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(Stroke. 1996;27:1793-1797.)
© 1996 American Heart Association, Inc.

Articles

Circadian and Circannual Rhythmicity in the Occurrence of Subarachnoid Hemorrhage

Massimo Gallerani, MD; Francesco Portaluppi, MD; Giuseppe Maida, MD; Arturo Chieregato, MD; Ferdinando Calzolari, MD; Giorgio Trapella, MD Roberto Manfredini, MD

the Emergency Department (M.G.) and the Departments of Anesthesiology (A.C.) and Neuroradiology (F.C.), St Anna Hospital, Ferrara, Italy, and the Departments of Internal Medicine (F.P., R.M.) and Neurosurgery (G.M., G.T.), University of Ferrara (Italy).

Correspondence to Massimo Gallerani, MD, Emergency Department, St Anna Hospital, corso Giovecca 203, I-44100 Ferrara, Italy.

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Background and Purpose Inconsistent data are available on the temporal pattern of onset of subarachnoid hemorrhage (SAH). We investigated the possible influence of vascular risk factors.

Methods Of a consecutive series of 217 cases of SAH, precise determination (within 30 minutes) of the time of symptom onset was possible in 199 (91.7%). Partial Fourier series with up to six harmonics were applied to hourly and monthly data, and the best-fitting curves for circadian and annual rhythmicity were calculated. The amplitude-MESOR (rhythm-adjusted mean over the time period analyzed) ratio was used as a measure of temporal variability.

Results In the total population, a significant circadian pattern of occurrence was demonstrated with major peaks in the morning (9 AM) and evening (9 PM) hours and a nocturnal trough (3 AM). Younger, male, and hypertensive subjects had lower amplitude-MESOR ratios; smokers had no significant rhythmicity. The annual pattern showed a 6-month periodicity with two major peaks in March and September and minor differences in the subgroups studied.

Conclusions Our study indicates that the temporal distribution in onset of SAH may be influenced by variable combinations of environmental and vascular risk factors.

Key Words: circadian rhythm • risk factors • stroke onset • subarachnoid hemorrhage

	Introduction

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Since the first report of "less common onset at night,"¹ several investigators have studied temporal distribution in the onset of subarachnoid hemorrhage (SAH), but with inconsistent results. Some reported no significant circadian distribution² or significant circadian distribution only in hypertensive subjects,³ whereas others found a major peak of occurrence in the morning⁴ or in the afternoon.⁵ A bimodal distribution, with a major morning and a secondary evening peak, was also reported.^{6 7}

Seasonal distribution has also been investigated. Some authors detected a peak in either autumn⁴ or winter,^{8 9 10 11} whereas others found no seasonal peak.^{2 12 13 14 15}

Vascular risk factors have specific temporal distributions (see Reference 16 for review), and the onset of vascular accidents such as SAH is likely to be influenced by variable combinations of these rhythms.

Based on these considerations, we studied temporal variability in the occurrence of SAH, taking into account possible differences due to etiology, age, sex, and certain major risk factors.

	Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Ferrara is a town in northern Italy with approximately 150 000 inhabitants with a distribution of age, sex, and socioeconomic status similar to that of Italy as a whole. The population is almost exclusively white. The only available hospital in this community is St Anna Hospital, which also serves as the sole teaching center for the School of Medicine. For geographic (other hospitals with acute-care facilities are not available for a radius of 40 miles) and organizational reasons (there is one transport service for all out-of-hospital emergencies, with vehicles strategically distributed throughout the territory and average time of arrival at the Emergency Department 15 minutes from the time the call is received) and because of the excellent standard of care of the local medical facilities, there is very little, if any, loss of cases because of hospitalization elsewhere. House calls are performed by family physicians during the daytime hours (8 AM to 8 PM) and by physicians of a specific section of the Emergency Department during nighttime hours and holidays at no charge for the patient. Thus, the Emergency Department has an uninterrupted flow of 57 000 patient visits for all causes per year. Key physicians, including neurologists and neurosurgeons, are active in the hospital 24 hours a day throughout the year.

