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Smokovski I, Steinle N, Behnke A, Bhaskar SMM, Grech G, Richter K, Niklewski G, Birkenbihl C, Parini P, Andrews RJ, Bauchner H, Golubnitschaja O. Digital biomarkers: 3PM approach revolutionizing chronic disease management - EPMA 2024 position. EPMA J 2024; 15:149-162. [PMID: 38841615 PMCID: PMC11147994 DOI: 10.1007/s13167-024-00364-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 04/23/2024] [Indexed: 06/07/2024]
Abstract
Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide. Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs. Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large. DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.
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Affiliation(s)
- Ivica Smokovski
- University Clinic of Endocrinology, Diabetes and Metabolic Disorders, Skopje, North Macedonia
- Faculty of Medical Sciences, University Goce Delcev, Stip, North Macedonia
| | - Nanette Steinle
- Veteran Affairs Capitol Health Care Network, Linthicum, MD USA
- University of Maryland School of Medicine, Baltimore, MD USA
| | - Andrew Behnke
- Endocrinology Section, Carilion Clinic, Roanoke, VA USA
- Virginia Tech Carilion School of Medicine, Roanoke, VA USA
| | - Sonu M. M. Bhaskar
- Department of Neurology, Division of Cerebrovascular Medicine and Neurology, National Cerebral and Cardiovascular Centre (NCVC), Suita, Osaka Japan
- Department of Neurology & Neurophysiology, Liverpool Hospital, Ingham Institute for Applied Medical Research and South Western Sydney Local Health District, Sydney, NSW Australia
- NSW Brain Clot Bank, Global Health Neurology Lab & NSW Health Pathology, Sydney, NSW Australia
| | - Godfrey Grech
- Department of Pathology, Faculty of Medicine & Surgery, University of Malta, Msida, Malta
| | - Kneginja Richter
- Faculty of Medical Sciences, University Goce Delcev, Stip, North Macedonia
- CuraMed Tagesklinik Nürnberg GmbH, Nuremberg, Germany
- Technische Hochschule Nürnberg GSO, Nuremberg, Germany
- University Clinic for Psychiatry and Psychotherapy, Paracelsus Medical University, Nuremberg, Germany
| | - Günter Niklewski
- University Clinic for Psychiatry and Psychotherapy, Paracelsus Medical University, Nuremberg, Germany
| | - Colin Birkenbihl
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Paolo Parini
- Cardio Metabolic Unit, Department of Medicine Huddinge, and Department of Laboratory Medicine, Karolinska Institute, and Medicine Unit of Endocrinology, Theme Inflammation and Ageing, Karolinska University Hospital, Stockholm, Sweden
| | - Russell J. Andrews
- Nanotechnology & Smart Systems Groups, NASA Ames Research Center, Aerospace Medical Association, Silicon Valley, CA USA
| | - Howard Bauchner
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
| | - Olga Golubnitschaja
- Predictive, Preventive and Personalized (3P) Medicine, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Coskun A, Zarepour A, Zarrabi A. Physiological Rhythms and Biological Variation of Biomolecules: The Road to Personalized Laboratory Medicine. Int J Mol Sci 2023; 24:ijms24076275. [PMID: 37047252 PMCID: PMC10094461 DOI: 10.3390/ijms24076275] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 03/24/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023] Open
Abstract
The concentration of biomolecules in living systems shows numerous systematic and random variations. Systematic variations can be classified based on the frequency of variations as ultradian (<24 h), circadian (approximately 24 h), and infradian (>24 h), which are partly predictable. Random biological variations are known as between-subject biological variations that are the variations among the set points of an analyte from different individuals and within-subject biological variation, which is the variation of the analyte around individuals’ set points. The random biological variation cannot be predicted but can be estimated using appropriate measurement and statistical procedures. Physiological rhythms and random biological variation of the analytes could be considered the essential elements of predictive, preventive, and particularly personalized laboratory medicine. This systematic review aims to summarize research that have been done about the types of physiological rhythms, biological variations, and their effects on laboratory tests. We have searched the PubMed and Web of Science databases for biological variation and physiological rhythm articles in English without time restrictions with the terms “Biological variation, Within-subject biological variation, Between-subject biological variation, Physiological rhythms, Ultradian rhythms, Circadian rhythm, Infradian rhythms”. It was concluded that, for effective management of predicting, preventing, and personalizing medicine, which is based on the safe and valid interpretation of patients’ laboratory test results, both physiological rhythms and biological variation of the measurands should be considered simultaneously.
