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Circadian variations in the occurrence of first‐ever intracerebral hemorrhage from different sources of income: a hospital‐based cross‐sectional study

Abstract

Background

The onset time of intracerebral hemorrhage (ICH) may be closely related to the working style and living habits of people, which are determined by different income sources in China. Therefore, the purpose of our study was to investigate the Circadian Variations in the occurrence of ICH from different sources of income.

Methods

This retrospective study enrolled 4,327 patients with first-ever ICH. Based on the time of day at which the patients developed symptoms, the classifiable onset time was assigned to one of eight three-hour intervals. And based on different income sources, they were categorized into three groups: Farmers, Wage-earners, and Freelancers. Demographic and risk factors of patients were then summarized, and the circadian variation of the 3 groups of patients’ known time of onset and those stratified by sex and age were analyzed.

Results

The frequency of ICH onset exhibited significant circadian variation among the 3 income groups, demonstrating a bimodal distribution in the daytime, with a nadir during the night (all P < 0.001). Three groups showed a significant initial peak between 06:01 and 09:00, and the same peak was observed in their subgroups of sex and age. In the 3 income source groups, there was a smaller second peak that between 15:01 and 18:00 for Farmers and Wage-earners and 18:01 and 21:00 for Freelancers. After stratification by sex and age, the second peak was between 18:01 and 21:00 for female in Farmers, female in Freelancers, under 65 years of age in Wage-earners and 65 years or older in Freelancers, while 15:01 and 18:00 for the other groups.

Conclusions

Different circadian variations of ICH onset time are found in patients with different income sources in southwest China’s Chongqing Municipality cohort. Moreover, the frequency and distribution pattern of peak hours may be closely related to the working style and living habits of people with different income sources.

Peer Review reports

Background

Primary intracerebral hemorrhage (ICH) is a common, devastating disease that lacks an effective treatment and is associated with a high rate of death or disability [1,2,3,4]. Evidence from epidemiological studies on worldwide stroke victims have indicated that the overall incidence of ICH had been roughly stable at 24.6 per 100,000 person-years, yet the rate has increased to 51.8 in Asian populations [3]. Moreover, the National Epidemiology Survey of Stroke in China (NESS-China) reported that 66.2 out of every 100,000 people suffered from ICH in 2012 to 2013, and the greatest stroke burden was observed in rural areas of northern regions [5].

For nearly 30 years, circadian variation of ICH has been widely investigated around the world [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22] (Table 1). Nevertheless, the findings in many community- and hospital-based studies were variable or even contradictory. Numerous studies have demonstrated that ICH mostly first occurred in the morning hours [6,7,8,9,10,11,12,13,14, 20, 21], and some of which were accompanied by a second peak [6,7,8,9, 20]. Several researchers have also found the peak occurs between morning and afternoon [17, 22] or after awakening [18, 19]. Furthermore, two additional studies with 527 patients in Asia reported that a single peak was observed in the afternoon hours [15, 16]. In these studies, circadian variation of ICH incidence may be dependent on the methodology used. They divided a day into 2-[6,7,8,9, 15, 21], 3-[20], 4-[11], 6-[10, 12,13,14, 16], 8-hour intervals [17, 22] or more [18, 19]. The different findings concerning the time intervals of statistically significant ICH incidence were identified as: a morning peak (06:00–08:00 h [6], 08:00–10:00 h [7,8], 08:00–12:00 h [11], 10:00–12:00 h [9], 06:00–12:00 h [10,12-14]), sometimes with a second peak (16:00–18:00 h [7] and 18:00–20:00 h [6, 8-9]), or a single peak during the afternoon (18:00–20:00 h [15], 12:00–18:00 [16]), or a single peak from morning to afternoon (08:00–16:00 h [17]), or a peak after awakening [18, 19]. Not only the methodology, the exact peak circadian variation of ICH incidence may also be dependent on sex, age, and living habits [15, 20].