Between January 1985 and December 1994, a consecutive series of 217 cases of SAH were observed in the St Anna Hospital of Ferrara. Precise determination (within 30 minutes, witnessed) of the time of symptom onset was possible in 199 subjects (91.7%). The remaining subjects were excluded from the analysis. The diagnosis was always confirmed by either CT, angiography, surgical intervention, or autopsy. This allowed us to distinguish primary SAH from ruptures of cerebral aneurysms or arteriovenous malformations (secondary SAH). Alcohol or drug abuse ("yes" or "no"), smoking habit ("yes" or "no"), history of hypertension ("yes" if either a record of antihypertensive treatment or mean systolic/diastolic blood pressure >140/90 on at least three separate occasions before the episode that led to the diagnosis of SAH; "no" if none of the above), and diabetes mellitus ("yes" if, before the SAH episode, either a record of antidiabetic treatment, plasma glucose level 140 mg/dL after an overnight fast on two occasions, or positive glucose tolerance test on at least two occasions outside pregnancy and drug treatments that can impair glucose tolerance; "no" if none of the above) were assessed by patient or relative interviews integrated with the clinical records provided by the family physician.

Month, day, and clock hour of symptom onset were categorized into 24 1-hour increments (eg, 6:00 to 6:59 AM reported as 6:00 AM) and 12 1-month intervals.

Group comparisons were performed with either Student's t test for unpaired data, Wilcoxon's test, or when applicable, one-way ANOVA after homogeneity of variances was assessed with Bartlett's test. The Student-Newman-Keuls multiple comparison test was used as a post hoc procedure.

The presence of a significant temporal distribution was first tested after all cases were categorized into 24 l-hour and 12 l-month periods. A ² test for goodness of fit was applied to the number of observed versus expected episodes of SAH during the hourly and monthly intervals. ANOVA was used for the same purpose.

We then analyzed rhythmicity by applying a partial Fourier series with up to six harmonics (periods of 24, 12, 8, 6, 4.8, and 4 hours for circadian rhythmicity and periods of 12, 6, 4, and 3 months for circannual rhythmicity) to the time series data using Chronolab software on an Apple Macintosh computer.¹⁷ The program permits, among all the possible combinations of the periods chosen by the user, the selection of the harmonic or the combination of harmonics that best explains the variance of data. The percentage of rhythm (percentage of overall variability of data about the arithmetic mean attributable to the fitted rhythmic function) and the probability value resulting from the F statistic used to test the hypothesis of zero amplitude were chosen to be reported in the results as representative parameters of goodness of fit and statistical significance of each fitted function, respectively. The program calculates the midline estimating statistic of rhythm (MESOR, the rhythm-adjusted mean over the time period analyzed) and the amplitude (half the distance between the absolute maximum and minimum of the function) of the best-fitting curve. By division of the two values, the amplitude-MESOR ratio is then calculated and used as a quantitative measure of temporal variability. The program also calculates peak and trough times of the fitted curve (times of occurrence of the absolute maximum and minimum) and the acrophase of each single harmonic (peak time of rhythmic change).

Significance levels were always assumed for P<.05.

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
An older age was observed in secondary forms of SAH and female, normotensive, and nonsmoking subjects compared with primary SAH and male, hypertensive, and smoking patients, respectively (Table 1). Diabetes mellitus was present in only seven subjects (3.5%), and therefore no subgroup analysis was performed for this risk factor. Drug and alcohol abuse were never found in this population.

View this table: [in this window] [in a new window]   Table 1. Characteristics of Study Population

The circadian distribution of onset of SAH in the total population showed a pattern (Fig 1) with maximal occurrence in the morning hours (9:00 AM), secondary peak in the evening (9:00 PM), and minimal occurrence at night (3:00 AM). This pattern was reflected by the best-fitting curve (Fig 1), calculated by rhythm analysis, which resulted from two significant components of 24-hour and 12-hour periods, respectively (Table 2). No significant additional harmonic was found. In the subgroups analyzed, SAH showed a similar circadian pattern, although sometimes either the 24-hour or the 12-hour component did not reach statistical significance. Only the subgroup of smokers showed no significant pattern at all. Moreover, analysis of rhythmicity demonstrated that secondary forms of SAH, women, normotensives, nonsmokers, and older patients had higher amplitude-MESOR ratios (in other words, a more pronounced circadian variability) compared with primary forms of SAH, men, hypertensives, and smokers, respectively.

