Heart rate circadian rhythm as a biological marker of desynchronization in major depression: a methodological and preliminary report

Heart rate (HR) was continuously monitored during successive 24-hr periods in 19 healthy subjects and 26 major depressed patients (DSM III-R). Recordings were performed after a 2-week wash-out period and the morningness or eveningness typology of each subject was determined. The chronobiological parameters and rhythm percentage (RP) were calculated by the single cosinor method from the smoothed HR curves of each subject. In normal subjects, HR follows a circadian rhythm (RP greater than 65%) with the lowest values at night. Morning type subjects have an earlier peak time (13:30) than evening type subjects (17:30). Major depressive patients were split into two groups; in the first one HR circadian rhythm was still present (RP greater than 63%) with a decrease in amplitude (24%) while in the second group, no circadian rhythm of HR could be detected (RP less than 25%, decrease in amplitude greater than 70%). In the group of patients with a persisting HR circadian rhythm, no veritable phase advance was observed. Our results suggest that circadian HR rhythm, which can be easily studied with non-invasive methods, might represent a chronobiological marker of some depressions. Given the lag that exists between the rhythms of morning type and evening type subjects, our study also stresses the importance of taking into account this behavioural trait in chronobiological studies.     

Heart Rate Orcadian Rhythm as a Biological Marker of Desynchronization in Major Depression: A Methodological and Preliminary Report

Heart rate (HR) was continuously monitored during successive 24-hr periods in 19 healthy subjects and 26 major depressed patients (DSM III-R). Recordings were performed after a 2-week wash-out period and the morningness or eveningness typology of each subject was determined. The chronobiological parameters and rhythm percentage (RP) were calculated by the single cosinor method from the smoothed HR curves of each subject. In normal subjects, HR follows a circadian rhythm (RP > 65%) with the lowest values at night. Morning type subjects have an earlier peak time (13:30) than evening type subjects (17:30). Major depressive patients were split into two groups; in the first one HR circadian rhythm was still present (RP > 63%) with a decrease in amplitude (24%) while in the second group, no circadian rhythm of HR could be detected (RP < 25%, decrease in amplitude > 70%). In the group of patients with a persisting HK circadian rhythm, no veritable phase advance was observed. Our results suggest that circadian HR rhythm, which can be easily studied with non-invasive methods, might represent a chronobiological marker of some depressions. Given the lag that exists between the rhythms of morning type and evening type subjects, our study also stresses the importance of taking into account this behavioural trait in chronobiological studies.     

The Maximization of Mean Vector Length for the Identification of Unknown Periods in Biological Rhythms: An Extension to Continuous Variables

In a recent paper Rocchi and Ghiandoni proposed the maximization of mean vector length as a circular statistics tool for the preliminary identification of unknown and unforeseeable periods in biological rhythms, but its application was limited to unitless data. In this paper an extension of the method to the case of continuous variables collected at equidistant time intervals is proposed. The extended method is proposed as an exploratory tool in the analysis of chronobiological data. Two different examples of the application of this method (to edible white truffle harvesting data and to TSH level data) are also included and compared with the Fourier spectral analysis.     

The Maximization of Mean Vector Length for the Identification of Unknown Periods in Biological Rhythms: An Extension to Continuous Variables

In a recent paper Rocchi and Ghiandoni proposed the maximization of mean vector length as a circular statistics tool for the preliminary identification of unknown and unforeseeable periods in biological rhythms, but its application was limited to unitless data. In this paper an extension of the method to the case of continuous variables collected at equidistant time intervals is proposed. The extended method is proposed as an exploratory tool in the analysis of chronobiological data. Two different examples of the application of this method (to edible white truffle harvesting data and to TSH level data) are also included and compared with the Fourier spectral analysis.     

