Seasonal and Weekly Patterns of Occurrence of Acute Cardiovascular Diseases: Does a Gender Difference Exist?

Abstract

Background:

Cardiovascular (CV) disease is the leading cause of death in women. It is known that acute CV events exhibit temporal patterns of onset, that is, seasonal and weekly. We aimed to verify whether such patterns show differences by gender.

Methods:

We analyzed cumulative data from our previous studies dealing with hospital admissions for CV events, such as acute myocardial infarction (AMI), stroke, transient ischemic attack (TIA), aortic diseases (AD), and pulmonary embolism (PE), in the region Emilia-Romagna (RER) of Italy (ICDM9-CM codes, years 1998–2006). Total population and subgroups by gender (percentage of monthly and daily events) were tested for uniformity with the chi-square test, and a chronobiologic method was applied to monthly percentage of data for seasonal rhythmic analysis.

Results:

Season: We considered 130,693 patients (45.1% women): 64,191 AMI, 43,642 TIA, 4,615 AD, 19,425 PE. The monthly and seasonal distribution showed respective peaks in January and in winter, with no differences by gender. Day-of-week: We considered 168,921 patients (45.6% women): 64,191 AMI, 56,453 stroke, 43,642 TIA, 4,615 AD. The weekly distribution showed a peak on Monday, with no differences by gender. A multivariate regression logistic analysis, including in the model either major CV risk factors (hypertension, dyslipidemia, diabetes mellitus) and subgroups by age, did not find any difference in the temporal distribution of events in women and men.

Conclusions:

The seasonal and day-of-week distribution of occurrence of CV events seems to be independent of gender.

Introduction

Cardiovascular (CV) disease is the leading cause of death in women, accounting for approximately 40% of deaths,¹ but awareness is low, and fewer than 10% of women perceive heart disease as their greatest health risk.² Gender has not always the same impact on clinical outcome after major CV events, however. For example, no differences were found in level of care or mortality in women admitted to Veterans hospitals in the United States for acute myocardial infarction (AMI) compared with men,³ and women arrived at the emergency department (ED) at equivalent speed as men after ischemic stroke, but female sex contributed to poorer functional outcomes.⁴ Also, women hospitalized for pulmonary embolism (PE) showed a lower odds of 30-day mortality compared with men.⁵

In the last two decades, it has been widely reported that the occurrence of acute CV events is not randomly distributed over time but exhibits temporal patterns, that is, circadian or seasonal patterns. In particular, morning hours and winter months are characterized by higher frequency of onset of AMI,^6
–8 stroke,^9,10 aortic rupture or dissection,^11,12 and PE.¹³ A day-of-week variation, characterized by a Monday peak of onset, was found for AMI and stroke.^14,15 We aimed to investigate whether the seasonal or weekly pattern of presentation of major CV events may exhibit differences by gender.

Subjects and Methods

Recruitment of cases

We analyzed cumulative data of previous studies from our group dealing with temporal variation of hospital admission for CV events, including AMI, stroke, transient ischemic attack (TIA), aortic diseases (AD), and PE,^16

–21 in the region Emilia Romagna (RER) of Italy, according to the regional database maintained by the Center for Health Statistics.

Setting

The Emilia Romagna is a region of Northeastern Italy, with a total surface area of 22,124 km² and a total population of about 3,985,000 (approximately 7% of the entire Italian population). Commencing in 1998, the RER database began to collect all the discharge hospital sheets (the so-called Scheda di Dimissione Ospedaliera [SDO]) of patients admitted to all hospitals. The SDO contains information about surname and given name of each subject, gender, date of birth, date and department of hospital admission and discharge, first nine different discharge diagnoses, and most important diagnostic procedures, based on the International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM). We used time of admission to hospital as the day of onset of each disease because of the particular organization of public healthcare in Italy, which is completely free of charge for both in-hospital and out-hospital care. An Italian citizen can have access to a medical visit at the general practitioner's ambulatory, at home (by their own general practitioner during diurnal hours and by a community ward doctor during night hours and Saturdays and Sundays), or at the ED (24 hours/day). Thus, especially during weekends, it is more likely that patients go directly to the hospital for acute care. In the case of acute CV events, it is extremely unlikely that day of onset and day of hospitalization do not coincide. The total population was divided into subgroups by gender, presence of major CV risk factors (hypertension, dyslipidemia, and diabetes mellitus), and age (<60, 60–69, 70–79, >80 years).

