Blood pressure is a vital physiological characteristic, which is closely related to a multitude of body functions. Sleep patterns can be modified by lifestyle changes or therapeutic interventions, and good understanding of how they fit into blood pressure control can help prevent hypertension and its complications. Indeed, poorly sleep quality has been associated with hypertension and related comorbid states, such as obesity, metabolic syndrome, and mental health problems. These conditions are probably mutually related, although casualty is more difficult to establish. Insufficient or poor quality of sleep could lead to pathophysiological abnormalities linked to hypertension, such as increased sympathetic drive, which could it turn disturb healthy sleep patterns [1]. Nevertheless, sleep patterns vary geographically and historically. In areas with large seasonal fluctuations of daylight duration, a shortened sleep during summer has been linked to higher overall sympathetic drive and elevated blood pressure when compared with longer sleeps during winter. In modern times, this pattern has been modified by introduction of electricity, and multiple new lifestyle factors associated with disrupted sleep throughout the year. These factors include shift work, late time entertainment, television, the internet, and travel across time zones. In fact, over a half of participants in the National Sleep Foundation's 2013 International Bedroom Poll reported insufficient sleep on workdays. Admittedly, these lifestyle changes affect the sleep even in individuals who could afford enough bedtime. Insomnia is common and is associated with higher night-time SBP and blunted day-to-night SBP dipping [2]. The nocturnal awakenings could be compensated by daytime naps and these could be difficult to account for during sleep studies. Whilst there are many classifications of sleep disorder, they would usually manifest by sleep deprivation because of lack of the necessary amount or quality of sleep, disrupted sleep, and events occurring during sleep, such as sleep apnoea or restless legs syndrome. All of them could have detrimental effects on blood pressure. Also, different sleep phases can play specific roles in sustained blood pressure elevation. In this issue of the Journal of Hypertension, the analysis of the Wisconsin Sleep Cohort Study presented demonstrates that hypertension was linked to longitudinal increased decline in rapid eye movement (REM) sleep percentage, and a lesser decline in percentage of time spent in N3 sleep [3]. The findings are not easy to interpret. In fact, previously changes in sleep architecture, such as decreased percentage of time in slow-wave sleep and low non-REM sleep delta power are associated with increase in hypertension incidence in specific population [4]. The blood pressure dipping status is likely to play a role in these processes. N3 sleep (i.e. slow-wave sleep) is believed to be the most restorative type when heart rate, blood pressure, cerebral blood flow, and respiration decrease. During REM sleep, these processes are increased compared with non-REM sleep. The balance of the phases can drive the overall night-time blood pressure reduction. However, the ‘dipping’ status varies from day to day and the assignment of patients to dippers and nondippers is not reproducible over time, similarly to the variation in the sleep quality and duration [5,6]. Of note, nondipping status has been associated with sleep disturbance [7]. Unsurprisingly, nocturnal blood pressure is increasingly considered a better predictor of cardiovascular risk than daytime blood pressure [7,8]. Unfortunately, data on diurnal blood pressure variations were not available for in the Wisconsin Sleep Cohort Study [3]. Arguably the fact that office blood pressure was used for the study analysis could also lead to a misassignment of some people with white-coat hypertension as having essential hypertension. Perceived difficulties with initiation and maintenance of sleep are also important, and both medical and mental health problems play their roles. The Wisconsin Sleep Cohort Study reported that subjective insomnia, presented by difficulty falling asleep, was present in people with hypertension [3]. The clinical significance of this finding is difficult to establish as the overall sleep duration has not been affected and its relationship to clinical outcomes was beyond the scope of the study. Previously, self-reported sleep duration only moderately correlates with actual measured sleep [9]. This