GPS Based Daily Activity Patterns in European Red Deer and North American Elk (Cervus elaphus): Indication for a Weak Circadian Clock in Ungulates

Long-term tracking using global positioning systems (GPS) is widely used to study vertebrate movement ecology, including fine-scale habitat selection as well as large-scale migrations. These data have the potential to provide much more information about the behavior and ecology of wild vertebrates: here we explore the potential of using GPS datasets to assess timing of activity in a chronobiological context. We compared two different populations of deer (Cervus elaphus), one in the Netherlands (red deer), the other in Canada (elk). GPS tracking data were used to calculate the speed of the animals as a measure for activity to deduce unbiased daily activity rhythms over prolonged periods of time. Speed proved a valid measure for activity, this being validated by comparing GPS based activity data with head movements recorded by activity sensors, and the use of GPS locations was effective for generating long term chronobiological data. Deer showed crepuscular activity rhythms with activity peaks at sunrise (the Netherlands) or after sunrise (Canada) and at the end of civil twilight at dusk. The deer in Canada were mostly diurnal while the deer in the Netherlands were mostly nocturnal. On an annual scale, Canadian deer were more active during the summer months while deer in the Netherlands were more active during winter. We suggest that these differences were mainly driven by human disturbance (on a daily scale) and local weather (on an annual scale). In both populations, the crepuscular activity peaks in the morning and evening showed a stable timing relative to dawn and dusk twilight throughout the year, but marked periods of daily a-rhythmicity occurred in the individual records. We suggest that this might indicate that (changes in) light levels around twilight elicit a direct behavioral response while the contribution of an internal circadian timing mechanism might be weak or even absent.     

Pharmfit-a Nonlinear Fitting Program for Pharmacology

A new program is presented for nonlinear fitting of data from pharmacological and chronobiological investigations. It contains functions for calculating data from ligand-binding studies and competition experiments, for the analysis of dose-response curves, for pharmacokinetic calculations, and for cosine analysis of harmonic and overlapping rhythms. In addition, it is possible to implement general equations by the user. The program allows data exchange with most spreadsheet, database, and graphics presentation programs, and accepts data from two widely used ambulatory 24-h blood-pressure monitoring systems. The fitting procedure uses the Marquardt-Levenberg algorithm. It calculates the weighted or the unweighted fit together with a great variety of statistics for estimation of goodness of fit. A graphics module permits graphical presentation of the fitted curve. Moreover, fitting of data to different models can be compared for the most likely fit and model discrimination statistics for improvement of further experiments are provided. To demonstrate the chronobiological application of the fitting program PHARMFIT, the analysis of telemetric heart rate data from rats is presented.     

PHARMFIT—a Nonlinear Fitting Program for Pharmacology

A new program is presented for nonlinear fitting of data from pharmacological and chronobiological investigations. It contains functions for calculating data from ligand-binding studies and competition experiments, for the analysis of dose-response curves, for pharmacokinetic calculations, and for cosine analysis of harmonic and overlapping rhythms. In addition, it is possible to implement general equations by the user. The program allows data exchange with most spreadsheet, database, and graphics presentation programs, and accepts data from two widely used ambulatory 24-h blood-pressure monitoring systems. The fitting procedure uses the Marquardt–Levenberg algorithm. It calculates the weighted or the unweighted fit together with a great variety of statistics for estimation of goodness of fit. A graphics module permits graphical presentation of the fitted curve. Moreover, fitting of data to different models can be compared for the most likely fit and model discrimination statistics for improvement of further experiments are provided. To demonstrate the chronobiological application of the fitting program PHARMFIT, the analysis of telemetric heart rate data from rats is presented.     

Use of Phase for the Detection of Hidden Periodicites

Standard tests for the detection of hidden periodicities in time series are largely ignored by applied workers. Various simple but inappropriate methods are used instead. Therefore a method is suggested which is both simple and appropriate but which requires the prior knowledge of certain characteristics of the suspected periodicity. For illustration, this method is applied to a set of data from a chronobiological study.     

