age range: 21 – 35 y, median: 28 y),

participated in the present study. Data were collected from the Chhattisgarh region (Latitude: 21°

30' N; Longitude: 82° 0' E). The subjects were day active and did not report any obvious clinical

complications. Various biographical information, such as age, gender, height (cm) and weight

(kg), smoking and alcohol habits, range of sleep (22:00 to 02:00) and awake timings (05:00 to

09:00) of each subject were recorded. All subjects gave a written informed consent. The study

obtained approval of the Institutional Ethics Committee on Human Research of the Pt.

Ravishankar Shukla University, Raipur, India.

2.2. Assessment of characteristics of circadian rhythms in blood pressure and heart rate

Circadian variability in blood pressure (BP) (systolic blood pressure, SBP & diastolic blood

pressure, DBP) and heart rate (HR) were monitored non-invasively using ambulatory blood

pressure monitor (ABPM, TM 2430; A&D, Japan) for at least 2-4 consecutive days in each

subject. Mean arterial pressure (MAP) and double product (DP) were derived from the SBP,

5

DBP, and HR. ABPM is a completely automatic device and works on the basis of oscillometric

technique. It is worn on a belt, which is connected to the adult cuff on the upper left arm by a

plastic tube. SBP, DBP, and HR were measured every 15-minute during daytime (between 07:00

and 22:00) and every 30-minute during night time (between 22:00 and 07:00). All subjects were

instructed to keep their arm relatively stationary during the recording. During the period of study

subjects followed their normal activities and maintained sleep log on daily basis.

2.3. Nocturnal dipping in blood pressure

Nocturnal dipping in BP in each subject was computed using method of dipping criteria based on

SBP:

(1 – night time SBP/day time SBP)*100 (Kario et al. 2000)

Subjects were classified into three categories on the basis of nocturnal dipping, such as

extreme dippers (dipping ≥ 20%), dippers (dipping between ≥10% and <20%) and non-dippers

(dipping between ≥ 0% and <10%)

2.4. Double product (DP) and Mean Arterial Pressure (MAP): DP and MAP are the derived

variables and were computed using the formulae given by Drattsev et al. (2006).

2.4.1. Double Product (DP): It is a quick test to measure the cardiac activity of the patients.

DP = (SYS × PUL) / 100

2.4.2. Mean arterial Pressure (MAP): It is used to describe an average blood pressure in an

individual. It is derived from the systolic and diastolic measurement values.

MAP = DIA + ((SYS – DIA) / 3)

2.5. Statistical analysis:

Data obtained for blood pressure and heart rate were analyzed with the help of specific A&D

software. Cosinor rhythmometry was used for documenting a circadian rhythm in studied

variables (Nelson et al. 1979). A rhythm is characterized by estimating three parameters, such as

Mesor (M, 24-h average of the best fitting cosine function), amplitude (A, half of the difference

between the highest and the lowest value of the best fitting cosine function), and peak or

acrophase (Ø, the timing of the highest value with reference to midnight). The rhythm detection

ratio was computed for each variable. A power spectrum method was used to detect prominent

period ( ) in the time series for each variable (De Prins et al. 1986). This method is suitable for

time series with missing data as well as for data collected with unequal time intervals. Other

conventional statistical analyses were also performed whenever required.

6

3. Results

Of the 100 subjects, 13, 63 and 24 subjects were extreme dippers (ED), dippers (D) and non-

dippers (ND), respectively. In males, 10%, 57.5% and 32.5% were ED, D and ND, whereas

prevalence of ED, D and ND were 15%, 66.67% and 18.33%, respectively in females. Chi

square results revealed that gender and dipping pattern are independent of each other (χ2 = 2.79;

df = 2; p = 0.25).

3.1. Rhythm detection and period:

A statistically significant circadian rhythm, gauged by a null amplitude hypothesis test, was

documented for most of the studied variables, namely SBP, DBP, HR, DP and MAP, irrespective

of gender. The rhythm detection ratio was high in both males and females (Table 1). With

reference to dipping pattern the rhythm detection ratio in all studied variables was higher in

dippers and extreme dippers; however, it declined in non-dippers (Table 1). Further, frequency

of prominent periods (τ = 24 h or τ = 12 h or τ 24 or τ 12 periods) of these variables resulting

from power spectrum analysis is shown in Table 2. Results of the Chi-square test indicate that

significant relationship could not be validated between gender and prominent periods for all

variables. However, significant association was witnessed between nocturnal dipping categories

and prominent periods of SBP (2 = 14.03; df = 4; p < 0.01) and MAP (

2 = 14.01; df = 4; p <

0.01).