may well reflect the recognized phenomena of sleep state misperception when people report shorter than actual sleep duration. For example, anxiety and depression states are related to both sleep disturbance and increased risk of hypertension and its complications [10]. Assessment of mental health was beyond the scope of the Wisconsin Sleep Cohort Study and the choice of study population could also be of relevance as a meta-analysis of studies that assessed subjective sleep quality and blood pressure or hypertension showed that poor sleep quality was significantly associated with a greater likelihood of hypertension and patients with hypertension had significantly worse sleep quality scores with the opposite observed in blood pressure dippers [11]. There are also data indicating that hypertension could contribute towards shortening sleep duration, which, in turn, could further raise blood pressure [12]. Furthermore, the relationship between sleep duration and hypertension could be nonlinear. Whilst the role of sleep deprivation is more recognized, long sleep duration has been associated with hypertension prevalence in some cross-sectional studies but no published longitudinal studies have shown an association between long sleep duration and hypertension incidence [13]. Whilst there is little doubt that sleep health is implicated in pathophysiology of hypertension, the complexity of the relationship makes it difficult to identify specific sleep abnormalities implicated in blood pressure elevation at both epidemiological and individual patient level. This is largely because of lack of adequate tools for prolonged monitoring of sleep. Fortunately, the progress in affordable wearable technologies facilitating telemonitoring for both blood pressure and sleep parameters can provide the new insight in the relationship and help to model more effective interventions with personalized care, and timely feedback from healthcare professionals to direct the needed behavioural changes and treatments. Healthy sleep is vital for wellbeing and its value is not only increasingly appreciated by the public but also by employers and legislators. Future studies are awaited with interest. ACKNOWLEDGEMENTS Conflicts of interest There are no conflicts of interest.
guage, included posters and public information leaflets on AF and pulse rhythm.Pharmacists were instructed to take the pulse manually, assess symptoms and risk factors.Whenever an abnormal heart rate or rhythm was detected, the patient was referred to a physician with a letter containing additional information.Where feasible, the manual pulse check was supplemented by use of a mobile single lead ECG.Results: Ten countries participated, and 3,974 participants were involved in the awareness campaign.For the screening event, a total of 2,573 patients were included in the final analysis.The majority were female (68.9%); mean age approx.65 years.Risk factors identified: hypertension (48.9%), diabetes (19.8%) and peripheral heart disease (15.4%).The least common was having had a stroke, Transient Ischaemic Attack or Thromboembolism, (1.1%).Mean heart rate detected was 72.7bpm.Bradycardia detected in 107 people and tachycardia in 14 people.An irregular pulse was detected in 212 patients (8.3%).AF confirmed in 35 people, a detection rate of 1.4%. Know Your PulseConclusion: Opportunistic screening for AF in people over the age of 65 years is recommended in ESC guidelines on the management of AF.The experience gained from conducting this initiative in various health care settings suggests that community pharmacies may be a good location for identifying undiagnosed people with AF.
HomeStrokeVol. 50, No. 8Using Blood Biomarkers to Identify Atrial Fibrillation–Related Stroke Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBUsing Blood Biomarkers to Identify Atrial Fibrillation–Related StrokeBalancing Simplicity and Practicality Monika Kozieł, MD, PhD, Tatjana S. Potpara, MD, PhD and Gregory Y.H. Lip, MD Monika KoziełMonika Kozieł From the Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, United Kingdom (M.K., G.Y.H.L.) Department of Cardiology, Congenital Heart Diseases and Electrotherapy, Medical University of Silesia, Silesian Centre for Heart Diseases, Zabrze, Poland (M.K., G.Y.H.L.) , Tatjana S. PotparaTatjana S. Potpara School of Medicine, Belgrade University, Serbia (T.S.P., G.Y.H.L.) Cardiology Clinic, Clinical Centre of Serbia, Belgrade (T.S.P., G.Y.H.L.) and Gregory Y.H. LipGregory Y.H. Lip Correspondence to Gregory Y.H. Lip, MD, University of Liverpool, Liverpool, United Kingdom. Email E-mail Address: [email protected] From the Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, United Kingdom (M.K., G.Y.H.L.) Department of Cardiology, Congenital Heart Diseases and Electrotherapy, Medical University of Silesia, Silesian Centre for Heart Diseases, Zabrze, Poland (M.K., G.Y.H.L.) School of Medicine, Belgrade University, Serbia (T.S.P., G.Y.H.L.) Cardiology Clinic, Clinical Centre of Serbia, Belgrade (T.S.P., G.Y.H.L.) Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Denmark, (G.Y.H.L.). Originally published20 Jun 2019https://doi.org/10.1161/STROKEAHA.119.026185Stroke. 2019;50:1956–1957This article is a commentary on the followingBlood Biomarkers of Heart Failure and Hypercoagulation to Identify Atrial Fibrillation–Related StrokeOther version(s) of this articleYou are viewing the most recent version of this article. Previous versions: June 20, 2019: Ahead of Print See related article, p 2223Stroke risk in patients with atrial fibrillation (AF) is heterogenous, being characterized by different stroke risk factors. Given the need to use anticoagulants for stroke prevention, much focus has been on improving risk stratification, to target patients who would benefit the most from thromboprophylaxis.1 The more common and validated risk factors for stroke and bleeding in AF have been used to formulate clinical risk prediction scores.2 With the intention to improve risk prediction, attention has been directed to using biomarkers (biological markers) whether from blood (eg, troponin and natriuretic peptide), urine, or imaging (cardiac or cerebral), while balancing the need for simplicity of use and everyday practical application.3Why the interest in these biomarkers? Numerous biomarkers have been related to stroke and bleeding in AF and have contributed to our understanding of pathophysiological mechanisms involved in complications of AF or to be used as surrogate markers of thrombosis in clinical studies.4 Many proposed biomarkers in AF have provided independent prognostic value in AF and have generally improved risk stratification over an approach based on clinical factors alone.In the current issue of Stroke, Kneihsl et al5 report findings regarding the association between blood biomarkers and AF-related stroke. The investigators used data from one stroke unit and analyzed prospectively all consecutive ischemic stroke patients admitted over 1 year. The authors found that AF-related stroke patients had higher NT-proBNP (N-terminal pro-B-type natriuretic peptide) and D-dimer levels and lower antithrombin III when compared with patients with a noncardiac stroke pathogenesis. Moreover, they observed that NT-proBNP level was independently associated with AF-related stroke on multivariate analysis. Limitations include the small number of patients and single center cohort, as well as baseline one-off sampling to predict a remote event occurring many years later. The lack of laboratory follow-up samples would not adequately account for the dynamic nature of clinical risk.In many biomarker studies, the incremental predictive value of biomarker(s) over clinical factor–based risk scores still remains marginal, although may be statistically significant with large cohorts. Another limitation is the interpatient and intrapatient (and assay) variability, diurnal variation, and influence of concomitant diseases and drug therapies. Moreover, stroke or bleeding risk is a continuum and is not a one-off evaluation at baseline to determine events many years later; indeed, risk is a dynamic (rather than static) process modified by aging, incident risk factors, or changing comorbidities.6Moreover, many biomarkers are nonspecifically associated with multiple outcomes, cardiovascular and noncardiovascular, whether in AF or non-AF settings and are also elevated in many comorbidities, for example, renal failure, pulmonary embolism, decompensated heart failure, severe infection, or inflammatory disorders. Indeed, biomarkers predicting stroke are also predictive of bleeding and even glaucoma, as well as death, heart failure, etc.7,8 Thus, many biomarkers simply reflect a sick patient or a sick heart. Another hurdle includes the variations in availability, use of specific biomarker assays, and access to laboratories in different healthcare systems.Some biomarker-based approaches have been proposed and validated in highly selected anticoagulated clinical trial cohorts, statistically improving on stroke and bleeding risk prediction