Table 1 Available evidence on patterns of onset of ICH for nearly 30 years

China is a country with a large agricultural industry, the population of farmers numbers nearly 600 million. Chongqing is a province located in the southwest of China, and the two centers involved in this study, University-Town Hospital of Chongqing Medical University and Chongqing University Three Gorges Hospital, are located in the west and east of Chongqing respectively. A report from the National Bureau of Statistics shows that as of 2019, Chongqing has a permanent population of more than 30 million, of which non-urban residents account for about 40 %. The composition of Chongqing’s labor force is still dominated by the primary industry such as planting, forestry and animal husbandry, which accounts for more than 40 %, followed by the secondary industry dominated by processing and manufacturing, which accounts for about 20 %, and finally the tertiary industry dominated by commerce and service industry, which accounts for more than 30 %. Farmers’ income mainly comes from agricultural production, while for urban population, their main sources of income are wages and non-fixed income. People with different income sources differ in working style and living habits, and these factors may be related to the circadian variations in the occurrence of ICH.

Therefore, our aim was to investigate circadian variations in the occurrence of ICH from different sources of income, as well as observe whether the onset time of ICH was affected by the working style and living habits.

Methods

Study population

This cross-sectional study was approved by the ethics committee of University-Town Hospital of Chongqing Medical University. We screened 4,327 patients with ICH from databases of University-Town Hospital of Chongqing Medical University and Chongqing University Three Gorges Hospital (including Neurological Department, Neurosurgery, First aid Branch, Baian Branch and Yuan Branch) from January 1, 2012 to December 31, 2017. All patients underwent complete cranial computed tomography (CT) scans and/or magnetic resonance imaging (MRI) within 24 h after onset of symptoms. The diagnosis of ICH was confirmed by local neurologists and/or neurosurgeons based on the standards of the literature [23]. We exclude cases involving traumatic ICH, subarachnoid hemorrhages (SAH), those with a history of ICH, and case where exact hours or approximate time intervals of onset could not be determined. All the variables including sex, age, occupation, income source, date and hour of onset, situation at onset, health-related behaviors, and past medical history were recorded. We divided the patients into three groups based on the different income sources: i.e. Farmers (whose income mainly came from agricultural production), Wage-earners (whose income mainly came from wages) and Freelancers (whose income were unfixed).

Data analysis

Based on the data provided by the medical records, the classifiable onset time was assigned to one of eight three-hour intervals: 00:01–03:00; 03:01–06:00; 06:01–09:00; 09:01–12:00; 12:01–15:00; 15:01–18:00; 18:01–21:00; 21:01–24:00 h. Distributions of ICH onset in the 8 onset-time partitions were analyzed in the overall population, in the 3 income source groups and subgroups of income sources further stratified by sex and age. To examine the effect of age on the time of onset, the patients were stratified into < 65 and ≥ 65 years. The risk factors of ICH in different income source groups were assessed as follows. Hypertension(HTN)was defined as those with an average (calculated from three measurements) systolic blood pressure (SBP) ≥ 140 mmHg and/or an average diastolic blood pressure (DBP) ≥ 90 mmHg, a history of hypertension, or prior use of antihypertensive agents. However, cases of prehypertension (PHT, representing a SBP of 120–139 mmHg and/or a DBP of 80–89 mmHg) were not included [24]. Dyslipidemia was defined as to be those with total cholesterol (TC) ≥ 5.2 mmol/L, triglyceride (TG) ≥ 1.7 mmol/L, low-density lipoprotein cholesterol (LDL-C) ≥ 3.4 mmol/L [25], or the current use of anti - lipidemic medication. Diabetes mellitus (DM) was defined to be the cases with a history of DM or fasting blood glucose (FPG) level ≥ 7.0 mmol/L [26]. The variable Alcohol Consumption is defined to be those that self-reported consuming alcohol more than three times per week. Smoking was defined as current regular use (any amount).

Statistical analyses

All analyses were carried out with SPSS version 24.0 software (IBM Inc., Amonk, NY). Mean ages were expressed as mean (SD), while other variables including demographics and risk factors, were presented as values or percentages. Analysis of variance was used for mean age comparison among the 3 income source groups, and demographics and risk factors were compared by χ2 test. χ2 test was used to compare the distribution of ICH by 3-h time intervals among them. In addition, to screen out confounders, stratified analyses were used to assess the contribution of demographic and risk factors to the temporal distributions of ICH in the overall population. Variables used were age, sex, hypertension, dyslipidemia, diabetes mellitus, alcohol consumption, and smoking. Assuming that time of day has no effect on the onset of ICH, we should observe a roughly uniform distribution. Data were statistically analyzed with χ2 test for goodness of fit to the model of equal distribution of ICH to evaluate the circadian variations in ICH onset. To estimate the relative risks (RR) with 95 % confidence intervals (CI) of ICH occurring at specific time periods respectively, the observed number of ICH was compared with the average number of 8 3-h intervals. RR adjusted for confounders by cox regression analysis. P < 0.05 was considered significant.