View larger version (85K): [in this window] [in a new window]   Figure 1. Twenty-four–hour distribution of onset of subarachnoid hemorrhage. Histograms represent percentage of total events (n=199) occurring in each hour of the day. Superimposed is the best-fitting curve calculated by rhythm analysis with a partial Fourier series. The parameters of the curve are given in Table 2 under "Total subjects."

View this table: [in this window] [in a new window]   Table 2. 24-Hour Rhythmicity in Onset of Subarachnoid Hemorrhage

In the total population, the monthly distribution of onset of SAH showed maximal occurrence in spring and autumn (Fig 2). Analysis of annual rhythmicity (Table 3) confirmed the presence of a significant 6-month periodicity, with two peaks occurring in March and September. A similar pattern was observed in the subgroups analyzed, with failure to reach statistical significance in a few subgroups and no appreciable differences in the amplitude-MESOR ratios.

View larger version (79K): [in this window] [in a new window]   Figure 2. Monthly distribution of onset of subarachnoid hemorrhage. Histograms represent percentage of total events (n=199) occurring in each month of the year. Superimposed is the best-fitting curve calculated by rhythm analysis with a partial Fourier series. The parameters of the curve are given in Table 3 under "Total subjects."

View this table: [in this window] [in a new window]   Table 3. Annual Rhythmicity in Onset of Subarachnoid Hemorrhage

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
The main results of this study confirm the existence of both circadian and seasonal patterns of onset of SAH and indicate that such temporal variability is influenced by different combinations of cardiovascular risk factors.

A possible concern regarding our findings is that a reduced sample size might have affected the statistical significance of the temporal variation of SAH onset in the smaller strata defined by risk factors. However, no relationship was found between statistical significance and sample size, since significance was not reached in a wide range of sample sizes (60 to 123); on the other hand, significance was detected in one instance with a minimal sample size of 60 (Tables 2 and 3). Moreover, the SD for the number of events per time period was equal to 4.9 (hourly) and 5.8 (monthly) for our total population of subjects with SAH. With the aforementioned SDs, a sample size of 60 permits the detection of a difference of three events per time period at a significance level () of 0.05, with a statistical power of 0.90 and 0.80 for the hourly and monthly data, respectively. Hence, the statistical power was acceptable even in the smallest subgroups analyzed, and a type II statistical error seems unlikely for our findings.

Another possible concern about our findings is the reliability of the data source. However, the peculiar setting of this study (see "Subjects and Methods") makes it unlikely that a significant number of SAH episodes occurring in the Ferrara community could have been excluded from our observation or that other external factors related to the data source influenced the rhythmicities that we found. Even patients who die during transportation to the hospital are routinely diagnosed postmortem, since specific local laws require autopsy in these cases. The only exceptions are the most severely ill patients who die at home before an emergency call is made or answered. However, SAH usually occurs while the patient is active (thus enhancing the probability that an emergency is called) and seldom leads to death in a matter of minutes. Therefore, the number of SAH patients who die undiagnosed in the Ferrara community is not likely to have significantly influenced our findings.

A bimodal circadian distribution in the onset of SAH, similar to our results, was previously reported.^{6 7} However, other studies reported a different bimodal distribution (with morning and afternoon peaks) in hypertensive patients but no significant circadian pattern in normotensive patients,³ a single peak of onset in either the morning⁴ or the afternoon,⁵ or no significant circadian distribution.²

The inconsistent findings reported in the literature could reflect the different qualities of the studies, when we consider that only three^{4 14 15} of the 15 that we found^{1 2 3 4 5 6 7 8 9 10 11 12 13 14 15} were community based. Our findings indicate, however, that different combinations of pathophysiological and risk factors in the various populations may have contributed significantly to this inconsistency. In this study, younger age, male sex, hypertension, and particularly smoking were associated with decreased or even nonsignificant circadian variability in the onset of SAH, as indicated by decreasing amplitude-MESOR ratios, eventually leading to a loss in statistical significance of any rhythmic component. SAH is likely to occur at a younger age in individuals with a higher vascular risk. Male sex, hypertension, and smoking are also associated with a higher vascular risk and in our study population appeared to correlate with SAH onset at a younger age.