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Ferreira-Souza LF, Julianelli-Peçanha M, Coelho-Oliveira AC, da Silva Bahia CMC, Paineiras-Domingos LL, Reis-Silva A, Moura-Fernandes MC, Trindade-Gusmão LC, Taiar R, da Cunha Sá-Caputo D, Rapin A, Bernardo-Filho M. Impacts of COVID-19 Pandemic on Sleep Quality Evaluated by Wrist Actigraphy: A Systematic Review. J Clin Med 2023; 12:jcm12031182. [PMID: 36769830 PMCID: PMC9917512 DOI: 10.3390/jcm12031182] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/14/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
COVID-19 has probably contributed as a risk factor for sleep disturbance. Actigraphy has been used to evaluate sleep complaints in self-isolated populations and frontline doctors during the COVID-19 pandemic. This systematic review aims to summarize the impact of the COVID-19 pandemic on sleep through wrist actigraphy, estimating sleep latency, total sleep time, awakening-after-sleep onset, and sleep efficiency. Searches were conducted of observational studies on the PubMed, Embase, Scopus, Web of Science, and PEDro databases from 1 December 2019 to 31 December 2022. Ninety articles were found, and given the eligibility criteria, fifteen were selected. Six studies were classified by the National Health and Medical Research Council as evidence level IV, two studies as level III-3, and seven studies as level III-2. According to the ACROBAT-NRSI instrument, three studies were classified as having a "serious" risk of bias, two as having "critical" risk, four as having "moderate" risk, and six as having "low" risk. In the selected publications, various populations were evaluated via actigraphy during the COVID-19 pandemic, with reports of "poor" sleep quality. Actigraphy may be a relevant tool to assess individual day-night rhythms and provide recommendations under enduring pandemic conditions. Moreover, as actigraphy presents objective data for sleep evaluations, it is suggested that this method be used in similar pandemics and that actigraphy be included as part of the sleep hygiene strategy.
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Affiliation(s)
- Luiz Felipe Ferreira-Souza
- Laboratório de Vibrações Mecânicas e Práticas Integrativas—LAVIMPI, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes and Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
- Programa de Pós-Graduação em Saúde, Medicina Laboratorial e Tecnologia Forense, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
| | - Marize Julianelli-Peçanha
- Coordenação Médica do Hospital Estadual da Mulher Heloneida Studart, São João de Meriti 25565-171, RJ, Brazil
| | - Ana Carolina Coelho-Oliveira
- Laboratório de Vibrações Mecânicas e Práticas Integrativas—LAVIMPI, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes and Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
- Programa de Pós-Graduação em Fisiopatologia Clínica e Experimental, Faculdade de Ciências Médicas, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20550-900, RJ, Brazil
| | - Christianne Martins Corrêa da Silva Bahia
- Serviço de Neurologia, Setor de Distúrbios do Sono, Hospital Universitário Pedro Ernesto, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20550-031, RJ, Brazil
| | - Laisa Liane Paineiras-Domingos
- Laboratório de Vibrações Mecânicas e Práticas Integrativas—LAVIMPI, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes and Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
- Programa de Pós-Graduação em Ciências Médicas, Faculdade de Ciências Médicas, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20551-030, RJ, Brazil
- Departamento de Fisioterapia, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador 40110-902, BA, Brazil
| | - Aline Reis-Silva
- Laboratório de Vibrações Mecânicas e Práticas Integrativas—LAVIMPI, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes and Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
- Programa de Pós-Graduação em Saúde, Medicina Laboratorial e Tecnologia Forense, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
- Programa de Pós-Graduação em Ciências Médicas, Faculdade de Ciências Médicas, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20551-030, RJ, Brazil
| | - Márcia Cristina Moura-Fernandes
- Laboratório de Vibrações Mecânicas e Práticas Integrativas—LAVIMPI, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes and Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
- Programa de Pós-Graduação em Fisiopatologia Clínica e Experimental, Faculdade de Ciências Médicas, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20550-900, RJ, Brazil
- Programa de Pós-Graduação em Ciências Médicas, Faculdade de Ciências Médicas, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20551-030, RJ, Brazil
| | - Luiza Carla Trindade-Gusmão
- Laboratório de Vibrações Mecânicas e Práticas Integrativas—LAVIMPI, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes and Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