Results

Patient characteristics

4,150 patients with known onset time were recruited in our study. Among them, 2,126 were Farmers, 1,194 were Wage-earners and 830 were Freelancers. The remaining 177 patients with no identifiable times of onset were excluded from further analysis. Table 2 shows the demographics and risk factors of all patients afflicted with their first ICH in Farmers, Wage-earners, and Freelancers. There were significant differences in demographics and risk factors except dyslipidemia among the 3 income source groups (all P < 0.05). There were also significant differences in the temporal distributions of ICH among the 3 income source groups. After stratified by the demographics and risk factors in the overall population, the differences in the temporal distributions of ICH were observed in age, sex, and hypertension strata (all P < 0.05), whereas there were no significant differences in the other risk factor strata.

Table 2 Demographics and risk factors of the patients with ICH

Income sources and circadian variations

In the 3 income groups and their subgroups stratified by age and sex, the circadian variations of onset time were bimodal in all cases; there was one significant initial peak during the period of 06:01–09:00, a smaller second peak during 15:01–21:00, and a nadir during the night (all P < 0.001). The smaller second peak for each group, however, was different; In the 3 income source groups, the smaller second peak was 15:01–18:00 for Farmers and Wage-earners, and 18:01–21:00 for Freelancers (Fig. 1). In the sex subgroups, all showed a second peak between 15:01 and 18:00 except female in Farmers and female in Freelancers, whose second peak was between 18:01 and 21:00 (Fig. 2). In the age subgroups, the second peak was 18:01–21:00 for the group under 65 years of age in Wage-earners and 65 years or older in Freelancers, while 15:01 and 18:00 for the other groups (Fig. 3). RR values for each peak period were statistically significant after adjustment for age, sex, and hypertension (Table 3).

Table 3 The peak time of ICH and relative risk values
Fig. 1
figure1

Time-specific onset percentage in patients with ICH stratified by sources of income

Fig. 2
figure2

Time-specific onset percentage in patients with ICH stratified by sex

Fig. 3
figure3

Time-specific onset percentage in patients with ICH stratified by age

Discussion

We investigated the circadian patterns of more than 4,000 patients with ICH from different income sources. To our knowledge, this is the largest study of circadian variation of onset time of ICH available in the literature so far. Our research found there were different circadian variations of ICH onset time in patients with different income sources, that may be related to their different working style and living habits.

The present study showed a significant morning peak between 06:01 and 09:00 in 3 different income source groups as well as in those subgroups further stratified by sex and age. This early morning peak was similar to the finding by Omama et al. [6] and consistent with the striking rise of blood pressure (BP) for 2 to 3 h after awakening [27]. The aforementioned papers observed that it is more common that ICH attacks in the morning hours than any other time of the day. They attribute this to a complex interaction of many factors, with the result that a sudden increase in BP emerging as an explanation for this pattern of onset [6,7,8,9,10,11,12,13,14, 19,20,21,22]. Metoki et al. [28] found that the risk of hemorrhagic stroke would be increased only when the morning mean SBP soared by more than one-fifth (≥ 25 mmHg), regardless of nocturnal blood pressure patterns. The risk of ICH was also exacerbated by extreme drops in SBP (≥ 20 mmHg nocturnal decline in SBP from the diurnal level). Some authors have comprehensively analyzed the endogenous and exogenous factors which influence morning BP surge [29, 30]. A report studied by Morris et al. that observed 14 healthy, sedentary, nonsmoking, nonobese, and normotensive male (aged 19–50 years) also indicated that the morning BP increase was attenuated during bed rest, suggesting that the adoption of an upright posture and/or physical activity in the morning contributes to the morning BP surge [31]. Furthermore, racial differences may also affect morning BP surge [32]. In the existing literature, alcohol intake has been identified as a significant risk factor for ICH. It was reported that people who had heavy alcohol intake (alcohol intake ≥ 46 g/d) had higher BP in the morning and increased the risk of stroke, especially hemorrhagic stroke [33, 34]. In our findings, we observed that the Wage-Earners group had the highest proportions of heavy drinkers, smokers, dyslipidemia, and DM, but the morning peak was in accord with the others. It seems that none of the common risk factors correlates alone with the morning BP surge of ICH occurrence but it may have a closer tie with physical and/or emotional activity after waking.

