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Jolly AA, Brown RB, Tozer DJ, Hong YT, Fryer TD, Aigbirhio FI, O’Brien JT, Markus HS. Are central and systemic inflammation associated with fatigue in cerebral small vessel disease? Int J Stroke 2024; 19:705-713. [PMID: 38533609 PMCID: PMC11292988 DOI: 10.1177/17474930241245613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/19/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Fatigue is a common symptom in cerebral small vessel disease (SVD), but its pathogenesis is poorly understood. It has been suggested that inflammation may play a role. We determined whether central (neuro) inflammation and peripheral inflammation were associated with fatigue in SVD. METHODS Notably, 36 patients with moderate-to-severe SVD underwent neuropsychometric testing, combined positron emission tomography and magnetic resonance imaging (PET-MRI) scan, and blood draw for the analysis of inflammatory blood biomarkers. Microglial signal was taken as a proxy for neuroinflammation, assessed with radioligand 11C-PK11195. Of these, 30 subjects had full PET datasets for analysis. We assessed global 11C-PK11195 binding and hotspots of 11C-PK11195 binding in the normal-appearing white matter, lesioned tissue, and combined total white matter. Peripheral inflammation was assessed with serum C-reactive protein (CRP) and using the Olink cardiovascular III proteomic panel comprising 92 biomarkers of cardiovascular inflammation and endothelial activation. Fatigue was assessed using the fatigue severity scale (FSS), the visual analog fatigue scale, and a subscale of the Geriatric Depression Scale. RESULTS Mean (SD) age was 68.7 (11.2) years, and 63.9% were male. Of these, 55.6% showed fatigue on the FSS. Fatigued participants had higher disability scores (p = 0.02), higher total GDS scores (p = 0.02), and more commonly reported a history of depression (p = 0.04). 11C-PK11195 ligand binding in the white matter was not associated with any measure of fatigue. Serum CRP was significantly associated with average fatigue score on FSS (ρ = 0.48, p = 0.004); this association persisted when controlling for age, sex, disability score, and depression (β = 0.49, 95% CI (0.17, 2.26), p = 0.03). Blood biomarkers from the Olink panel showed no association with fatigue. CONCLUSION In symptomatic SVD patients, neuroinflammation, assessed with microglial marker 11C-PK11195, was not associated with fatigue. We found some evidence for a role of systematic inflammation, evidenced by an association between fatigue severity and raised CRP, but further studies are required to understand this relationship and inform whether it could be therapeutically modified to reduce fatigue severity. DATA ACCESS STATEMENT Data for this study are available from the corresponding author upon reasonable request.
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Affiliation(s)
- Amy A Jolly
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Robin B Brown
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Daniel J Tozer
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Young T Hong
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Tim D Fryer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Franklin I Aigbirhio
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - John T O’Brien
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Hugh S Markus
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Smith MJ, Pellegrini M, Major B, Graco M, Porter S, Kramer S, Sewell K, Salberg S, Chen Z, Mychasiuk R, Lannin NA. Improving physical movement during stroke rehabilitation: investigating associations between sleep measured by wearable actigraphy technology, fatigue, and key biomarkers. J Neuroeng Rehabil 2024; 21:84. [PMID: 38802847 PMCID: PMC11131210 DOI: 10.1186/s12984-024-01380-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 05/10/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Sleep disturbance and fatigue are common in individuals undergoing inpatient rehabilitation following stroke. Understanding the relationships between sleep, fatigue, motor performance, and key biomarkers of inflammation and neuroplasticity could provide valuable insight into stroke recovery, possibly leading to personalized rehabilitation strategies. This study aimed to investigate the influence of sleep quality on motor function following stroke utilizing wearable technology to obtain objective sleep measurements. Additionally, we aimed to determine if there were relationships between sleep, fatigue, and motor function. Lastly, the study aimed to determine if salivary biomarkers of stress, inflammation, and neuroplasticity were associated with motor function or fatigue post-stroke. METHODS Eighteen individuals who experienced a stroke and were undergoing inpatient rehabilitation participated in a cross-sectional observational study. Following consent, participants completed questionnaires to assess sleep patterns, fatigue, and quality of life. Objective sleep was measured throughout one night using the wearable Philips Actiwatch. Upper limb motor performance was assessed on the following day and saliva was collected for biomarker analysis. Correlation analyses were performed to assess the relationships between variables. RESULTS Participants reported poor sleep quality, frequent awakenings, and difficulties falling asleep following stroke. We identified a significant negative relationship between fatigue severity and both sleep quality (r=-0.539, p = 0.021) and participants experience of awakening from sleep (r=-0.656, p = 0.003). A significant positive relationship was found between grip strength on the non-hemiplegic limb and salivary gene expression of Brain-derived Neurotrophic Factor (r = 0.606, p = 0.028), as well as a significant negative relationship between grip strength on the hemiplegic side and salivary gene expression of C-reactive Protein (r=-0.556, p = 0.048). CONCLUSION The findings of this study emphasize the importance of considering sleep quality, fatigue, and biomarkers in stroke rehabilitation to optimize recovery and that interventions may need to be tailored to the individual. Future longitudinal studies are required to explore these relationships over time. Integrating wearable technology for sleep and biomarker analysis can enhance monitoring and prediction of outcomes following stroke, ultimately improving rehabilitation strategies and patient outcomes.