Data collection and analysis

Monthly/seasonal

Data on monthly and seasonal admissions referred to AMI, TIA, AD, and PE.^16

–19 Hospital admission for each type of acute CV event (percentage of events) was categorized according to 12 1-month intervals and 4 3-month intervals (spring: March 21–June 20; summer: June 21–September 22; autumn: September 23–December 20; winter: December 21–March 20) for circannual and seasonal analysis, respectively.

Day-of-week

Data on day-of-week admission referred to AMI, stroke, TIA, and AD.^16,17,20,21 Hospital admission for each type of acute CV event (percentage of events) was categorized according to the day of the week.

Statistical analysis

The chi-square test and Bonferroni correction for multiple comparisons were used to compare different periods of admission and gender. To investigate the possible existence of a chronobiologic rhythmic variation, monthly percentages of events were analyzed by applying a partial Fourier analysis to the time series data using validated Chronolab software,²² which can select the harmonic or the combination of harmonics that best explains the variance of the time series data. The percentage of rhythm (PR, i.e., percentage of overall variability of data about the arithmetic mean of the fitted rhythmic function) and the probability value resulting from the F-statistic (hypothesis of zero amplitude) are representative parameters of goodness of fit and statistical significance of each fitted function, respectively. Finally, for evaluation of the risk of time of hospital admission, gender, major CV risk factors, and subgroups of age, a regression logistic analysis and a multivariable logistic regression analysis were used. Significance levels were assumed for p<0.05.

Results

Season

During the considered periods, the RER database contained the records of 130,693 patients (45.1% women) admitted for CV acute events (64,191 AMI, 43,642 TIA, 4,615 AD, 19,425 PE). The monthly distribution (percentage of daily events) showed a peak in January and a trough in July, with no differences between CV single disease and gender (Table 1). The chronobiologic analysis, performed to the monthly percentages of events, yielded a rhythmic seasonal variation (Table 2) characterized by peaks in winter for total cases (January, p=0.044), women (January, p=0.008), and men (January, p=0.102) (Fig. 1). Men, however, also showed a trend toward a biphasic pattern, with peaks in March and October (p=0.072). The seasonal distribution (percentage of monthly events) showed a winter–autumn peak and a summer trough (Fig. 2), with no differences between CV single disease and gender (Table 3).

FIG. 1.

Monthly distribution of hospital admissions (percentage of events) for acute cardiovascular (CV) events. (A) All cases. (B) Women. (C) Men. Cosinor fit is superimposed on the graphs.

FIG. 2.

Seasonal distribution of hospital admissions (percentage of events) for acute CV events. White bars, women; black bars, men.

Table 1.

Monthly Distribution of Acute Cardiovascular Events (% of Events) According to Gender

	Aneurysms ^a		Pulmonary embolism ^b		Acute myocardial infarction ^c		TIA ^d		All ^e
	Women n=1,243 26.9%	Men n=3,372 73.1%	Women n=11,102 57.7%	Men n=814 42.3%	Women n=23,805 37.1%	Men n=40,386 62.9%	Women n=23,765 54.5%	Men n=19,877 45.5%	Women n=23,765 54.5%	Men n=19,877 45.5%
January	8.7%	9.5%	9.4%	9.6%	9.1%	9.0%	8.3%	8.5%	8.8%	8.9%
February	8.8%	7.8%	8.4%	8.2%	8.0%	8.2%	7.7%	7.4%	8.0%	8.0%
March	9.0%	9.4%	8.7%	8.5%	8.5%	8.7%	8.6%	8.9%	8.6%	8.7%
April	8.0%	8.6%	8.7%	8.5%	8.2%	8.5%	8.2%	8.4%	8.1%	8.4%
May	8.4%	8.0%	7.7%	7.3%	8.0%	8.5%	8.5%	8.9%	8.2%	8.4%
June	8.2%	8.6%	6.4%	6.6%	7.6%	7.6%	8.5%	8.4%	7.8%	7.7%
July	8.8%	8.8%	7.6%	7.5%	7.2%	7.3%	8.0%	8.0%	7.6%	7.6%
August	7.8%	7.3%	8.3%	8.8%	7.4%	7.3%	8.3%	8.1%	7.9%	7.7%
September	7.9%	8.5%	8.0%	8.9%	8.4%	7.9%	8.5%	8.0%	8.4%	8.1%
October	8.7%	8.6%	9.5%	8.7%	9.2%	9.1%	8.8%	8.9%	9.1%	9.0%
November	7.0%	7.5%	9.3%	9.1%	9.2%	8.7%	8.4%	8.3%	8.9%	8.6%
December	8.6%	7.4%	9.2%	8.9%	9.0%	9.3%	8.1%	8.1%	8.7%	8.8%