3.2. 24-h average (Mesor)

A statistically significant difference was validated for the Mesors of SBP (p<0.001), DBP

(p<0.001) HR (p<0.01) DP (p<0.001) and MAP (p<0.001) between males and females. The

Mesors of SBP, DBP, DP and MAP in males were significantly higher than that of females

(Table 3). However, the Mesor of HR in females was significantly higher than males. Further,

the factor, dipping pattern produced a significant effect on circadian Mesors of SBP (p<0.05),

DBP (p<0.05), DP (p<0.05) and MAP (p<0.001). Mesors of these variables were significantly

higher in ED as compared to D (Table 4).

3.3. Amplitude

Significant differences could not be validated for circadian amplitudes of the studied variables

with respect to gender (Table 3). However, a significant effect of dipping pattern was

documented on amplitudes of all variables (p<0.01; p<0.001) except HR (Table 4). Amplitudes

7

of these variables were significantly higher in extreme dippers than those of the dippers and non-

dippers (Table 4). Further, amplitudes of SBP and MAP were significantly higher in D as

compared to ND. In case of HR significant difference could not be validated among D, ND &

ED.

3.4. Acrophase (Peak)

The average peaks of the SBP, DBP, HR, DP and MAP occurred mostly in the afternoon hours,

irrespective of gender and dipping pattern (Tables 3 and 4). A significant difference was

validated for the acrophase of DBP (p<0.05) and MAP (p<0.01) between males and females.

Acrophase of these two variables occurred later in males than in females (Table 3). Further,

when subjects were categorized on the basis of dipping pattern, the peaks of all variables

occurred later in non-dippers than that of extreme dippers and dippers (Table 4). However,

dipping pattern produced significant effect on peaks of SBP (p<0.01) and MAP (p<0.05) only

(Table 4).

3.5. Nocturnal dipping in blood pressure

Table 5 shows percentage of overall nocturnal dipping in BP in extreme dippers, dippers and

non-dippers as function of gender. Significant difference could not be validated in nocturnal

dipping between males and females.

4. Discussion

Use of ambulatory blood pressure monitor (ABPM) to study variability in blood pressure is

increasingly gaining acceptance in clinics around the world (Thomas et al. 2006). It is being used

as an important tool in the diagnosis and treatment of hypertension. It has been demonstrated that

measurement of ambulatory blood pressure has more prognostic value and is more closely

associated with target organ damage than routine one time measurement of blood pressure in the

clinic (White et al. 1989). In the present study, we examined blood pressure variability (BPV) in

healthy human subjects using ABPM. We estimated the rhythm parameters, such as Mesor,

amplitude and peak of variables, such as SBP, DBP, HR, DP & MAP. BPV is one of the

recognized risk factors for different types of cardiovascular diseases (Parati et al. 2003).

In the present study, it was observed that the most of the subjects exhibited statistically

significant circadian rhythm in SBP, DBP, HR, DP, and MAP, irrespective of gender and

dipping pattern. However, the rhythm detection ratio was lower in non-dippers than that of

dippers and extreme dippers. All subjects participated in this study were apparently healthy as

8

they did not self-report any disease. In earlier studies, circadian rhythm in blood pressure has

been described in many normotensive subjects and patients with primary hypertension (Kohno et

al. 1998; Rawson et al. 2000; Hermida et al. 2001; Halberg et al. 2004). The characteristics of

circadian rhythm of blood pressure parameters altered in diseased persons. O’Brien et al. (1988)

documented disappearance of the circadian BP rhythm in patients with secondary or non-dipper

type hypertension. The desynchronized rhythm in BP is associated with increased risk of

cardiovascular complications (Profant and Dimsdale 1999; Parati et al. 2003). Pistrosch et al.

(2007) reported that cardiovascular risk is more in diabetic patients and fasting hyperglycemia

was associated with abnormal diurnal BP variation. Association of blood pressure and metabolic

syndrome in untreated hypertension has also been reported (Hermida et al. 2009). Furthermore,

in the present study, as described earlier the rhythm detection ratio declined in non-dippers. The

prevalence of non-dippers in this study was 24%. The median age of the subjects was 26.79 year.