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Affiliation(s)
- Madeleine J Smith
- Department of Neuroscience, School of Translational Medicine, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Michael Pellegrini
- Department of Neuroscience, School of Translational Medicine, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Brendan Major
- Department of Neuroscience, School of Translational Medicine, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
- Alfred Health, Melbourne, VIC, 3004, Australia
| | | | | | - Sharon Kramer
- Department of Neuroscience, School of Translational Medicine, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
- Alfred Health, Melbourne, VIC, 3004, Australia
| | - Katherine Sewell
- Department of Neuroscience, School of Translational Medicine, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Sabrina Salberg
- Department of Neuroscience, School of Translational Medicine, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Zhibin Chen
- Department of Neuroscience, School of Translational Medicine, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Richelle Mychasiuk
- Department of Neuroscience, School of Translational Medicine, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Natasha A Lannin
- Department of Neuroscience, School of Translational Medicine, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia.
- Alfred Health, Melbourne, VIC, 3004, Australia.
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3
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Luzum G, Thrane G, Aam S, Eldholm RS, Grambaite R, Munthe-Kaas R, Thingstad P, Saltvedt I, Askim T. A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study. Arch Phys Med Rehabil 2024; 105:921-929. [PMID: 38242298 DOI: 10.1016/j.apmr.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 01/21/2024]
Abstract
OBJECTIVE This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up. DESIGN A prospective multicenter cohort-study with 18-month follow-up. SETTING Outpatient clinics at 3 university hospitals and 2 local hospitals. PARTICIPANTS 474 participants with the diagnosis of acute stroke (mean ± SD age; 70.5 (11.3), 59% male; N=474). INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES The primary outcome, fatigue at 18 months, was assessed using the Fatigue Severity Scale (FSS-7). FSS-7≥5 was defined as fatigue. In total, 45 prediction variables were collected, at initial hospital-stay and 3-month post-stroke. RESULTS The best performing model, random forest, predicted 69% of all subjects with fatigue correctly with a sensitivity of 0.69 (95% CI: 0.50, 0.86), a specificity of 0.74 (95% CI: 0.66, 0.83), and an Area under the Receiver Operator Characteristic curve of 0.79 (95% CI: 0.69, 0.87) in new unseen data. The proportion of subjects predicted to suffer from fatigue, who truly suffered from fatigue at 18-months was estimated to 0.41 (95% CI: 0.26, 0.57). The proportion of subjects predicted to be free from fatigue who truly did not have fatigue at 18-months was estimated to 0.90 (95% CI: 0.83, 0.96). CONCLUSIONS Our findings indicate that the model has satisfactory ability to predict fatigue in the chronic phase post-stroke and may be applicable in clinical settings.