Between women and men: chi-square=0.291, p=NS; ^bchi-square=0.127, p=NS; ^cchi-square=0.062, p=NS; ^dchi-square=0.044, p=NS; ^echi-square=0.024, p=NS.

TIA, transient ischemic attack.

Table 2.

Chronobiologic Analysis of Distribution (Monthly %) of Acute Cardiovascular Events: Total Cases and Subgroups by Gender

	n	Period (hours)	PR (%)	Peak	p
Total cases	131,693	8,766.00	50.1	January	0.044
Women	59,915	8,766.00	65.7	January	0.008
Men	71,778	8,766.00	39.7	January	0.102
		4,383.00	66.6	March–October	0.072

Peak (acrophase), the absolute maximum value during the observed period; PR, percentage of rhythm.

Table 3.

Seasonal Distribution of Acute Cardiovascular Events (% of Events) by Gender

	Aneurysms ^a		Pulmonary embolism ^b		Acute myocardial infarction ^c		TIA ^d		All ^e
	Women n=1,243 26.9%	Men n=3,372 73.1%	Women n=11,102 57.7%	Men n=814 42.3%	Women n=23,805 37.1%	Men n=40,386 62.9%	Women n=23,765 54.5%	Men n=19,877 45.5%	Women n=23,765 54.5%	Men n=19,877 45.5%
Winter	26.8%	25.8%	26.6%	26.3%	26.1%	26.3%	24.5%	24.6%	25.6%	25.8%
Spring	25.9%	27.5%	21.5%	21.9%	24.1%	24.9%	25.5%	26.0%	24.2%	25.0%
Summer	23.4%	22.7%	23.9%	25.1%	22.8%	22.2%	24.7%	24.2%	23.8%	23.1%
Autumn	23.9%	24.1%	28.0%	26.7%	27.0%	26.6%	25.3%	25.1%	26.5%	26.1%

Between women and men: chi-square=0.078, p=NS; ^bchi-square=0.066, p=NS; ^cchi-square=0.025, p=NS; ^dchi-square=0.011, p=NS; ^echi-square=0.027, p=NS.

Day of week

During the considered period, the RER database contained the records of 168,921 patients admitted for CV acute events (64,191 AMI, 56,453 stroke, 43,642 TIA, 4,615 AD; 45.6% females). The weekly distribution (percentage of daily events) showed a peak on Monday and a trough on Sunday (Fig. 3), with no differences between CV single disease and gender (Table 4).

FIG. 3.

Day-of-week distribution of hospital admissions (percentage of events) for acute CV events. White bars, women; black bars, men.

Table 4.

Day-of-Week Distribution of Acute Cardiovascular Events (% of Events) by Gender

	Aneurysms ^a		Pulmonary embolism ^b		Acute myocardial infarction ^c		TIA ^d		All ^e
	Women n=1,243 26.9%	Men n=3,372 73.1%	Women n=11,102 57.7%	Men n=814 42.3%	Women n=23,805 37.1%	Men n=40,386 62.9%	Women n=23,765 54.5%	Men n=19,877 45.5%	Women n=23,765 54.5%	Men n=19,877 45.5%
Monday	16.4%	16.5%	16.0%	17.0%	15.0%	16.0%	15.8%	16.3%	15.5%	16.2%
Tuesday	14.4%	14.8%	15.3%	15.2%	14.9%	14.8%	15.0%	15.1%	15.0%	14.9%
Wednesday	14.2%	15.5%	14.7%	15.3%	14.3%	14.3%	14.5%	14.9%	14.5%	14.6%
Thursday	14.2%	14.4%	15.4%	14.4%	14.7%	14.0%	14.7%	14.6%	14.8%	14.2%
Friday	14.6%	14.5%	14.8%	15.1%	14.3%	14.5%	14.9%	14.3%	14.7%	14.5%
Saturday	14.2%	12.2%	12.6%	12.5%	13.6%	13.4%	13.5%	13.1%	13.4%	13.2%
Sunday	11.8%	12.0%	11.3%	10.5%	13.2%	13.1%	11.6%	11.6%	12.2%	12.3%

Between women and men: chi-square=0.218, p=NS; ^bchi-square=0.109, p=NS; ^cchi-square=0.053, p=NS; ^dchi-square=0.032, p=NS; ^echi-square=0.032, p=NS.

