Diurnal and twenty-four hour patterning of human diseases: Cardiac, vascular, and respiratory diseases, conditions, and syndromes

Smolensky, Michael H.; Portaluppi, Francesco; Manfredini, Roberto; Hermida, Ramón C.; Tiseo, Ruana; Sackett-Lundeen, L; Haus, Erhard

doi:10.1016/j.smrv.2014.07.001

Cited by 64 publications

(43 citation statements)

References 131 publications

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“…Future studies are needed to explore the influence of racial and age differences in phenotyping fatigue and the associated transcriptome profiles of these phenotypes. Further, symptom intensity in many conditions exhibit diurnal rhythmicity (35, 36). Because subjects included in this study were seen in the morning (8 am to 12 noon), future studies are needed to explore the role of circadian rhythm in the fatigue experience and the associated changes in the transcriptome profiles of subjects with fatigue.…”

Section: Discussionmentioning

confidence: 99%

Different Phenotyping Approaches Lead to Dissimilar Biologic Profiles in Men With Chronic Fatigue After Radiation Therapy

Feng

Dickinson

Kline

et al. 2016

Journal of Pain and Symptom Management

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Context Cancer-related fatigue (CRF) persists months after treatment completion. Although a CRF biomarker has not yet been identified, validated self-report questionnaires are used to define and phenotype CRF in the discovery of potential biomarkers. Objectives The purposes of this study are to identify CRF subjects using three well-known CRF phenotyping approaches utilizing validated self-report questionnaires and to compare the biologic profiles that are associated with each CRF phenotype. Methods Fatigue in men with non-metastatic prostate cancer receiving external beam radiation therapy (EBRT) was measured at: baseline (T1), midpoint (T2), endpoint (T3), and one year post-EBRT (T4) using the Functional Assessment of Cancer Therapy-Fatigue (FACT-F) and Patient Reported Outcomes Measurement Information System-Fatigue (PROMIS-F). Chronic fatigue (CF) and non-fatigue (NF) subjects were grouped based on three commonly used phenotyping approaches: 1) T4 FACT-F <43; 2) T1-T4 decline in FACT-F score >3 points; 3) T4 PROMIS-F T-score >50. Differential gene expressions using whole genome microarray analysis were compared in each of the phenotyping criterion. Results The study enrolled 43 men, where 34-38% had CF based on the 3 phenotyping approaches. Distinct gene expression patterns were observed between CF and NF subjects in each of the three CRF phenotyping approaches: 1) Approach 1 had the largest number of differentially expressed genes, and 2) Approaches 2 and 3 had 40 and 21 differentially expressed genes between the fatigue groups, respectively. Conclusion The variation in genetic profiles for CRF suggests that phenotypic profiling for CRF should be carefully considered because it directly influences biomarker discovery investigations.

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Section: Discussionmentioning

confidence: 99%

Different Phenotyping Approaches Lead to Dissimilar Biologic Profiles in Men With Chronic Fatigue After Radiation Therapy

Feng

Dickinson

Kline

et al. 2016

Journal of Pain and Symptom Management

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“…However, in both studies the temporal pattern of fall occurrence was found to be related to certain specific dominant diagnoses. The two studies are consistent in finding highest incidence of falls in stroke and Parkinson patients between 09:00 and 12:00 h and in cardiovascular (congestive heart failure) patients between 03:00 and 06:00 h. Interestingly, the reported elevated morning incidence of falls of stroke patients coincides in time to the greatest incidence of stroke events during the 24 h in the population at large (Casetta et al, 2002); moreover, the reported elevated nocturnal incidence of falls of heart failure patients coincides in time to when the symptoms of this heart condition are worst during the 24 h (Smolensky et al, 2014a). These same two studies also report more serious falls, ones that result in significant injury and fracture, are most frequent between 00:00 and 03:00 h. However, these latter findings are not substantiated in studies conducted by others (Manfredini et al, 2011;Parker et al, 1996).…”

Section: Temporal Patterning Of Falls According To Patient Diagnosis mentioning

confidence: 90%

24-hour pattern of falls in hospitalized and long-term care institutionalized elderly persons: A systematic review of the published literature