- Programa de Pós-Graduação em Saúde, Medicina Laboratorial e Tecnologia Forense, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
| | - Redha Taiar
- MATériaux et Ingénierie Mécanique (MATIM), Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Danubia da Cunha Sá-Caputo
- Laboratório de Vibrações Mecânicas e Práticas Integrativas—LAVIMPI, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes and Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
- Programa de Pós-Graduação em Saúde, Medicina Laboratorial e Tecnologia Forense, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
- Programa de Pós-Graduação em Ciências Médicas, Faculdade de Ciências Médicas, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20551-030, RJ, Brazil
| | - Amandine Rapin
- Faculté de Médecine, Université de Reims Champagne Ardennes, UR 3797 VieFra, 51097 Reims, France
| | - Mario Bernardo-Filho
- Laboratório de Vibrações Mecânicas e Práticas Integrativas—LAVIMPI, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes and Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, RJ, Brazil
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Li D, Li S, Xia Z, Cao J, Zhang J, Chen B, Zhang X, Zhu W, Fang J, Liu Q, Hua W. Prognostic significance of pretreatment red blood cell distribution width in primary diffuse large B-cell lymphoma of the central nervous system for 3P medical approaches in multiple cohorts. EPMA J 2022; 13:499-517. [PMID: 36061828 PMCID: PMC9437163 DOI: 10.1007/s13167-022-00290-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/04/2022] [Indexed: 12/08/2022]
Abstract
Background/aims Predicting the clinical outcomes of primary diffuse large B-cell lymphoma of the central nervous system (PCNS-DLBCL) to methotrexate-based combination immunochemotherapy treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). The red blood cell distribution width (RDW) has been reported to be associated with the clinical outcomes of multiple cancer. However, its prognostic role in PCNS-DLBCL is yet to be evaluated. Therefore, we aimed to effectively stratify PCNS-DLBCL patients with different prognosis in advance and early identify the patients who were appropriate to methotrexate-based combination immunochemotherapy based on the pretreatment level of RDW and a clinical prognostic model. Methods A prospective-retrospective, multi-cohort study was conducted from 2010 to 2020. We evaluated RDW in 179 patients (retrospective discovery cohorts of Huashan Center and Renji Center and prospective validation cohort of Cancer Center) with PCNS-DLBCL treated with methotrexate-based combination immunochemotherapy. A generalized additive model with locally estimated scatterplot smoothing was used to identify the relationship between pretreatment RDW levels and clinical outcomes. The high vs low risk of RDW combined with MSKCC score was determined by a minimal P-value approach. The clinical outcomes in different groups were then investigated. Results The pretreatment RDW showed a U-shaped relationship with the risk of overall survival (OS, P = 0.047). The low RDW (< 12.6) and high RDW (> 13.4) groups showed significantly worse OS (P < 0.05) and progression-free survival (PFS; P < 0.05) than the median group (13.4 > RDW > 12.6) in the discovery and validation cohort, respectively. RDW could predict the clinical outcomes successfully. In the discovery cohort, RDW achieved the area under the receiver operating characteristic curve (AUC) of 0.9206 in predicting the clinical outcomes, and the predictive value (AUC = 0.7177) of RDW was verified in the validation cohort. In addition, RDW combined with MSKCC predictive model can distinguish clinical outcomes with the AUC of 0.8348 for OS and 0.8125 for PFS. Compared with the RDW and MSKCC prognosis variables, the RDW combined with MSKCC scores better identified a subgroup of patients with favorable long-term survival in the validation cohort (P < 0.001). RDW combined MSKCC score remained to be independently associated with clinical outcomes by multivariable analysis. Conclusions Based on the pretreatment RDW and MSKCC scores, a novel predictive tool was established to stratify PCNS-DLBCL patients with different prognosis effectively. The predictive model developed accordingly is promising to judge the response of PCNS-DLBCL to methotrexate-based combination immunochemotherapy treatment. Thus, hematologists and oncologists could tailor and adjust therapeutic modalities by monitoring RDW in a prospective rather than the reactive manner, which could save medical expenditures and is a key concept in 3PM. In brief, RDW combined with MSKCC model could serve as an important tool for predicting the response to different treatment and the clinical outcomes for PCNS-DLBCL, which could conform with the principles of predictive, preventive, and personalized medicine. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-022-00290-5.