A multivariate regression logistic analysis, including in the model major CV risk factors (hypertension, dyslipidemia, diabetes mellitus) and subgroups by age (<60, 60–69, 70–79, >80 years), did not find any difference in the temporal distribution, either monthly/seasonal or weekly, of events between women and men.

Discussion

Gender does not seem to influence the seasonal or weekly preference in the occurrence of CV events, with winter–autumn and Monday confirmed to be higher risk periods. As for the seasonality of CV diseases, this is not surprising, as several factors, for example, variations in blood pressure (BP), lipids, endothelial function, markers of inflammation, and endogenous factors, may play a role,²³ but with no significant differences among men and women. Arterial BP rises in winter in both sexes, with increases greater in older than in younger subjects.²⁴ Winter peaks in plasma total cholesterol and low-density lipoprotein cholesterol (LDL-C), have been reported as well,²⁵ and this pattern is independent of gender, body mass index (BMI), overall diet, or physical activity.²⁶ Seasonal variations in endothelial function and markers of inflammation have been found, with no differences between men and women. In the large population (n=1,973, 53% women) of the Framingham Offspring Cohort, brachial flow-mediated vasodilatation (FMD) was lowest in the winter,²⁷ and fibrinogen exhibited significant winter peaks, with changes ranging from 30 mg/dL to 70 mg/dL in different studies.^28
–30 The seasonal variation in risk factors, however, contributes less to the female gender, as the estimated increase in score risk is 6.8% in men and 3.6% in women.³¹ For endogenous rhythms, which are difficult to investigate because of the potentially masking effects imposed by environmental and life habits but are potentially capable of playing a role in the seasonal variation of CV diseases, no significant seasonal differences have been reported between women and men as they have for endocrine and metabolic functions correlated with CV functions.³²

Day-of-week and CV risk

Day-of-week distribution of CV events shows a higher frequency peak on Monday irrespective of gender. As for day-of-week occurrence of myocardial infarction, Willich et al.³³ reported a significant increase in relative risk of disease onset on Monday in the working versus nonworking population. A couple of years later, Spielberg et al.³⁴ did not confirm differences for working and nonworking subjects, that all showed a Monday peak. As for gender, one study in Japan reported a Monday peak in workingmen but not in women, who exhibited a peak on Saturday.³⁵ Thus, a weekend stressful burden for women was hypothesized by the authors. A Monday peak, irrespective of age and gender, also has been reported for cardiac arrest.³⁶ A meta-analysis investigated the excess risk associated with the Monday peak in CV mortality and found an increased pooled odds ratio (OR) of 1.19, with no significant differences between subgroups by gender and age.³⁷ Stress from commencing the weekly working activity, higher BP levels, and unfavorable biochemical status have been proposed as potential triggering factors.^38,39 In the present ICD-9 code-based study, we did not have any information about working activity, and this represents a limitation. As we did not find differences in subgroups by age (and age may be correlated with working activity), however, it can be presumed that the Monday preference is independent of working activity. Regarding risk factors, in a previous study from our group, the higher Monday frequency of stroke was independent of the presence of arterial hypertension, diabetes mellitus, and dyslipidemia,⁴⁰ and the present results provide further confirmation for total CV events also.

Conclusions

At least for the time of acute onset, there are no differences among men and women, and to the best of our knowledge, this the first study to investigate such a hypothesis. This is not surprising, as no significant temporal variations by gender have been reported for classic CV risk factors. However, the study of biologic clocks and human rhythms is a novel, greatly expanding field of research. Recent original research opened the possibility that a molecular mechanism intrinsic to the cardiomyocyte, such as the circadian clock, and its alterations may contribute to the temporal variation of CV disease.⁴¹ At present, we do not know of gender differences in the circadian molecular mechanisms in humans. Further studies are needed to explore the intriguing possible relationship among biologic rhythms, CV disease, and gender.