This suggests the subjects, who participated in this study were relatively younger. It has been

reported that non-dippers are at higher risk than dippers (Ohkubo et al. 2002). It seems that risk

of cardiovascular complications is on the rise among young individuals of this region. Further

intensive investigations are required to strengthen the above hypothesis.

Result of the present study depicts that the circadian Mesors of SBP, DBP, DP and MAP

were significantly higher in males as compared to females at the group level. However, the

average of 24-h SBP and DBP falls within the normal range in both groups. Suzuki et al. (1993)

also documented gender-linked variability in circadian blood pressure rhythm. They reported

that the 24-h average of SBP increased with advancing age in women, however in case of men,

the Mesor of SBP was higher in older group only. Observation of Otsuka et al. (1990) supported

the above speculation. Further, significant difference could not be validated for the circadian

amplitudes of the studied variables as function of gender. There are some contradicting reports

regarding alteration in amplitude of blood pressure as function of age and gender. Otsuka et al.

(1990) documented an increase in circadian amplitude of SBP with increasing age in women

only. In contrast, Suzuki et al. (1993) reported that amplitude of SBP decreased with advancing

age in men. Furthermore, present study showed that the nocturnal dipping pattern altered the

circadian amplitudes of the studied variables. It is a normal phenomenon that non-dippers have

lower amplitude than dippers and extreme dippers. Perez-Lloret et al. (2004) also reported

decreased amplitude of ultradian and circadian rhythms in BP in nondippers than dippers.

We documented that the peaks of SBP, DBP, HR, DP and MAP occurred during afternoon

hours in all subjects, irrespective of gender and nocturnal dipping pattern. This is in accordance

with the evidence that blood pressure rhythm is associated and synchronized with sleep

9

wakefulness rhythm (Van de Borne et al. 1994; Redon 2004). A delay in circadian acrophase of

most of the variables, observed in this study, tend to be associated with dipping pattern.

Interestingly occurrence of circadian acrophase was delayed in non-dippers as compared to other

two groups. At present it is difficult to explain the above phenomenon.

In the present study, overall nocturnal dipping in blood pressure was within the normal

range in both males and females. The dipping was less in males than their respective group of

females; however, the difference was not significant. Nocturnal dipping in blood pressure is an

important predictive factor of heart failure. The level of blood pressure is decreased considerably

during sleep period and suddenly elevates during early morning hours (transition period between

sleep and wakefulness) resulting in cardiac surge. But in case of non-dipping the blood pressure

remains elevated during sleep period also, which is a higher risk of cardiovascular diseases

(Profant and Dimsdale 1999). It has been documented that non-dipping in nocturnal blood

pressure is an independent predictor of higher risk of target organ damage and increased brain

and cardiac complications among hypertensive individuals (Verdecchia et al. 2001; Routledge et

al. 2007). Therefore, it is suggested that measuring blood pressure with the help of ABPM for at

least 24-h duration in hypertensive patients may provide beneficial out come.

Blood pressure and heart rate are affected by the endogenous and exogenous factors

(Waterhouse et al. 2007). The present results showed that circadian rhythm parameters of HR did

not exhibit any significant difference as function of gender and nocturnal dipping pattern except

for Mesor between males and females. In one of the earlier reports it has been documented that

heart rate variability (HRV) is affected to lesser extent by the factor gender (Jensen-Urstad et al.

1997). However, Umetani et al. (1998) reported that HRV is influenced by the gender, but it is

age dependent. Kohno et al. (1998) demonstrated that the HR and pulse pressure were

significantly higher in the hyperthyroid group as compared to the control group.

Double product is an estimation of myocardial work (heart muscle) and is related to

myocardial oxygen consumption (Hermida et al. 2001). In the present study, about 98% subjects

exhibited significant 24-h rhythm in DP, irrespective of gender and dipping pattern. However, in

non-dippers it reduced to 85%. Hermida et al. (2001) also documented a higher percentage of

circadian components in DP. Further, we noticed that males exhibited an increase in 24-h

average of double product than their female counterparts. Our results are in agreement with the

findings of Hermida et al. (2001). They reported effect of gender on Mesor and amplitude of DP

circadian rhythm. However, they further emphasized that the difference in hourly means of DP

was noticeable only during the sleep time. According to Thom et al. (2006) and Martin et al.