using baseline assessments.9–11 Even then, the C indexes as measures of prediction were modest, approximately 0.7; however, in real-world cohorts, especially with long-term follow-up, the inclusion of multiple biomarkers did not confer added predictive advantage over the risk prediction based on clinical scores.12,13 Also, the patient pathway often encounters a newly diagnosed patient on no antithrombotic therapy or aspirin, where clinicians need to risk stratify such patient and, after initiation of anticoagulation, reassess the risk regularly. As mentioned, stroke risk is a dynamic process, and regular reassessment is needed given the changing clinical profile of the patient.14 Also, bleeding risk prediction should be used to address modifiable bleeding risk factors and to flag up the high-risk patients for early and more frequent review (eg, 4 weeks rather than 4 to 6 months).15Perhaps biomarkers may be better used to rule out (rather than rule in) in relation to management decision-making. Further investigation of biomarkers for stroke or bleeding risk stratification in AF should balance the practical usefulness, costs, and daily use in clinical practice and provide data on the patient pathway from the nonanticoagulated inception stage to follow-up during long-term oral anticoagulant therapy, including possible treatment changes over time.16 Adding more and more biomarkers would certainly improve risk stratification (at least statistically) but have less practicality and clinical usefulness. However, biomarkers could aid risk stratification and treatment decision-making in patients who are borderline with regard to initiation of oral anticoagulants, for example, the population with CHA2DS2-VASc score 0 to 1.For many clinicians, simplicity and practicality matter, especially in busy clinics and ward settings. Given that the default should be to offer stroke prevention (ie, anticoagulation) to AF patients unless they are low risk, the initial focus should be to identify those low-risk patients as the first step,16 rather than the continued obsession to focus on identifying high-risk patients (whether using biomarkers or not) that led to underutilization of anticoagulants over the last decades.DisclosuresDr Lip is a consultant for Bayer/Janssen, BMS/Pfizer, Medtronic, Boehringer Ingelheim, Novartis, Verseon, and Daiichi-Sankyo and speaker for Bayer, BMS/Pfizer, Medtronic, Boehringer Ingelheim, and Daiichi-Sankyo. No fees are directly received personally. Dr Potpara declares personal fees from Bayer. The other author reports no conflicts.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Correspondence to Gregory Y.H. Lip, MD, University of Liverpool, Liverpool, United Kingdom. Email gregory.[email protected]ac.ukReferences1. Lip GYH, Freedman B, De Caterina R, Potpara TS. Stroke prevention in atrial fibrillation: past, present and future. Comparing the guidelines and practical decision-making.Thromb Haemost. 2017; 117:1230–1239. doi: 10.1160/TH16-11-0876CrossrefMedlineGoogle Scholar2. Borre ED, Goode A, Raitz G, Shah B, Lowenstern A, Chatterjee R, et al. Predicting thromboembolic and bleeding event risk in patients with non-valvular atrial fibrillation: a systematic review.Thrombosis and haemostasis. 2018; 118:2171–2187.CrossrefMedlineGoogle Scholar3. Lip GY. Stroke and bleeding risk assessment in atrial fibrillation: when, how, and why?Eur Heart J. 2013; 34:1041–1049. doi: 10.1093/eurheartj/ehs435CrossrefMedlineGoogle Scholar4. Khan AA, Lip GYH. The prothrombotic state in atrial fibrillation: pathophysiological and management implications.Cardiovasc Res. 2019; 115:31–45. doi: 10.1093/cvr/cvy272CrossrefMedlineGoogle Scholar5. Kneihsl M, Gattringer T, Bisping E, Scherr D, Raggam R, Mangge H, et al. Blood biomarkers of heart failure and hypercoagulation to identify atrial fibrillation-related stroke.Stroke. 2019; 50:2223–2226. doi: 10.1161/STROKEAHA.119.025339LinkGoogle Scholar6. Chang TY, Lip GYH, Chen SA, Chao TF. Importance of risk reassessment in patients with atrial fibrillation in guidelines: assessing risk as a dynamic process.Can J Cardiol. 2019; 35:611–618. doi: 10.1016/j.cjca.2019.01.018Google Scholar7. Ban N, Siegfried CJ, Lin JB, Shui YB, Sein J, Pita-Thomas W, et al. Gdf15 is elevated in mice following retinal ganglion cell death and in glaucoma patients.JCI Insight. 