Research revealed that nearly 40 % of Chinese residents tend to take a nap during the afternoon [35]. Spengos et al. considered the close relationship between the late afternoon peak of onset of ICH and afternoon sleep, the siesta [7]. Wage-earners and Farmers have relatively fixed work schedules, and most of them also have a habit of taking a siesta. This may account for the pronounced afternoon peak. On the contrary, Freelancers usually didn’t have a fixed schedule and the habit of taking a siesta due to their irregular working hours, that could explain why they didn’t display a same peak distribution pattern in the afternoon as Wage-earners and Farmers.

When analyses were performed by dividing the patients according to sex and age, there were partial differences in the afternoon peak, which was consistent with the findings of Inagawa T et al. [8, 15]. Combined with the traditional customs and contemporary national conditions of China, we considered that this may be related to the different labor conditions of different groups, family role positioning and so on. Liu et al. found that SBP increased significantly in the later half of long working hours, indicating that the risk of ICH may be related to intensity and time of working [36, 37]. Female in Farmers and Freelancers mainly take the responsibilities of household chores as a traditional role in the family, including buying groceries and cooking, etc. They may continue perform housework after finishing work in the afternoon [38]. This could explain their early evening peak. Female in Wage-earners may be less traditional about their role in the home, seeing themselves as breadwinners and working like male [39]. They may be consequently less likely to be a homemaker after working hours. This can contribute to the reason for both male and female in Wage-earners exhibited the same peak distribution pattern in the afternoon. We found no difference in the afternoon peak among farmers of different age groups, which may be related to the same working pattern whether they are young or old [40]. However, for Wage-earners 65 years or older, they are generally retired and do not work [41], this may be why they are different from Wage-earners younger than 65 years in the afternoon peak time.

At present, no study has been found on the relationship between circadian variations of ICH onset and working style and living habits of population groups. This study is the first to explore whether working style and living habits have effect on ICH onset time by analyzing circadian variations of ICH onset from different sources of income. Our study demonstrated significant differences in the circadian variations of ICH onset from different sources of income population groups. Not only does this finding provide evidence of working style and living habits can influence circadian variations of ICH onset, but also provides the possibility for further research on how the work-life rhythm affect the circadian variations of ICH onset. In addition, it can help us to provide individualized management strategies for patients with ICH.

Our study has certain limitations apart from the inherent limitation of the retrospective design. First, we only analyzed data on patients admitted to hospitals and did not do a population based study. Therefore, patients who may be missed including those who died before admission and those who did not visit the medical department. These missing patients cannot be grouped accurately because they could belong to any group. However, from experience, we can probably infer that most of them may belong to farmers, the reason may be that farmers’ low socioeconomic status leads to insufficient awareness of disease and lack of economic ability. We believe that this situation may underestimate the incidence of ICH in a specific population, but has little effect on the circadian variation of ICH onset. Population-based studies may better present conclusions that are in line with local populations, but due to the lack of local databases, population-based studies cannot be done at present. The establishment of the corresponding database to make up for these deficiencies is what we expect to be able to complete in the future. In addition, the factors affecting socioeconomic status are very complex and specific in China, so no consensus or criteria for socioeconomic status is available so far. The information about income of patients is not collected routinely by the hospital. Therefore, factors related to socioeconomic status can not be analyzed in deepth in our study. Furthermore, according to the existing data, patients were divided into 3 groups by sources of income: Farmers, Wage-earners, and Freelancers. However, patients in Wage-earners and Freelancers might have some differences in working style due to the different nature of the specific work, especially for freelancers. For example, night shift workers may be classified in the Wage-earners group, and there may also be patients with regular work-life rhythm in Freelancers, therefore, the simple grouping based on the data at hand might not reflect their actual working style. Finally, the living habits of the 3 groups of patients we mentioned was based on the general rules of the local population, but individual differences may exist, such as the habit of napping, the role in the home and the time to get up in the morning. This was an inevitable limitation of our study. The latter two limitations can make the results of our study mainly represented patients in the 3 groups who conformed to general rules, rather than all patients.