(2008) an increase in DP in males was on account of increased mental stress. Further, the peak of

10

DP occurred in the afternoon hours in all subjects at group level when they were categorized as

function of gender and dipping pattern. Our result corroborates the finding of Hermida et al.

(2001) in that the highest value of double product was documented during the afternoon hours.

Mean arterial pressure (MAP) has clinical and physiological significance in both the

representation of perfusion pressure and its utilization in the calculation of homodynamic

variables. MAP has been established as an independent predictor of ischemic stroke in

hypertensive subjects (Zheng et al. 2008). The normal range of MAP falls between 70 and

110 mmHg. In the present study, a statistical significant difference in the Mesor and acrophase of

MAP between males and females was depicted. Moreover, significant differences were also

validated for all rhythm characteristics of MAP as function of nocturnal dipping patterns. What

could be the possible mechanism behind the gender- and dipping pattern-linked variability in

MAP is difficult to answer at this moment? Further intensive studies should be conducted to

resolve this issue.

On the basis of our findings we can conclude that variability in BP may be associated with

factor gender in some extent, whereas nocturnal dipping in BP is independent of gender.

Interestingly in the present study, about 24% subjects were non-dippers that may be an indication

of higher risk of cardiovascular diseases among individuals belonging to younger generation of

this region.

“According to World Health Report (2002), cardiovascular diseases (CVDs) will be the

largest cause of death and disability by 2020 in India. In 2020 AD, 2.6 million Indians are

predicted to die due to coronary heart disease that constitutes 54.1 % of all CVD deaths. Nearly

half of these deaths are likely to occur in young and middle aged individuals (30-69 years)

(Ahlawat et al. 2002; Kumar et al. 2009)”. Therefore, it is recommended that the study on BP

variability should be extended to a larger human population of India.

5. Acknowledgement

We are thankful to the Head, School of Life Sciences, Pt. Ravishankar Shukla University,

Raipur. We are also thankful to subjects, who participated in this study. Thanks are due to the

UGC and CSIR for providing with the fellowship in the form of JRF and SRF to NV.

http://en.wikipedia.org/wiki/MmHg

11

6. References

Ahlawat SK, Singh MM, Kumar R, Kumari S, Sharma BK. 2002. Time trends in the prevalence

of hypertension and associated risk factors in chandigarh. J Indian Med Assoc. 100: 547-

552, 554-555, 572.

Bigger JT, Fleiss JL, Steinman RC, Rolinitzky LM, Kleiger RE, Rottman JN. 1992. Frequency

domain measures of heart period variability and mortality after myocardial infarction.

Circulation 85:164-171.

Bigger JT, Fleiss JL, Steinman RC, Rolnitzky LM, Schneider WJ, Stein PK. 1995. RR variability

in healthy, middle-aged persons compared with patients with chronic coronary heart

disease or recent acute myocardial infarction. Circulation 91:1936-1943.

De Prins J, Cornelissen G, Malbecq W. 1986. Statistical procedures in chronobiology and

chronopharmacology. Annu Rev Chronopharmacol. 2:27-141.

Drattsev E Yu, Vikulov AD, Mel’nikov A. 2007. Relationship between rheovasographic

parameters and double product. Human Physiology 33:110-114.

Driziene Z, Jakutiene E, Stakisaitis D, Pundziene B, Sveikata A. 2008. Characteristics of gender-

related circadian arterial blood pressure in healthy adolescents. Medicina (Kaunas)

44:768-774.

Gowan JM, Pike K, Burr RL. 1995. Effects of gender and age on heart rate variability in healthy

individuals and in persons after sudden cardiac arrest. J Electrocardiol. 27 Suppl:1-9.

Halberg F, Katinas G, Cornelissen G, Schwartzkopff O, Fiser B, Siegelova J. 2004. When week-

long monitoring is not enough: case reports. Scripta Medica.77:255-260.

Halhuber MJ, Cornelissen G, Bartter FC, Delea CS, Kreze A, Mikulecky M, Müller-Bohn T,

Siegelova J, Dusek J, Schwartzkopff O, Halberg F. 2002. Circadian urinary

glucocorticoid and rhythmic blood pressure coordination. Scripta Medica. 75:139-144.