The circadian profile of blood pressure is the major determinant of the circadian distribution of SAH onset. Accordingly, a correlation between hourly occurrence of SAH and corresponding mean systolic and diastolic blood pressure values of ambulant normotensive and hypertensive subjects is demonstrated.⁷ Usually, blood pressure shows two daytime peaks and a nocturnal nadir, which closely resembles the circadian distribution of SAH onset.⁷ However, with increasing severity of hypertension and onset of left ventricular hypertrophy, the nocturnal fall in blood pressure is lost or even reversed,^{18 19 20} and the nocturnal impact of blood pressure on the vascular walls is increased. This is likely to increase the risk of nocturnal onset of SAH.

Smoking increases variability throughout the day and night and decreases nocturnal levels of blood pressure.^{21 22} Blood pressure variability is an established determinant of onset of cardiovascular accidents.²³ Moreover, smoking causes transient increments in arterial wall stiffness that increase the risk for wall damage.²⁴ These mechanisms may affect the circadian distribution of SAH onset in smokers and may explain the blunt onset pattern that we found in this subgroup.

Altogether, it seems possible to speculate that the circadian pattern of SAH onset results from a variable combination of temporal patterns of various pathophysiological mechanisms. Usually, the morning and evening hours coincide with peak activation of these mechanisms, whereas the nocturnal hours are times of lowest risk. However, different combinations of factors involved and/or possible changes in the specific temporal pattern of one or more of the intervening factors may affect the circadian pattern of SAH onset.

We documented a significant 6-month periodicity in the onset of SAH, with two peaks occurring in spring and autumn. A previous Italian study also reported a peak of SAH onset in autumn.⁴ In contrast, in populations from Northern Europe and the northern United States, either a winter peak of SAH onset^{8 9 10 11} or no seasonal periodicity was found.^{2 12 13 14 15}

A seasonal influence on blood pressure has been demonstrated, with higher blood pressures in winter than in summer.²⁵ Thus, it is commonly accepted that environmental temperature has an inverse relation to blood pressure.²⁶ During peripheral vasoconstriction, systolic blood pressure increases more than diastolic, so that pulse amplitude is generally increased, although there is a little change in cardiac output or pulse rate.²⁷ This enhances the forces acting to produce wall deformation and increases friction and shear stress on the internal surface. The consequent vascular damage may progress until vessel dissection is determined.²⁸ Such mechanisms, undoubtedly present in Italy during the colder months, could also be activated and exacerbated in this geographic area in the months of the transitional seasons of spring and autumn, when frequent stress episodes exerted on the arterial wall by typical temperature excursions may precipitate aneurysmal rupture to a greater degree than the more stable (but not rigid) winter cold. Hence, environmental factors related to the geographic area of the populations studied may explain the seemingly inconsistent data in the literature on the seasonal pattern of SAH onset.

In conclusion, this study confirms the existence of circadian and seasonal variations in onset of SAH and indicates that such temporal variability may be affected by different combinations of pathophysiological and environmental factors involved in the determination of the acute events and/or possible changes in the specific temporal pattern of one or more of the concomitant vascular risk factors.

	Acknowledgments

 
This study was supported in part by grants from the Italian Ministry of University and Scientific and Technological Research.

Received May 3, 1996; revision received June 10, 1996; accepted June 10, 1996.

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This Article Abstract Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Gallerani, M. Articles by Manfredini, R. Search for Related Content PubMed PubMed Citation Articles by Gallerani, M. Articles by Manfredini, R.