López‐Soto

Manfredini

Smolensky

et al. 2014

Chronobiology International

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Falls are common among the elderly > 65 years of age and can result in both serious trauma and costly medical care. The epidemiology of falls in the elderly typically focuses on identifying contributory exogenous environmental and endogenous age-related physical, cognitive and other health status factors; however, one potentially important variable seldom considered is time of fall. We sought to determine if falls in hospitalized/institutionalized elderly persons exhibit 24 h and other temporal patterns, since knowledge of such could be useful in their prevention. We conducted a systematic review of the published literature to critically appraise and synthesize the methods and findings of previous reports addressing clock-time, day-of-week and month-of-year fall patterns of institutionalized elderly cohorts. Medline, SCOPUS, Ovid SP and Web of Knowledge were systematic assessed, entering search terms of "accidental fall", "circadian rhythm", "biological clocks", "circadian clocks", "activity cycles", "periodicity", and with databases accepting an age limiter, "age of 65(+) years". Methodological quality was assessed by STROBE and CONSORT checklists, respectively, in observational and clinical studies. Publications were reviewed if meeting inclusion criteria of: (i) being an empirical study, (ii) adopting circadian and/or other period rhythmicity as a fall risk, and (iii) focusing on hospitalized/institutionalized falls in those ≥ 65 years of age; plus exclusion criteria of: (i) cohort < 65 years of age and (ii) reports as dissertations or editorials. The search retrieved 170 publications; however, only nine met all inclusion/exclusion criteria. Typically, past studies disregarded the temporal aspects of fall incidents; the few that did varied in quality, institutional setting, and patient type, i.e. medical diagnoses. Overall, findings suggest a single or double-peak 24 h pattern of fall incidence, with time of greatest incidence seemingly associated with circadian rhythm-dependent differences in the symptom intensity of dominating medical diagnoses (e.g. heart failure versus Alzheimer syndrome) among sample cohorts plus location (e.g. bathroom versus hallway) of occurrence. Future research is urgently required to define temporal patterns in falls and their etiology, e.g. relative to circadian and other rhythms in both the manifestation and intensity of medical conditions and adverse effects of medications according to scheduling, plus staff personnel number and work schedules as it affects patient oversight, and cyclic environmental conditions (e.g. light intensity exposure) to best design preventive measures.

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“…Thus, in humans, immune responses are stronger in the second half of the night and early morning hours. These are the times when inflammation is exacerbated and symptoms and mortality rates are highest (Buttgereit et al 2015;Smolensky et al 2015). In parallel, timed therapies that decrease inflammation during the night and early morning hours has proven to be more successful than untimed therapy (Smolensky et al 2007;Buttgereit et al 2015;Smolensky et al 2015).…”

Section: Circadian Rhythms In Respiratory Inflammatory Diseasementioning

confidence: 99%

“…Symptoms often intensify overnight and are worst upon wakening, displaying a "morning attack" (Smolensky et al 2007;Smolensky et al 2015;Long 2007;Gelfand 2004;Smolensky et al 1995;Reinberg et al 1988). Due to the time at which symptoms intensify, AR symptoms often disrupt sleep (Craig et al 2008;González-Núñez et al 2013;Santos et al 2006).…”

Section: Allergic Rhinitismentioning

confidence: 99%

A circadian based inflammatory response – implications for respiratory disease and treatment

Comas

Gordon

Oliver

et al. 2017

Sleep Science Practice

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Circadian clocks regulate the daily timing of many of our physiological, metabolic and biochemical functions. The immune system also displays circadian oscillations in immune cell count, synthesis and cytokine release, clock gene expression in cells and organs of the immune system as well as clock-controlled genes that regulate immune function. Circadian disruption leads to dysregulation of immune responses and inflammation which can further disrupt circadian rhythms. The response of organisms to immune challenges, such as allergic reactions also vary depending on time of the day, which can lead to detrimental responses particularly during the rest and early active periods. This review evaluates what is currently known in terms of circadian biology of immune response and the cross-talk between circadian and immune system. We discuss the circadian pattern of three respiratory-related inflammatory diseases, chronic obstructive pulmonary disease, allergic rhinitis and asthma. Increasing our knowledge on circadian patterns of immune responses and developing chronotherapeutic studies in inflammatory diseases with strong circadian patterns will lead to preventive measures as well as improved therapies focussing on the circadian rhythms of symptoms and the daily variation of the patients' responses to medication.

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Diurnal and twenty-four hour patterning of human diseases: Cardiac, vascular, and respiratory diseases, conditions, and syndromes

Cited by 64 publications

References 131 publications

Different Phenotyping Approaches Lead to Dissimilar Biologic Profiles in Men With Chronic Fatigue After Radiation Therapy

Different Phenotyping Approaches Lead to Dissimilar Biologic Profiles in Men With Chronic Fatigue After Radiation Therapy

24-hour pattern of falls in hospitalized and long-term care institutionalized elderly persons: A systematic review of the published literature

A circadian based inflammatory response – implications for respiratory disease and treatment

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