A Circular Statistics Tool for the Preliminary Identification of Unknown Periods in Biological Rhythms: An Application

A tool for the preliminary identification of unknown and unforeseeable periods in biological rhythms is presented. This method, called 'maximization of mean vector length', is based on circular statistics assumptions. It can be used effectively as an exploratory tool in the analysis of chronobiological data, while it carries a high risk of type I statistical error when used as a confirmative method. However, a correction term is provided in order to reduce this risk. An example of the application of this method to a set of spontaneous delivery data is also included.     

Methods for comparison of parameters from longitudinal rhythmometric models with multiple components

Multiple components linear least-squares methods have been proposed for the detection of periodic components in nonsinusoidal longitudinal time series. However, a proper test for comparison of parameters obtained from this method for two or more time series is not yet available. Accordingly, we propose two methods, one parametric and one nonparametric, to compare parameters from rhythmometric models with multiple components. The parametric method is based on techniques commonly and generally employed in linear regression analysis. The comparison of parameters among two or more time series is accomplished by the use of so-called dummy variables. The nonparametric method is based on bootstrap techniques. This approach basically tests if the difference in any given parameter obtained by fitting a model with the same periods to two different longitudinal time series differs from zero. This method calculates a confidence interval for the difference in the tested parameter. If this interval does not contain zero, it can be concluded that the parameters obtained from the two time series are different with high probability. An estimation of the p-value for the corresponding test can also be calculated. By the use of similar bootstrap techniques, confidence intervals can also be obtained for any parameter derived from the multiple component fit of several periods to nonsinusoidal longitudinal time series, including the orthophase (peak time), bathyphase (trough time), and global amplitude (difference between the maximum and the minimum) of the fitted model waveform. These methods represent a valuable tool for the comparison of rhythm parameters obtained by multiple component analysis, and they render this approach as a generally applicable one for waveform representation and detection of periodicities in nonsinusoidal, sparse, and noisy longitudinal time series sampled with either equidistant or unequidistant observations.     

WinCD: Windows Software for Complex Demodulation

We describe WinCD, a program for extracting quantitative information about periodicity in time-series data using the method of complex demodulation (CD). The method is particularly well suited for the analysis of the effects of variables that may produce changes in biological rhythms, such as sleep deprivation, adaptation to changes in work schedules, time zone displacements, and various sorts of pathology. WinCD enables exploratory analysis of time series data by providing graphical displays of raw and processed time series, as well as numerous options for viewing and saving quantitative data. We describe WinCD operations and examples of the use of the program.     

WinCD: Windows Software for Complex Demodulation

We describe WinCD, a program for extracting quantitative information about periodicity in time-series data using the method of complex demodulation (CD). The method is particularly well suited for the analysis of the effects of variables that may produce changes in biological rhythms, such as sleep deprivation, adaptation to changes in work schedules, time zone displacements, and various sorts of pathology. WinCD enables exploratory analysis of time series data by providing graphical displays of raw and processed time series, as well as numerous options for viewing and saving quantitative data. We describe WinCD operations and examples of the use of the program.     

Periodic fluctuations of the blood pressure and heart contraction rate in premature newborn infants

To study the postnatal development of circadian rhythm of the blood pressure and the heart rate these parameters were monitored automatically during 24-72 hours at 5h intervals. Fifty infants were investigated at the age 1, 2, 3, and 4 weeks. The results were compared with the cosine curves of different periods (1-48 h) by the IBM PC XT. The fluctuations with all mentioned periods including circadian could be determined in each infant, dominating period being of any duration. In traditional approach the expressiveness of periodical fluctuations is evaluated by the amplitude of cosine curve. Application of the criterion has shown that only amplitude values exceeding 8 mm Hg should be taken into account. Hence, the analysis of BP and HR time series has demonstrated that about 45-50% of the infants have noncircadian rhythms, 20-20% have no significant periodical fluctuations and only 25-30% have circadian rhythm.     

Sudden infant death syndrome: links with infant care practices.