Harshfield GA, Alpert BS, Willey ES, Somes GW, Murphy JK, Dupaul LM. 1989. Race and

gender influence ambulatory blood pressure patterns of adolescents. Hypertension

14:598-603.

Hermida RC, Chayan L, Ayala DE, Mojón A, Dominguez MJ, Fontao MJ. 2009. Association of

metabolic syndrome and blood pressure nondipping profile in untreated hypertension. Am

J Hypertens. 22:307-313.

Hermida RC, Fernandez JR, Ayala DE, Mojon A, Alonso L, Smolensky M. 2001. Circadian

rhythm in double (rate-pressure) product in healthy normotensive young subjects.

Chronobiol Int. 18:475-489.

Huikuri HV, Pikkujamsa SM, Airaksinen KEJ, Ikaheimo MJ, Rantala AO, Kauma H, Lilja M,

Kesaniemi YA. 1996. Sex-related differences in autonomic modulation of heart rate in

middle-aged subjects. Circulation 94:122-125.

Jensen-Urstad K, Storck N, Bouvier F, Ericson M, Lindblad LE, Jensen-Urstad M. 1997. Heart

rate variability in healthy subjects is related to age and gender. Acta Physiol Scand.

160:235-241.

Kario K, Schwartz JE, Pickering TG. 2000. Changes of nocturnal blood pressure dipping status

in hypertensives by nighttime dosing of α-Adrenergic blocker, Doxazosin results from

the HALT study. Hypertension 35:787-794.

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Driziene%20Z%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Jakutiene%20E%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Stakisaitis%20D%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Pundziene%20B%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Sveikata%20A%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Jensen-Urstad%20K%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Storck%20N%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Bouvier%20F%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Ericson%20M%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Lindblad%20LE%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Jensen-Urstad%20M%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

12

Kleiger RE, Miller JP, Bigger JT, Moss A, Multicenter Post-Infarction Research Group. 1987.

Decreased heart rate variability and its association with increased mortality after acute

myocardial infarction. Am J Cardiol. 59:256-262.

Kohno I, Iwasaki H, Okutani M, Sano S, Mochizuki Y, Sano S. 1998. Circadian blood pressure

and heart rate profile in normotensive patients with mild hyperthyroidism. Chronobiol

Int.15:337-347.

Kumar A, Nagtilak S, Sivakanesan R, Gunasekera S. 2009. Cardiovascular risk factors in elderly

normolipidemic acute myocardial infarct patients - A case controlled study from India.

Southeast Asian J Trop Med Public Health 40:581-592.

Larochelle P. 2002. Circadian variation in blood pressure: dipper or non-dipper. The Journal of

Clinical Hypertension 4:3-8.

Martin EA, Tan S-L, MacBride LR, Lavi S. 2008. Sex differences in vascular and endothelial

responses to acute mental stress. Clin Auton Res. 18:339-345.

Mitsutake G, Cornélissen G, Otsuka K, Dawes C, Burch J, Rawson MJ, Siegelová J, Jancik J,

Masek M, Pazdirek J, Halberg F. 2002. Relationship between positive and negative

moods and blood pressure in a clinically healthy man. Scripta Medica. 75:315-320.

Nelson W, Tong YL, Lee JK, Halberg F. 1979. Methods of Cosinor-rhythmometry.

Chronobiologia 6:305-323.

O’Brien E, Sheridan J, O’Malley K. 1988. Dippers and nondippers (Letter). Lancet 13:397.

O’Brien IAD, O’Hare P, Corrall RJM. 1986. Heart rate variability in healthy subjects: Effect of

age and the derivation of normal ranges for tests of autonomic function. Br Heart J.

55:348-354.

Ohkubo T, Hozawa A, Yamaguchi J, Kikuya M, Ohmori K, Michimata M, Matsubara M,

Hashimoto J, Hoshi H, Araki T, Tsuji I, Satoh H, Hisamichi S, Imai Y. 2002. Prognostic

significance of the nocturnal decline in blood pressure in individuals with and without

high 24-h blood pressure: The Ohasama study. J Hypertens. 20:2183-2189.

Ori Z, Monir G, Weiss J, Sayhouni X, Singer DH. 1992. Heart rate variability: Frequency

domain analysis. Cardiol Clin. 10:499-537.

Otsuka K, Watanabe H, Cornélissen G, Shinoda M, Uezono K, Kawasaki T, Halberg F. 1990.