Conclusions

Our study is the first to demonstrate the circadian variations of ICH onset time in patients with different income sources in southwest China’s Chongqing Municipality cohort. These appearances are in accordance with the daily life characteristics of the local people and the routines among the people of different income sources. Although one or more peak hours can be found in ICH onset time in a day, the frequency and distribution pattern of peak hours were closely related to the working style and living habits of people with different income sources. Therefore, we speculate that the work-life rhythm may be a very important factor affecting the onset time of ICH, this may provide the basis for a larger scope of investigation in the future, and provide the possibility for further research on how the work-life rhythm affect the onset time of ICH specific mechanism. Meanwhile, recognition of these particular circadian patterns in ICH is important in planning preventive and control strategies for the sudden, catastrophic cerebrovascular events.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon request.

Abbreviations

ICH:

Intracerebral hemorrhage

NESS-China:

National Epidemiology Survey of Stroke in China

CT:

Computed tomography

MRI:

Magnetic resonance imaging

SAH:

Subarachnoid hemorrhages

HTN:

Hypertension

SBP:

Systolic pressure

DBP:

Diastolic blood pressure

PHT:

Prehypertension

TC:

Total cholesterol

TG:

Triglyceride

LDL-C:

Low-density lipoprotein cholesterol

DM:

Diabetes mellitus

FPG:

Fasting blood glucose

RR:

Relative risks

CI:

Confidence intervals

BP:

Blood pressure

References

  1. 1.

    Craen A, Mangal R, Stead TG, Ganti L. Gender differences in outcomes after non-traumatic intracerebral hemorrhage. Cureus. 2019;11(10):e5818.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Chen Y, Wright N, Guo Y, Turnbull I, Kartsonaki C, Yang L, China Kadoorie Biobank Collaborative Group., et al. Mortality and recurrent vascular events after first incident stroke: a 9-year community-based study of 0·5 million Chinese adults. Lancet Glob Health. 2020;8(4):e580-90.

    Article  Google Scholar 

  3. 3.

    van Asch CJ, Luitse MJ, Rinkel GJ, van der Tweel I, Algra A, Klijn CJ. Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis. Lancet Neurol. 2010;9(2):167–76.

    Article  Google Scholar 

  4. 4.

    Feigin VL, Lawes CM, Bennett DA, Barker-Collo SL, Parag V. Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review. Lancet Neurol. 2009;8(4):355–69.

    Article  Google Scholar 

  5. 5.

    Wang W, Jiang B, Sun H, Ru X, Sun D, Wang L, NESS-China Investigators., et al. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults. Circulation. 2017;135(8):759–71.

    Article  Google Scholar 

  6. 6.

    Omama S, Yoshida Y, Ogawa A, Onoda T, Okayama A. Differences in circadian variation of cerebral infarction, intracerebral haemorrhage and subarachnoid haemorrhage by situation at onset. J Neurol Neurosurg Psychiatry. 2006;77(12):1345–9.

    CAS  Article  Google Scholar 

  7. 7.

    Spengos K, Vemmos KN, Tsivgoulis G, Synetos A, Zakopoulos NA, Zis V, et al. Two-peak temporal distribution of stroke onset in Greek patients. a hospital-based study. Cerebrovasc Dis. 2003;15(1–2):70–7.

    Article  Google Scholar 

  8. 8.

    Inagawa T, Takechi A, Yahara K, Saito J, Moritake K, Kobayashi S, et al. Primary intracerebral and aneurysmal subarachnoid hemorrhage in Izumo City, Japan. Part I: incidence and seasonal and diurnal variations. J Neurosurg. 2000;93(6):958–66.

    CAS  Article  Google Scholar 

  9. 9.

    Sloan MA, Price TR, Foulkes MA, Marler JR, Mohr JP, Hier DB, et al. Circadian rhythmicity of stroke onset. Intracerebral and subarachnoid hemorrhage. Stroke. 1992;23(10):1420–6.

    CAS  Article  Google Scholar 

  10. 10.

    Fodor DM, Babiciu I, Perju-Dumbrava L. Circadian variation of stroke onset: a hospital-based study. Clujul Med. 2014;87(4):242–9.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Butt MU, Zakaria M, Hussain HM. Circadian pattern of onset of ischaemic and haemorrhagic strokes, and their relation to sleep/wake cycle. J Pak Med Assoc. 2009;59(3):129–32.

    PubMed  Google Scholar 

  12. 12.

    Passero S, Reale F, Ciacci G, Zei E. Differing temporal patterns of onset in subgroups of patients with intracerebral hemorrhage. Stroke. 2000;31(7):1538–44.