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Affiliation(s)
- Danhui Li
- Department of Pathology, Renji Hospital, School of Medicine, Shanghai JiaoTong University, No. 160 PuJian Road, Shanghai, 200127 China
| | - Shengjie Li
- Department of Neurosurgery, Huashan Hospital, Fudan University, No. 12 Wulumuqi Road, Shanghai, 200040 China
- Institute of Neurosurgery, Fudan University, Shanghai, 200040 China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, 200040 China
- Department of Clinical Laboratory, EENT Hospital, Shanghai Medical College, Fudan University, Shanghai, 200032 China
| | - Zuguang Xia
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032 China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 China
| | - Jiazhen Cao
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 China
- Department of Clinical Laboratory, Fudan University Shanghai Cancer Center, Shanghai, 200032 China
| | - Jinsen Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, No. 12 Wulumuqi Road, Shanghai, 200040 China
- Institute of Neurosurgery, Fudan University, Shanghai, 200040 China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, 200040 China
| | - Bobin Chen
- Department of Hematology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Xin Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, No. 12 Wulumuqi Road, Shanghai, 200040 China
- Institute of Neurosurgery, Fudan University, Shanghai, 200040 China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, 200040 China
| | - Wei Zhu
- Department of Neurosurgery, Huashan Hospital, Fudan University, No. 12 Wulumuqi Road, Shanghai, 200040 China
- Institute of Neurosurgery, Fudan University, Shanghai, 200040 China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, 200040 China
| | - Jianchen Fang
- Department of Pathology, Renji Hospital, School of Medicine, Shanghai JiaoTong University, No. 160 PuJian Road, Shanghai, 200127 China
| | - Qiang Liu
- Department of Pathology, Renji Hospital, School of Medicine, Shanghai JiaoTong University, No. 160 PuJian Road, Shanghai, 200127 China
| | - Wei Hua
- Department of Neurosurgery, Huashan Hospital, Fudan University, No. 12 Wulumuqi Road, Shanghai, 200040 China
- Institute of Neurosurgery, Fudan University, Shanghai, 200040 China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, 200040 China
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Karabatsiakis A, de Punder K, Salinas-Manrique J, Todt M, Dietrich DE. Hair cortisol level might be indicative for a 3PM approach towards suicide risk assessment in depression: comparative analysis of mentally stable and depressed individuals versus individuals after completing suicide. EPMA J 2022; 13:383-395. [PMID: 36061827 PMCID: PMC9425778 DOI: 10.1007/s13167-022-00296-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 08/10/2022] [Indexed: 11/24/2022]
Abstract
Depression and suicidal behavior are interrelated, stress-associated mental health conditions, each lacking biological verifiability. Concepts of predictive, preventive, and personalized medicine (3PM) are almost completely missing for both conditions but are of utmost importance. Prior research reported altered levels of the stress hormone cortisol in the scalp hair of depressed individuals, however, data on hair cortisol levels (HCL) for suicide completers (SC) are missing. Here, we aimed to identify differences in HCL between subject with depression (n = 20), SC (n = 45) and mentally stable control subjects (n = 12) to establish the usage of HCL as a new target for 3PM. HCL was measured in extracts of pulverized hair (1-cm and 3-cm hair segments) using ELISA. In 3-cm hair segments, an average increase in HCL for depressed patients (1.66 times higher; p = .011) and SC (5.46 times higher; p = 1.65 × 10−5) compared to that for controls was observed. Furthermore, the average HCL in SC was significantly increased compared to that in the depressed group (3.28 times higher; p = 1.4 × 10−5). A significant correlation between HCL in the 1-cm and the 3-cm hair segments, as well as a significant association between the severity of depressive symptoms and HCL (3-cm segment) was found. To conclude, findings of increased HCL in subjects with depression compared to that in controls were replicated and an additional increase in HCL was seen in SC in comparison to patients with depression. The usage of HCL for creating effective patient stratification and predictive approach followed by the targeted prevention and personalization of medical services needs to be validated in follow-up studies.