Gender, age and circadian blood pressure variation of apparently healthy rural vs

metropolitan Japanese. Chronobiologia 17:253-265.

Parati G, Ongaro G, Bilo G. 2003. Non invasive beat-to beat blood pressure monitoring: New

developments. Blood Press Monit. 8:31-36.

Parati G, Ravogli A, Giannattasio C, Mutti E, Trazzi S, Villani A, Mancia G. 1992. Changes in

24 hour blood pressure and in cardiac and vascular structure in normotensive subjects

with parental hypertension. Clin Exp Hypertens A. 14:67-83.

Perez-Lloret S, Aguirre AG, Cardinali DP, Toblli JE. 2004. Disruption of ultradian and circadian

rhythms of blood pressure in nondipper hypertensive patients. Hypertension 44:311-315.

Pistrosch F, Reissmann E, Wildbrett J, Koehler C, Hanefeld M. 2007. Relationship between

diurnal blood pressure variation and diurnal blood glucose levels in type 2 diabetic

patients. Am J Hyperten. 20:546-547.

Profant J, Dimsdale JE. 1999. Race and diurnal blood pressure patterns: A review and meta-

analysis. Hypertension 33:1099-1104.

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Ohkubo%20T%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Hozawa%20A%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Yamaguchi%20J%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Kikuya%20M%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Ohmori%20K%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Michimata%20M%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVAbstract

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Otsuka%20K%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Watanabe%20H%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Corn%C3%A9lissen%20G%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Shinoda%20M%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Uezono%20K%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Kawasaki%20T%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Halberg%20F%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Perez-Lloret%20S%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Aguirre%20AG%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Cardinali%20DP%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Toblli%20JE%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Pistrosch%20F%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Reissmann%20E%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Wildbrett%20J%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Koehler%20C%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Hanefeld%20M%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

13

Rawson MJ, Cornelissen G, Holte J, Katinas G, Eckert E, Siegelova J, Fiser B, Halberg F. 2000.

Circadian and circaseptan components of blood pressure and heart rate during depression.

Scripta Medica. 73:117-124.

Redfern PH, Lemmer B. 1997. Physiology and Pharmacology of Circadian Rhythms. Handbook

of Experimental Pharmacology. (Springer Berlin). 125.

Redon J. 2004. The normal circadian pattern of blood pressure: Implications for treatment. Int J

Pract. 58:3-8.

Rich MW, Saini JS, Kleiger RE, Carney RM, teVelde A, Freedland KE. 1988. Correlation of

heart rate variability with clinical and angiographic variables and late mortality after

coronary angiography. Am J Cardiol. 62:714-717.

Routledge FS, McFetridge-Durdle JA, Dean CR. 2007. Canadian Hypertension Society. Night-

time blood pressure patterns and target organ damage: A review. Can J Cardiol. 23:132-

138.

Ryan SM, Goldberger AL, Pincus SM, Mietus J, Lipsitz LA. 1994. Gender- and age-related

differences in heart rate dynamics: Are women more complex than men? J Am Coll

Cardiol. 24:1700-1707.

Shannon DC, Carley DW, Benson H. 1987. Aging of modulation of heart rate. Am J Physiol.

253:H874-H877.

Singer DH, Ori Z. 1995. Heart rate variability. Armonk (NY): Futura Press. Change in heart rate

variability associated with sudden cardiac death; 429–448.

Suzuki Y, Kuwajima I, Kuramoto K, Otsuka K. 1993. Age and gender difference in circadian

rhythm in blood pressure. Nippon Ronen Igakkai Zasshi. 30:778-786.

Task Force of the European Society of Cardiology and the North American Society of Pacing

and Electrophysiology. 1996. Heart rate variability standards of measurement,

physiological interpretation, and clinical use. Circulation 93:1043- 1065.

Thom T, Haase N, Rosamond W, Howard VJ, Rumsfeld J, Manolio T, Zheng ZJ, Flegal K,

O'Donnell C, Kittner S, Lloyd-Jones D, Goff DC Jr, Hong Y, Adams R, Friday G, Furie

K, Gorelick P, Kissela B, Marler J, Meigs J, Roger V, Sidney S, Sorlie P, Steinberger J,

Wasserthiel-Smoller S, Wilson M, Wolf P. 2006. Heart disease and stroke statistics–2006

update: A report from the American Heart Association Statistics Committee and Stroke

Statistics Subcommittee. Circulation 113:e85–e151.