    CAS  Article  Google Scholar 

  13. 13.

    Gallerani M, Trappella G, Manfredini R, Pasin M, Napolitano M, Migliore A. Acute intracerebral haemorrhage: circadian and circannual patterns of onset. Acta Neurol Scand. 1994;89(4):280–6.

    CAS  Article  Google Scholar 

  14. 14.

    Ricci S, Celani MG, Vitali R, La Rosa F, Righetti E, Duca E. Diurnal and seasonal variations in the occurrence of stroke: a community-based study. Neuroepidemiology. 1992;11(2):59–64.

    CAS  Article  Google Scholar 

  15. 15.

    Inagawa T. Diurnal and seasonal variations in the onset of primary intracerebral hemorrhage in individuals living in Izumo City, Japan. J Neurosurg. 2003;98(2):326–36.

    Article  Google Scholar 

  16. 16.

    Cheung RT, Mak W, Chan KH. Circadian variation of stroke onset in Hong Kong Chinese: a hospital-based study. Cerebrovasc Dis. 2001;12(1):1–6.

    CAS  Article  Google Scholar 

  17. 17.

    Nyquist PA, Brown RD Jr, Wiebers DO, Crowson CS, O’Fallon WM. Circadian and seasonal occurrence of subarachnoid and intracerebral hemorrhage. Neurology. 2001;56(2):190–3.

    CAS  Article  Google Scholar 

  18. 18.

    Turin TC, Kita Y, Rumana N, Nakamura Y, Takashima N, Ichikawa M, et al. Wake-up stroke: incidence, risk factors and outcome of acute stroke during sleep in a Japanese population. Takashima Stroke Registry 1988–2003. Eur Neurol. 2013;69(6):354–9.

    Article  Google Scholar 

  19. 19.

    Nagakane Y, Miyashita K, Nagatsuka K, Yamawaki T, Naritomi H. Primary intracerebral hemorrhage during asleep period. Am J Hypertens. 2006;19(4):403–6.

    Article  Google Scholar 

  20. 20.

    Kocer A, Ilhan A, Ince N, Bilge C. The related causes in very early morning onset of stroke. Prog Neuropsychopharmacol Biol Psychiatry. 2005;29(6):983–8.

    Article  Google Scholar 

  21. 21.

    Wroe SJ, Sandercock P, Bamford J, Dennis M, Slattery J, Warlow C. Diurnal variation in incidence of stroke: Oxfordshire community stroke project. BMJ. 1992;304(6820):155–7.

    CAS  Article  Google Scholar 

  22. 22.

    Kelly-Hayes M, Wolf PA, Kase CS, Brand FN, McGuirk JM, D’Agostino RB. Temporal patterns of stroke onset. Framingham Study Stroke. 1995;26(8):1343–7.

    CAS  Article  Google Scholar 

  23. 23.

    Sacco RL, Kasner SE, Broderick JP, Caplan LR, Connors JJ, Culebras A, et al. An updated definition of stroke for the 21st century: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2013;44(7):2064–89.

    Article  Google Scholar 

  24. 24.

    Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA. 2003;289(19):2560–72.

    CAS  Article  Google Scholar 

  25. 25.

    Li J, Bao W, Zhang T, Zhou Y, Yang H, Jia H, et al. Independent relationship between serum ferritin levels and dyslipidemia in Chinese adults: A population study. PLoS One. 2017;12(12):e0190310.

    Article  Google Scholar 

  26. 26.

    Diabetes Canada Clinical Practice Guidelines Expert Committee, Punthakee Z, Goldenberg R, Katz P. Definition, classification and diagnosis of diabetes, prediabetes and metabolic syndrome. Can J Diab. 2018;42(Suppl 1):10–5.

    Google Scholar 

  27. 27.

    Hermida RC, Fernández JR, Ayala DE, Mojón A, Alonso I, Smolensky M. Circadian rhythm of double (rate-pressure) product in healthy normotensive young subjects. Chronobiol Int. 2001;18(3):475–89.

    CAS  Article  Google Scholar 

  28. 28.

    Metoki H, Ohkubo T, Kikuya M, Asayama K, Obara T, Hashimoto J, et al. Prognostic significance for stroke of a morning pressor surge and a nocturnal blood pressure decline: the Ohasama study. Hypertension. 2006;47(2):149–54.