2017; 2:pii: 91455.Google Scholar8. Sharma A, Stevens SR, Lucas J, Fiuzat M, Adams KF, Whellan DJ, et al. Utility of growth differentiation factor-15, a marker of oxidative stress and inflammation, in chronic heart failure: insights from the HF-ACTION study.JACC Heart Fail. 2017; 5:724–734. doi: 10.1016/j.jchf.2017.07.013CrossrefMedlineGoogle Scholar9. Hijazi Z, Lindbäck J, Alexander JH, Hanna M, Held C, Hylek EM, et al; ARISTOTLE and STABILITY Investigators. The ABC (age, biomarkers, clinical history) stroke risk score: a biomarker-based risk score for predicting stroke in atrial fibrillation.Eur Heart J. 2016; 37:1582–1590. doi: 10.1093/eurheartj/ehw054CrossrefMedlineGoogle Scholar10. Oldgren J, Hijazi Z, Lindbäck J, Alexander JH, Connolly SJ, Eikelboom JW, et al; RE-LY and ARISTOTLE Investigators. Performance and validation of a novel biomarker-based stroke risk score for atrial fibrillation.Circulation. 2016; 134:1697–1707. doi: 10.1161/CIRCULATIONAHA.116.022802LinkGoogle Scholar11. Berg DD, Ruff CT, Jarolim P, Giugliano RP, Nordio F, Lanz HJ, et al. Performance of the ABC scores for assessing the risk of stroke or systemic embolism and bleeding in patients with atrial fibrillation in ENGAGE AF-TIMI 48.Circulation. 2019; 139:760–771. doi: 10.1161/CIRCULATIONAHA.118.038312LinkGoogle Scholar12. Rivera-Caravaca JM, Roldan V, Esteve-Pastor MA, Valdes M, Vicente V, Lip GYH, et al. Long-term stroke risk prediction in patients with atrial fibrillation: comparison of the abc-stroke and cha2ds2-vasc scores.J Am Heart Assoc. 2017; 6:pii: e006490.Google Scholar13. Roldán V, Rivera-Caravaca JM, Shantsila A, García-Fernández A, Esteve-Pastor MA, Vilchez JA, et al. Enhancing the 'real world' prediction of cardiovascular events and major bleeding with the CHA2DS2-VASc and HAS-BLED scores using multiple biomarkers.Ann Med. 2018; 50:26–34. doi: 10.1080/07853890.2017.1378429CrossrefMedlineGoogle Scholar14. Chao TF, Liao JN, Tuan TC, Lin YJ, Chang SL, Lo LW, et al. Incident co-morbidities in patients with atrial fibrillation initially with a cha2ds2-vasc score of 0 (males) or 1 (females): implications for reassessment of stroke risk in initially 'low-risk' patients [published online March 21, 2019].Thrombosis and haemostasis. doi: 10.1055/s-0039-1683933Google Scholar15. Chao TF, Lip GYH, Lin YJ, Chang SL, Lo LW, Hu YF, et al. Incident risk factors and major bleeding in patients with atrial fibrillation treated with oral anticoagulants: a comparison of baseline, follow-up and delta HAS-BLED scores with an approach focused on modifiable bleeding risk factors.Thromb Haemost. 2018; 118:768–777. doi: 10.1055/s-0038-1636534CrossrefMedlineGoogle Scholar16. Lip GYH, Banerjee A, Boriani G, Chiang CE, Fargo R, Freedman B, et al. Antithrombotic therapy for atrial fibrillation: CHEST guideline and expert panel report.Chest. 2018; 154:1121–1201. doi: 10.1016/j.chest.2018.07.040CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesBlood Biomarkers of Heart Failure and Hypercoagulation to Identify Atrial Fibrillation–Related StrokeMarkus Kneihsl, et al. Stroke. 2019;50:2223-2226 August 2019Vol 50, Issue 8 Advertisement Article InformationMetrics © 2019 American Heart Association, Inc.https://doi.org/10.1161/STROKEAHA.119.026185PMID: 31216960 Originally publishedJune 20, 2019 Keywordsrisk factorsstrokeatrial fibrillationEditorialsbiomarkersPDF download Advertisement SubjectsAtrial FibrillationIschemic StrokeRisk Factors
Atrial fibrillation (AF) is characterised by an increased risk of pathological thrombus formation due to a disruption of physiological haemostatic mechanisms that are better understood by reference to Virchow's triad of 'abnormal blood constituents', 'vessel wall abnormalities' and 'abnormal blood flow'. First, there is increased activation of the coagulation cascade, platelet reactivity and impaired fibrinolysis as a result of AF per se, and these processes are amplified with pre-existing comorbidities. Several prothrombotic biomarkers including platelet factor 4, von Willebrand factor, fibrinogen, β-thromboglobulin and D-dimer have been implicated in this process. Second, structural changes such as atrial fibrosis and endothelial dysfunction are linked to the development of AF which promote further atrial remodelling, thereby providing a suitable platform for clot formation and subsequent embolisation. Third, these factors are compounded by the presence of reduced blood flow secondary to dilatation of cardiac chambers and loss of atrial systole which have been confirmed using various imaging techniques. Overall, an improved understanding of the various factors involved in thrombus formation will allow better clinical risk stratification and targeted therapies in AF.