Thomas GP, Daichi S, Haas D. 2006. Ambulatory blood pressure monitoring. N Engl J Med.

354:2368-2374.

Umetani K, Singer DH, McCraty FR, Atkinson M. 1998. Twenty-four hour time domain heart

rate variability and heart rate: Relations to age and gender over nine decades. J Am Coll

Cardiol. 31:593-601.

Van de Borne P, Nguyen H, Biston P, Linkowski P, Degaute JP. 1994. Effects of wake and sleep

stages on 24-h autonomic control of blood pressure and heart rate in recumbent men. Am

J Physiol. 266:H548-H554.

Van Hoogenhuyze D, Weinstein N, Martin GJ, Weiss JS, Schaad JW, Sahyouni XN, Fintel D,

Remme WJ, Singer DH. 1991. Reproducibility and relationship to mean heart rate of

heart rate variability in normal subjects and in patients with congestive heart failure

secondary to coronary artery disease. Am J Cardiol. 68:1668-1676.

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Routledge%20FS%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22McFetridge-Durdle%20JA%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Dean%20CR%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term=%22Canadian%20Hypertension%20Society%22%5BCorporate%20Author%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus

14

Waterhouse J, Atkinson G, Reilly T, Jones H, Edwards B. 2007. Chronophysiology of the

cardiovascular system. Biol Rhythm Res. 38:181-194.

Verdecchia P, Schillaci G, Reboldi G, de Simone G, Porcellati C. 2001. Original articles

prognostic value of combined echocardiography and ambulatory blood pressure

monitoring in hypertensive patients at low or medium cardiovascular risk. Ital Heart J.

2:287-293.

White WB, Schulman P, McCabe EJ, Dey HM. 1989. Average daily blood pressure, not office

blood pressure, determines cardiac function in patients with hypertension. JAMA

261:873-877.

Zheng L, Sun Z, Li J, Zhang R, Zhang X. 2008. Pulse pressure and mean arterial pressure in

relation to ischemic stroke among patients with uncontrolled hypertension in rural areas

of china. American Heart Association 39:1932-1937.

http://www.informaworld.com/smpp/title~db=all~content=t713734219~tab=issueslist~branches=38

15

Table 1. Rhythm detection ratio of SBP, DBP, HR, DP and MAP as function of gender

and dipping pattern

Group N SBP DBP HR DP MAP

Male 40 0.82 0.82 0.95 0.97 0.82

Female 60 0.85 0.85 0.93 1 0.86

Extreme dipper 13 0.95 1 1 1 0.82

Dipper 63 0.92 0.85 1 1 0.85

Nondipper 24 0.64 0.64 0.82 0.85 0.64

All 100 0.83 0.83 0.94 0.98 0.84

Table 2. Prevalence of prominent periods of SBP, DBP, HR, DP and MAP as function of

gender and dipping pattern

Prominent

period

SBP DBP HR DP MAP

Gender

Male 24 - h 29 22 32 35 26

12 - h 8 9 1 3 8

Other 3 9 7 2 6

Female 24 - h 36 37 55 57 40

12 - h 9 14 1 1 10

Other 15 09 4 2 10

Dipping pattern

Extreme dipper 24 - h 7 6 10 11 8

12 - h 3 4 1 1 2

Other 3 3 2 1 3

Dipper 24 - h 47 44 50 60 50

12 - h 10 9 8 2 9

Other 6 10 5 1 4

Nondipper 24 - h 9 10 20 21 10

12 - h 5 9 1 1 6

Other 10 5 3 2 8

All 24 - h 65 59 87 92 66

12 - h 17 23 2 4 18

Other 18 18 11 4 16

16

Table 3. Comparison of circadian Mesors, amplitude and acrophase (Mean ± SD) of SBP, DBP, HR,