    CAS  Article  Google Scholar 

  29. 29.

    Kawano Y. Diurnal blood pressure variation and related behavioral factors. Hypertens Res. 2011;34(3):281–5.

    Article  Google Scholar 

  30. 30.

    Smolensky MH, Hermida RC, Portaluppi F. Circadian mechanisms of 24-hour blood pressure regulation and patterning. Sleep Med Rev. 2017;33:4–16.

    Article  Google Scholar 

  31. 31.

    Morris CJ, Hastings JA, Boyd K, Krainski F, Perhonen MA, Scheer FA, et al. Day/night variability in blood pressure: influence of posture and physical activity. Am J Hypertens. 2013;26(6):822–8.

    Article  Google Scholar 

  32. 32.

    Hoshide S, Kario K, de la Sierra A, Bilo G, Schillaci G, Banegas JR, et al. Ethnic differences in the degree of morning blood pressure surge and in its determinants between Japanese and European hypertensive subjects: data from the ARTEMIS study. Hypertension. 2015;66(4):750–6.

    CAS  Article  Google Scholar 

  33. 33.

    Ohira T, Tanigawa T, Tabata M, Imano H, Kitamura A, Kiyama M, et al. Effects of habitual alcohol intake on ambulatory blood pressure, heart rate, and its variability among Japanese men. Hypertension. 2009;53(1):13–9.

    CAS  Article  Google Scholar 

  34. 34.

    Ohkubo T, Metoki H, Imai Y. Alcohol intake, circadian blood pressure variation, and stroke. Hypertension. 2009;53(1):4–5.

    CAS  Article  Google Scholar 

  35. 35.

    Peng Y, Mei Z, Yi-Chong L, Yong J, Li-Min W, Wen-Hua Z. Analysis on characteristics of sleeping in Chinese population aged 15–69 years. Chin J Prev Contr Chron Dis. 2011;02:152–3.

    Google Scholar 

  36. 36.

    Liu X, Ikeda H, Oyama F, Wakisaka K, Takahashi M. Hemodynamic responses to simulated long working hours with short and long breaks in healthy men. Sci Rep. 2018;28(1):14556.

    Article  Google Scholar 

  37. 37.

    Liu X, Ikeda H, Oyama F, Takahashi M. Haemodynamic responses to simulated long working hours in different age groups. Occup Environ Med. 2019;76(10):754–7.

    Article  Google Scholar 

  38. 38.

    Chunyu Y, Fu H, Li L. The effect of family structure on female labor participation - empirical analysis based on the 2011 China health and retirement longitudinal study. Asian Soc Work Policy Rev. 2016;10(1):21–33.

    Article  Google Scholar 

  39. 39.

    Sin, Leo YM, Oliver HM, Yau. Female role orientation of Chinese women: conceptualization and scale development. Psychol Market. 2004;21(12):1033–58.

    Article  Google Scholar 

  40. 40.

    Jian-wen XIA. Change and positive role of farmer life style in countryside reform of China. Res Agric Modernization. 2006;5:353–5.

  41. 41.

    He J, Luan QM, Xie CG, Du CY, Cheng YR. Quality of life and mental state of those retired. Chin Ment Health J. 2002;16(3):177–8.

    Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

The study was supported by Medical research project of Chongqing Municipal Commission of Health and Family Planning. The funding body had no role in study design, data collection, data analysis, data interpretation, and in writing the manuscript.

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YYW:contributed to the design, interpretation of the data and drafted the manuscript; YYW, EYW and YTP: contributed to the data collection of the study; NY and EYW:performed the statistical analysis; YYW and YTP: contributed to interpreting results, reviewing the manuscript and approved the final version of the manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Yun-Tao Pu.

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Ethical approval for the study, including the permission to access and use the datasets, was obtained from the Institutional Review Board of Chongqing University Three Gorges Hospital and University-Town Hospital of Chongqing Medical University.

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The authors declare that they have no competing interests.

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Wang, YY., Yan, N., Wang, EY. et al. Circadian variations in the occurrence of first‐ever intracerebral hemorrhage from different sources of income: a hospital‐based cross‐sectional study. BMC Neurol 21, 140 (2021). https://doi.org/10.1186/s12883-021-02163-2

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Keywords

  • Intracerebral hemorrhage
  • Circadian variations
  • Income sources
  • Risk factors
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