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Affiliation(s)
- Alexander Karabatsiakis
- Department of Clinical Psychology II, Institute of Psychology, University of Innsbruck, Innsbruck, Austria
| | - Karin de Punder
- Department of Clinical Psychology II, Institute of Psychology, University of Innsbruck, Innsbruck, Austria
| | | | - Melanie Todt
- Institutes for Forensic Medicine, Hannover Medical School, Hannover, Germany
| | - Detlef E. Dietrich
- AMEOS Clinic for Psychiatry and Psychotherapy, Hildesheim, Germany
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
- Center for Systems Neuroscience Hannover, Hannover Medical School, Hannover, Germany
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Arafa A, Kokubo Y, Shimamoto K, Kashima R, Watanabe E, Sakai Y, Li J, Teramoto M, Sheerah HA, Kusano K. Sleep duration and atrial fibrillation risk in the context of predictive, preventive, and personalized medicine: the Suita Study and meta-analysis of prospective cohort studies. EPMA J 2022; 13:77-86. [PMID: 35273660 PMCID: PMC8897526 DOI: 10.1007/s13167-022-00275-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/10/2022] [Indexed: 12/08/2022]
Abstract
Background Short and long sleep durations are common behaviors that could predict several cardiovascular diseases. However, the association between sleep duration and atrial fibrillation (AF) risk is not well-established. AF is preventable, and risk prevention approaches could reduce its occurrence. Investigating whether sleep duration could predict AF incidence for possible preventive interventions and determining the impact of various lifestyle and clinical characteristics on this association to personalize such interventions are essential. Herein, we investigated the association between sleep duration and AF risk using a prospective cohort study and a meta-analysis of epidemiological evidence. Methods Data of 6898 people, aged 30-84 years, from the Suita Study, were analyzed. AF was diagnosed during the follow-up by ECG, medical records, checkups, and death certificates, while a baseline questionnaire was used to assess sleep duration. The Cox regression was used to compute the hazard ratios (HRs) and 95% confidence intervals (CIs) of AF risk for daily sleep ≤ 6 (short sleep), ≥ 8 (long sleep), and irregular sleep, including night-shift work compared with 7 h (moderate sleep). Then, we combined our results with those from other eligible prospective cohort studies in two meta-analyses for the short and long sleep. Results In the Suita Study, within a median follow-up period of 14.5 years, short and irregular sleep, but not long sleep, were associated with the increased risk of AF in the age- and sex-adjusted models: HRs (95% CIs) = 1.36 (1.03, 1.80) and 1.62 (1.16, 2.26) and the multivariable-adjusted models: HRs (95% CIs) = 1.34 (1.01, 1.77) and 1.63 (1.16, 2.30), respectively. The significant associations between short and irregular sleep and AF risk remained consistent across different ages, sex, smoking, and drinking groups. However, they were attenuated among overweight and hypertensive participants. In the meta-analyses, short and long sleep durations were associated with AF risk: pooled HRs (95% CIs) = 1.21 (1.02, 1.42) and 1.18 (1.03, 1.35). No signs of significant heterogeneity across studies or publication bias were detected. Conclusion Short, long, and irregular sleep could be associated with increased AF risk. In the context of predictive, preventive, and personalized medicine, sleep duration should be considered in future AF risk scores to stratify the general population for potential personalized lifestyle modification interventions. Sleep management services should be considered for AF risk prevention, and these services should be individualized according to clinical characteristics and lifestyle factors. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s13167-022-00275-4.
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Affiliation(s)
- Ahmed Arafa
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, 6-1, Kishibe-Shinmachi, Suita, Osaka 564-8565 Japan
- Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University, Suita, Japan
- Department of Public Health, Faculty of Medicine, Beni-Suef University, Beni Suef, Egypt
| | - Yoshihiro Kokubo
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, 6-1, Kishibe-Shinmachi, Suita, Osaka 564-8565 Japan
| | - Keiko Shimamoto
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Rena Kashima
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, 6-1, Kishibe-Shinmachi, Suita, Osaka 564-8565 Japan
- Public Health Division, Ibaraki Public Health Center, Osaka Prefectural Government, Ibaraki, Osaka Japan
| | - Emi Watanabe
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, 6-1, Kishibe-Shinmachi, Suita, Osaka 564-8565 Japan
| | - Yukie Sakai
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, 6-1, Kishibe-Shinmachi, Suita, Osaka 564-8565 Japan
| | - Jiaqi Li
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, 6-1, Kishibe-Shinmachi, Suita, Osaka 564-8565 Japan
- Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Masayuki Teramoto
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, 6-1, Kishibe-Shinmachi, Suita, Osaka 564-8565 Japan
- Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Haytham A. Sheerah
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, 6-1, Kishibe-Shinmachi, Suita, Osaka 564-8565 Japan
- Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Kengo Kusano
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
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Wang W, Yan Y, Guo Z, Hou H, Garcia M, Tan X, Anto EO, Mahara G, Zheng Y, Li B, Kang T, Zhong Z, Wang Y, Guo X, Golubnitschaja O. All around suboptimal health - a joint position paper of the Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine. EPMA J 2021; 12:403-433. [PMID: 34539937 PMCID: PMC8435766 DOI: 10.1007/s13167-021-00253-2] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/25/2021] [Indexed: 02/07/2023]
Abstract
First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person. This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions:Stress overload associated pathologiesMale and female healthPlanned pregnanciesPeriodontal healthEye disordersInflammatory disorders, wound healing and pain management with associated complicationsMetabolic disorders and suboptimal body weightCardiovascular pathologiesCancersStroke, particularly of unknown aetiology and in young individualsSleep medicineSports medicineImproved individual outcomes under pandemic conditions such as COVID-19.