DP and MAP as function of gender

Variable Male Female t value; df; p

Mesor

SBP 121.86 ± 1.86 106.40 ± 0.95 8.09; 98; <0.001

DBP 074.15 ± 1.10 067.74 ± 0.54 5.74; 98; <0.001

HR 077.63 ± 1.05 081.62 ± 0.93 2.79; 98; <0.01

DP 095.94 ± 2.32 087.78 ± 1.11 3.50; 98; <0.001

MAP 089.92 ± 1.35 080.28 ± 0.64 7.11; 98; <0.001

Amplitude

SBP 10.13 ± 0.91 09.34 ± 0.59 0.76; 98; 0.45

DBP 06.90 ± 0.52 07.49 ± 0.41 0.88; 98; 0.38

HR 10.64 ± 0.65 11.53 ± 0.45 1.16; 98; 0.25

DP 19.89 ± 1.18 19.13 ± 0.68 0.59; 98; 0.56

MAP 07.72 ± 0.60 07.93 ± 0.48 0.27; 98; 0.79

Acrophase

SBP 15.89 ± 0.28 15.39 ± 0.18 1.54; 98; 0.13

DBP 15.85 ± 0.31 15.14 ± 0.19 2.06; 98; <0.05

HR 15.78 ± 0.27 15.81 ± 0.17 0.09; 98; 0.93

DP 15.77 ± 0.25 15.44 ± 0.16 1.15; 98; 0.25

MAP 15.95 ± 0.30 15.02 ± 0.18 2.86; 98; <0.01

17

Table 4. Comparison of circadian Mesors, amplitude, and acrophase (Mean ± SD) of SBP,

DBP, HR, DP and MAP as function of dipping pattern

Variable Extreme Dipper Dipper Non-dipper f-value; df; p

Mesor

SBP 119.34 ± 3.74b 110.08 ± 1.31

a 115.50 ± 2.77

ab 4.40; 2,97; <0.05

DBP 073.73 ± 1.82b 069.21 ± 0.61

a 071.31 ± 1.76

ab 3.34; 2,97; <0.05

HR 081.95 ± 1.99a 080.10 ± 0.94

a 078.78 ± 1.35

a 0.82; 2,97; 0.44

DP 098.77 ± 3.59b 089.02 ± 1.17

a 092.16 ± 3.28

ab 3.88; 2,97; <0.05

MAP 088.93 ± 2.43 b 082.52 ± 0.82

a 085.78 ± 2.10

ab 4.26; 2,97; <0.05

Amplitude

SBP 15.44 ± 1.82c 09.66 ± 0.54

b 06.53 ± 0.63

a 17.16; 2,97; <0.001

DBP 10.32 ± 0.96b 07.18 ± 0.37

a 05.80 ± 0.61

a 9.63; 2,97; <0.001

HR 10.46 ± 0.60a 11.17 ± 0.47

a 11.58 ± 0.91

a 0.37; 2,97; 0.69

DP 24.15 ± 1.67b 19.00 ± 0.70

a 18.02 ± 1.42

a 4.86; 2,97; <0.01

MAP 11.85 ± 1.16c 07.82 ± 0.43

b 05.75 ± 0.53

a 14.05; 2,97; <0.001

Acrophase

SBP 14.9 ± 0.37a 15.3 ± 0.18

a 16.5 ± 0.37

b 5.79; 2,97; <0.01

DBP 14.8 ± 0.39a 15.4 ± 0.19

ab 15.9 ± 0.45

b 1.81; 2,97; 0.17

HR 15.5 ± 0.39a 15.7 ± 0.18

a 16.1 ± 0.34

a 0.73; 2,97; 0.48

DP 15.3 ± 0.33a 15.4 ± 0.17

a 16.1 ± 0.30

a 2.42; 2,97; 0.09

MAP 14.9 ± 0.37a 15.2 ± 0.18

a 16.2 ± 0.42

b 4.53; 2,97; <0.05

Means bearing similar superscripts do not differ from each other at p 0.05 (based on Duncan’s

multiple-range test)

Table 5. Overall nocturnal dipping in BP, computed using method of dipping criteria

based on SBP, in extreme dippers, dippers and non-dippers as function of gender

Group Male Female t value; df; p

Extreme Dippers 22.86 ± 1.61 26.46 ± 2.31 0.97; 11; 0.35

Dippers 14.66 ± 0.46 15.15 ± 0.44 0.71; 61; 0.48

Non dippers 06.75 ± 0.65 07.16 ± 0.95 0.36; 22; 0.72

All (ED + D + ND) 12.91 ± 0.86 15.38 ± 0.86 1.94; 98; 0.055

Circadian heart rate and blood pressure variability in...

Documents

Transcript of Circadian heart rate and blood pressure variability in...