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Affiliation(s)
- Geske Luzum
- Department of Neuromedicine and Movement Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Gyrd Thrane
- Department of Health and Care Science, The Arctic University of Norway, Tromsø, Norway
| | - Stina Aam
- Department of Neuromedicine and Movement Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway; Department of Geriatric Medicine, Clinic of Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway
| | - Rannveig Sakshaug Eldholm
- Department of Neuromedicine and Movement Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway; Department of Geriatric Medicine, Clinic of Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ramune Grambaite
- Department of Psychology, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
| | - Ragnhild Munthe-Kaas
- Department of Medicine, Kongsberg Hospital, Vestre Viken Hospital Trust, Drammen, Norway; Department of Medicine, Bærum Hospital, Vestre Viken Hospital Trust, Drammen, Norway
| | - Pernille Thingstad
- Department of Neuromedicine and Movement Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway; Department of Health and Welfare, Trondheim Municipality, Trondheim, Norway
| | - Ingvild Saltvedt
- Department of Neuromedicine and Movement Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway; Department of Geriatric Medicine, Clinic of Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway
| | - Torunn Askim
- Department of Neuromedicine and Movement Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
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Ouyang Q, Xu L, Zhang Y, Huang L, Du Y, Yu M. Relationship between glycated hemoglobin levels at admission and chronic post-stroke fatigue in patients with acute ischemic stroke. Exp Gerontol 2024; 188:112395. [PMID: 38452990 DOI: 10.1016/j.exger.2024.112395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 02/28/2024] [Accepted: 03/04/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Chronic Post-Stroke Fatigue (PSF) is a common and persistent complications among ischemic stroke survivors. The serum glycated hemoglobin (HbA1c) level, as it is known has emerged as a critical risk factor for Acute Ischemic Stroke (AIS) and post-stroke cognitive and emotional impairment. However, no studies have been conducted on the link between HbA1c and PSF. Therefore, this study aims to estimate the relationship between HbA1c and PSF in the chronic phase. METHODS A longitudinal study was conducted on 559 patients diagnosed with their first AIS episode and admitted to Suining Central Hospital within three days after onset. All patients were examined for serum HbA1c, blood glucose levels and routine blood biochemical indicators at admission. The Fatigue Severity Scale (FSS) was employed to assess fatigue symptoms at six months post-stroke. Multivariate logistic regression and smooth curve fitting were used to analyze the relationship between admission HbA1c, blood glucose levels, discharge blood glucose and PSF, and the predictive value of HbA1c on PSF was assessed using a segmented linear regression model. RESULTS 189(33.8 %)of the 559 patients included in the study, reported PSF at six-month follow-up. Compared with the non-PSF group, the PSF group displayed significantly higher levels of HbA1c (7.8 ± 3.0 vs 6.5 ± 2.0 %, P < 0.001), admission blood glucose (7.8 ± 3.8 vs 7.1 ± 3.5 mmol/L, P = 0.041), and discharge blood glucose (6.3 ± 1.6 vs 5.8 ± 1.2 mmol/L, P < 0.001). The dose-response relationship among admission HbA1c, blood glucose, discharge blood glucose and PSF showed that HbA1c level is positively and non-linearly related to the risk of PSF. A linear positive correlation is noted between PSF and discharge blood glucose levels, while no significant correlation was observed for the blood glucose levels upon admission. CONCLUSIONS Higher HbA1c levels at admission were independently associated with the risk of chronic PSF, the correlation between blood glucose and PSF showed significant variability, HbA1c may serve as a more stable risk factor in predicting the occurrence of chronic PSF and long-term active glycemic management may have a favorable impact on chronic PSF after AIS.
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Affiliation(s)
- Qingrong Ouyang
- Department of Neurology, Suining Central Hospital, Suining 629000, China
| | - Lei Xu
- Department of Neurology, Suining Central Hospital, Suining 629000, China
| | - Yunwei Zhang
- Department of Neurology, Suining Central Hospital, Suining 629000, China
| | - Luwen Huang
- Department of Neurology, Suining Central Hospital, Suining 629000, China
| | - Yang Du
- Department of Neurology, Suining Central Hospital, Suining 629000, China
| | - Ming Yu
- Department of Neurology, Suining Central Hospital, Suining 629000, China.