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Affiliation(s)
- Wei Wang
- Centre for Precision Health, Edith Cowan University, Perth, Australia
- Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
- First Affiliated Hospital, Shantou University Medical College, Shantou, China
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
| | - Yuxiang Yan
- Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
| | - Zheng Guo
- Centre for Precision Health, Edith Cowan University, Perth, Australia
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
| | - Haifeng Hou
- Centre for Precision Health, Edith Cowan University, Perth, Australia
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
| | - Monique Garcia
- Centre for Precision Health, Edith Cowan University, Perth, Australia
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
| | - Xuerui Tan
- First Affiliated Hospital, Shantou University Medical College, Shantou, China
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
| | - Enoch Odame Anto
- Centre for Precision Health, Edith Cowan University, Perth, Australia
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
- Department of Medical Diagnostics, College of Health Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Gehendra Mahara
- First Affiliated Hospital, Shantou University Medical College, Shantou, China
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
| | - Yulu Zheng
- Centre for Precision Health, Edith Cowan University, Perth, Australia
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
| | - Bo Li
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
- School of Nursing and Health, Henan University, Kaifeng, China
| | - Timothy Kang
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
- Institute of Chinese Acuology, Perth, Australia
| | - Zhaohua Zhong
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
- School of Basic Medicine, Harbin Medical University, Harbin, China
| | - Youxin Wang
- Centre for Precision Health, Edith Cowan University, Perth, Australia
- Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- Department of Medical Diagnostics, College of Health Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Xiuhua Guo
- Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
| | - Olga Golubnitschaja
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
- Predictive, Preventive and Personalised (3P) Medicine, Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - On Behalf of Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine
- Centre for Precision Health, Edith Cowan University, Perth, Australia
- Beijing Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
- First Affiliated Hospital, Shantou University Medical College, Shantou, China
- Suboptimal Health Study Consortium, Kumasi, Ghana
- Suboptimal Health Study Consortium, Perth, Australia
- Suboptimal Health Study Consortium, Beijing, China
- Suboptimal Health Study Consortium, Bonn, Germany
- European Association for Predictive, Preventive and Personalised, Medicine, Brussels, Belgium
- Department of Medical Diagnostics, College of Health Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- School of Nursing and Health, Henan University, Kaifeng, China
- Institute of Chinese Acuology, Perth, Australia
- School of Basic Medicine, Harbin Medical University, Harbin, China
- Predictive, Preventive and Personalised (3P) Medicine, Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Sleep Disorders in Cancer-A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111696. [PMID: 34770209 PMCID: PMC8583058 DOI: 10.3390/ijerph182111696] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/07/2021] [Accepted: 09/18/2021] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Sleep disorders, especially insomnia, are very common in different kinds of cancers, but their prevalence and incidence are not well-known. Disturbed sleep in cancer is caused by different reasons and usually appears as a comorbid disorder to different somatic and psychiatric diagnoses, psychological disturbances and treatment methods. There can be many different predictors for sleep disturbances in these vulnerable groups, such as pre-existing sleep disorders, caused by the mental status in cancer or as side effect of the cancer treatment. METHODS A systematic literature review of 8073 studies was conducted on the topic of sleep and sleep disorders in cancer patients. The articles were identified though PubMed, PsycInfo and Web of Knowledge, and a total number of 89 publications were qualified for analysis. RESULTS The identified eighty-nine studies were analyzed on the topic of sleep and sleep disorders in cancer, twenty-six studies on sleep and fatigue in cancer and sixty-one studies on the topic of sleep disorders in cancer. The prevalence of sleep disturbences and/or sleep disorders in cancer was up to 95%. DISCUSSION Sleep disturbances and sleep disorders (such as insomnia, OSAS, narcolepsy and RLS; REM-SBD) in cancer patients can be associated with different conditions. Side effects of cancer treatment and cancer-related psychological dysfunctions can be instigated by sleep disturbances and sleep disorders in these patients, especially insomnia and OSAS are common. An evidence-based treatment is necessary for concomitant mental and/or physical states.
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