Footnotes

Acknowledgments

We thank Dr Franco Guerzoni and Dr Nicola Napoli, Health Statistics Unit, Azienda Ospedaliera-Universitaria, Ferrara, Italy, for their valuable support. This work was supported in part by an institutional research grant from the University of Ferrara (Fondo Ateneo Ricerca).

Disclosure Statement

There are no commercial associations or competing financial interests or competing interests, actual or potential, for any of the authors.

References

Rosamond

, Flegal

, Furie

et al. Heart disease and stroke statistics—2008 update: A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation, 2008; 117:e25–e146.

Mosca

, Jones

, King

et al. Awareness, perception, and knowledge of heart disease risk and prevention among women in the United States. American Heart Association Women's Heart Disease and Stroke Campaign task Force. Arch Fam Med, 2000; 9:506–515.

Wheeler

, Bowen

, Maynard

et al. Women veterans and outcomes after acute myocardial infarction. J Womens Health, 2009; 18:613–618.

Knauft

, Chhabra

, McCullough

. Emergency department arrival times, treatment, and functional recovery in women with acute ischemic stroke. J Womens Health, 2010; 19:681–688.

Borrero

, Aujesky

, Stone

, Geng

, Fine

, Ibrahim

. Gender differences in 30-day mortality for patients hospitalized with acute pulmonary embolism. J Womens Health, 2007; 16:1165–1170.

Muller

, Stone

, Turi

et al. Circadian variation in the frequency of onset of acute myocardial infarction. N Engl J Med, 1985; 313:1315–1322.

Manfredini

, Boari

, Bressan

et al. Influence of circadian rhythm on mortality after myocardial infarction: Data from a prospective cohort of emergency calls. Am J Emerg Med, 2004; 22:555–559.

Spencer

, Goldberg

, Becker

, Gore

. Seasonal distribution of acute myocardial infarction in the Second Registry of Myocardial Infarction. J Am Coll Cardiol, 1998; 31:1226–1233.

Casetta

, Granieri

, Portaluppi

, Manfredini

. Circadian variability in hemorrhagic stroke. JAMA, 2002; 287:1266–1267.

10.

Gallerani

, Manfredini

, Ricci

et al. Chronobiological aspects of acute cerebrovascular diseases. Acta Neurol Scand, 1993; 87:482–487.

11.

Manfredini

, Portaluppi

, Zamboni

, Salmi

, Gallerani

. Circadian variation in spontaneous rupture of abdominal aorta. Lancet, 1999; 353:643–644.

12.

Mehta

, Manfredini

, Hassan

et al. Chronobiological patterns of acute aortic dissection. Circulation, 2002; 106:1110–1115.

13.

Gallerani

, Manfredini

, Ricci

et al. Sudden death from pulmonary thromboembolism: Chronobiological aspects. Eur Heart J, 1992; 13:661–665.

14.

Gnecchi-Ruscone

, Piccaluga

, Guzzetti

, Contini

, Montano

, Nicolis

. Mornings and Monday: Critical periods for the onset of acute myocardial infarction. Eur Heart J, 1994; 15:882–887.

15.

Manfredini

, Casetta

, Paolino

et al. Monday preference in onset of ischemic stroke. Am J Med, 2001; 111:401–403.

16.

Manfredini

, Manfredini

, Boari

et al. Seasonal and weekly patterns of hospital admissions for nonfatal and fatal myocardial infarction. Am J Emerg Med, 2009; 27:1096–1102.

17.

Manfredini

, Manfredini

, Boari

et al. Temporal patterns of hospital admissions for transient ischemic attack. A retrospective population-based study in the Emilia-Romagna region of Italy. Clin Appl Thromb Hemost, 2010; 16:153–160.

18.

Manfredini

, Boari

, Manfredini

et al. Seasonal variation in occurrence of aortic diseases: The database of hospital discharge data of the Emilia-Romagna region, Italy. J Thorac Cardiovasc Surg, 2008; 135:442–444.

19.

Gallerani

, Boari

, Smolensky

et al. Seasonal variation in occurrence of pulmonary embolism: Analysis of the database of the Emilia-Romagna region, Italy. Chronobiol Int, 2007; 24:143–160.

20.

Manfredini

, Manfredini

, Malagoni

, Salmi

, Boari

, Gallerani

. Day-of-week distribution of fatal and nonfatal stroke in elderly subjects. J Am Geriatr Soc, 2009; 57:1511–1513.