OBJECTIVES--To investigate infant care practices in a small ethnic minority population within Britain that might suggest possible factors contributing to the low incidence of the sudden infant death syndrome in Asian populations. DESIGN--Ethnographic interviewing, a qualitative comparative method drawn from social anthropology. SETTING--Central Cardiff. SUBJECTS--Non-random sample of 60 mothers of Bangladeshi or Welsh ethnic origin and working or middle class occupational status, who had infants under one year old. None of the families interviewed had experienced a sudden infant death. RESULTS--Broad cultural contrasts emerged as a series of themes from the interview data: living patterns, family networks, sleeping patterns, and concepts of time and dependence. CONCLUSION--Bangladeshi infants were cared for in a consistently rich sensory environment; Welsh infants, in contrast, were more likely to experience alternating periods of high and low sensory input. Long periods of lone quiet sleep may be one factor that contributes to a higher rate of sudden deaths in white than in Asian infants.     

Dysregulation of circadian rhythms following prolactin-secreting pituitary microadenoma

A patient who developed an irregular sleep-wake pattern following prolactin-secreting pituitary microadenoma is described. The patient reported difficulties in sleep onset and awakening at the desired time, which caused major dysfunction in his daily life activities. Despite these difficulties, the sleep-related complaints of the patient remained unrecognized for as long as three yrs. Statistical analyses of the patient's rest-activity patterns revealed that the disruption of the sleep-wake circadian rhythm originated from a disharmony between ultradian (semicircadian) and circadian components. The circadian component displayed shorter than 24 h periodicity most of the time, but the semicircadian component fluctuated between longer and shorter than 12 h periods. Additionally, desynchrony in terms of period length was found in the tentative analyses of the rest-activity pattern, salivary melatonin, and oral temperature. While the salivary melatonin time series data could be characterized by a best-fit cosine curve of 24 h, the time series data of oral temperature was more compatible with 28 h best-fit curve. The rest-activity cycle during the simultaneous measurements, however, was best approximated by a best-fit curve of 21 h. The dysregulation of circadian rhythms occurred concomitantly, but not beforehand, with the onset of pituitary disease, thus suggesting an association between the two phenomena. This association may have interesting implications to the modeling of the circadian time-keeping system. This case also highlights the need to raise the awareness to circadian rhythm sleep disorders and to consider disruptions of sleep-wake cycle in patients with pituitary adenoma.     

Analysis of Telemetric Time Series data for Periodic Components Using Dq-Fit

DQ-FIT and CV-SORT have been developed to facilitate the automatic analysis of data sampled by radiotelemetry, but they can also be used with other data sampled in chronobiological settings. After import of data, DQ-FIT performs conventional linear, as well as rhythm analysis according to user-defined specifications. Linear analysis includes calculation of mean values, load values (percentage of values above a defined limit), highest and lowest readings, and areas under the (parameter-time) curve (AUC). All of these parameters are calculated for the total sampling interval and for user-defined day and night periods. Rhythm analysis is performed by fitting of partial Fourier series with up to six harmonics. The contribution of each harmonic to the overall variation of data is tested statistically; only those components are included in the best-fit function that contribute significantly. Parameters calculated in DQ-FIT's rhythm analysis include mesor, amplitudes, and acrophases of all rhythmic components; significance and percentage rhythm of the combined best fit; maximum and minimum of the fitted curve and times of their occurrence. In addition, DQ-FIT uses the first derivative of the fitted curve (i.e., its slope) to determine the time and extent of maximal increases and decreases within the total sampling interval or user-defined intervals of interest, such as the times of lights on or off. CV-SORT can be used to create tables or graphs from groups of data sets analyzed by DQ-FIT. Graphs are created in CV-SORT by calculation of group mean profiles from individual best-fit curves rather than their curve parameters. This approach allows the user to combine data sets that differ in the number and/ or period length of harmonics included. In conclusion, DQ-FIT and CV-SORT can be helpful in the analysis of time-dependent data sampled by telemetry or other monitoring systems. The software can be obtained on request by every interested researcher. (Chronobiology International, 14(6), 561–574, 1997)