21.

Manfredini

, Boari

, Salmi

et al.

Day-of-week variability in the occurrence and outcome of aortic diseases: Does it exist?

Am J Emerg Med, 2008; 26:363–366.

22.

Mojòn

, Fernandez

, Hermida

. Chronolab: An interactive software package for chronobiologic time series analysis written for the Macintosh Computer. Chronobiol Int, 1992; 9:403–412.

23.

Manfredini

, Manfredini

, Malagoni

et al.

Chronobiology of vascular disorders: A “seasonal” link between arterial and venous thrombotic diseases?

J Coagul Disord, 2010; 2:61–67.

24.

Brennan

, Greenberg

, Miall

, Thompson

. Seasonal variation in arterial blood pressure. BMJ, 1982; 285:919–923.

25.

Gordon

, Hyde

, Trast

. Cyclic seasonal variation in plasma lipids and lipoprotein levels. The Lipid Research Clinics Coronary Primary Prevention Trial Placebo Group. J Clin Epidemiol, 1988; 41:679–689.

26.

Bluher

, Hentschel

, Rassoul

, Richter

. Influence of dietary intake and physical activity on annual rhythm of human blood cholesterol concentrations. Chronobiol Int, 2001; 18:541–557.

27.

Widlansky

, Vita

, Keyes

et al.

relation of season and temperature to endothelium-dependent flow-mediated vasodilation in subjects without clinical evidence of cardiovascular disease (from the Framingham Heart Study)

Am J Cardiol, 2007; 100:518–523.

28.

Stout

, Crawford

. Seasonal variation in fibrinogen concentration among elderly people. Lancet, 1991; 338:9–12.

29.

Van der Bom

, de Maat

MPM

, Bots

, Hofman

, Kluft

, Grobbee

. Seasonal variation in fibrinogen in the Rotterdam study. Thromb Haemost, 1997; 78:1059–1062.

30.

Woodhouse

, Khaw

, Plummer

, Foley

, Meade

. Seasonal variations of plasma fibrinogen and factor VII activity in the elderly: Winter infections and death from cardiovascular disease. Lancet, 1994; 343:435–439.

31.

Ulmer

, Kelleher

, Diem

, Concin

, Ruttmann

. Estimation of seasonal variation in risk factor profiles and mortality from coronary artery disease. Wien Klin Wochenschr, 2004; 116:662–668.

32.

Nicolau

, Haus

. Chronobiology of endocrine system. Endocrinologie, 1989; 27:153–183.

33.

Willich

, Lowel

, Lewis

, Hormann

, Arntz

, Keil

. Weekly variation of acute myocardial infarction. Increased Monday risk in the working population. Circulation, 1994; 90:87–93.

34.

Spielberg

, Falhenhahn

, Willich

, Wegscheider

, Voller

. Circadian, day-of-week, and seasonal variability in myocardial infarction: Comparison between working and retired patients. Am Heart J, 1996; 132:579–585.

35.

Kinjo

, Sato

et al. Variation during the week in the incidence of acute myocardial infarction: Increased risk for Japanese women on Saturdays. Heart, 2003; 89:398–403.

36.

Gruska

, Gaul

, Winkler

et al. Increased occurrence of out-of-hospital cardiac arrest on Mondays in a community-based study. Chronobiol Int, 2005; 22:107–120.

37.

Witte

, Grobbee

, Bots

, Hoes

. Excess cardiac mortality on Monday: The importance of gender, age and hospitalization. Eur J Epidemiol, 2005; 20:395–399.

38.

Murakami

, Otsuka

, Kubo

et al. Repeated ambulatory monitoring reveals a Monday morning surge in blood pressure in a community-dwelling population. Am J Hypertens, 2004; 17:1179–1183.

39.

Urdal

, Anderssen

, Holme

et al. Monday and non-Monday concentrations of lifestyle-related blood components in the Oslo Diet and Exercise Study. J Intern Med, 1998; 244:507–513.

40.

Manfredini

, Manfredini

, Boari

, Salmi

, Gallerani

. The Monday peak in the onset of ischemic stroke is independent of major risk factors. Am J Emerg Med, 2009; 27:244–246.

41.

Durgan

, Young

. The cardiomyocyte circadian clock: Emerging roles in health and disease. Circ Res, 2009; 106:546–550.