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Kuppuswamy A, Billinger S, Coupland KG, English C, Kutlubaev MA, Moseley L, Pittman QJ, Simpson DB, Sutherland BA, Wong C, Corbett D. Mechanisms of Post-Stroke Fatigue: A Follow-Up From the Third Stroke Recovery and Rehabilitation Roundtable. Neurorehabil Neural Repair 2024; 38:52-61. [PMID: 38156702 PMCID: PMC10798014 DOI: 10.1177/15459683231219266] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
BACKGROUND Post-stroke fatigue (PSF) is a significant and highly prevalent symptom, whose mechanisms are poorly understood. The third Stroke Recovery and Rehabilitation Roundtable paper on PSF focussed primarily on defining and measuring PSF while mechanisms were briefly discussed. This companion paper to the main paper is aimed at elaborating possible mechanisms of PSF. METHODS This paper reviews the available evidence that potentially explains the pathophysiology of PSF and draws parallels from fatigue literature in other conditions. We start by proposing a case for phenotyping PSF based on structural, functional, and behavioral characteristics of PSF. This is followed by discussion of a potentially significant role of early inflammation in the development of fatigue, specifically the impact of low-grade inflammation and its long-term systemic effects resulting in PSF. Of the many neurotransmitter systems in the brain, the dopaminergic systems have the most evidence for a role in PSF, along with a role in sensorimotor processing. Sensorimotor neural network dynamics are compromised as highlighted by evidence from both neurostimulation and neuromodulation studies. The double-edged sword effect of exercise on PSF provides further insight into how PSF might emerge and the importance of carefully titrating interventional paradigms. CONCLUSION The paper concludes by synthesizing the presented evidence into a unifying model of fatigue which distinguishes between factors that pre-dispose, precipitate, and perpetuate PSF. This framework will help guide new research into the biological mechanisms of PSF which is a necessary prerequisite for developing treatments to mitigate the debilitating effects of post-stroke fatigue.
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Affiliation(s)
- Annapoorna Kuppuswamy
- Queen Square Institute of Neurology, University College London, London, UK
- Department of Biomedical Sciences, University of Leeds, Leeds, UK
| | - Sandra Billinger
- Department of Neurology, University of Kansas Medical Center, University of Kansas Alzheimer’s Disease Research Center, Fairway, KS, MO, USA
| | - Kirsten G. Coupland
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Australia Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Coralie English
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Australia Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | | | - Lorimer Moseley
- IIMPACT in Health, University of South Australia, Adelaide, SA, Australia
| | - Quentin J. Pittman
- Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Dawn B. Simpson
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Australia Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Brad A. Sutherland
- Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TS, Australia
| | - Connie Wong
- Centre for Inflammatory Diseases, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Dale Corbett
- Department of Cellular and Molecular Medicine, University of Ottawa Brain and Mind Institute, University of Ottawa, Ottawa, ON, Canada
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Chen W, Jiang T, Huang H, Zeng J. Post-stroke fatigue: a review of development, prevalence, predisposing factors, measurements, and treatments. Front Neurol 2023; 14:1298915. [PMID: 38187145 PMCID: PMC10768193 DOI: 10.3389/fneur.2023.1298915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/01/2023] [Indexed: 01/09/2024] Open
Abstract
Background Post-stroke fatigue (PSF) is a ubiquitous and overwhelming symptom for most stroke survivors. However, there are no effective management strategies for PSF, which is partly due to our limited understanding. Objective In this paper, we review the development, prevalence, predisposing factors, measurements, and treatments of PSF. Results PSF is an independent symptom after stroke, with a prevalence ranging from 42 to 53%, which depends on the selection of measurement tools and stroke characteristics. It is affected by biological, physical, and psychological factors, among which inflammation may play a key role. Conclusion Numerous but non-specific evaluation measurement tools limit the management of PSF. In clinical practice, it may be beneficial to identify PSF by combining scales and objective indexes, such as walking tests and electromyographic examinations. There are no evidence-based interventions to improve PSF. However, increasing evidence suggests that transcranial direct-current stimulation and mindfulness-based interventions may become promising treatments. Further studies are urgently needed to better understand the etiology of PSF, thereby providing the basis for developing new measurement tools and targeted treatments.
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Affiliation(s)
| | - Tao Jiang
- Department of Neurology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
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Mouliou DS. C-Reactive Protein: Pathophysiology, Diagnosis, False Test Results and a Novel Diagnostic Algorithm for Clinicians. Diseases 2023; 11:132. [PMID: 37873776 PMCID: PMC10594506 DOI: 10.3390/diseases11040132] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/25/2023] Open
Abstract
The current literature provides a body of evidence on C-Reactive Protein (CRP) and its potential role in inflammation. However, most pieces of evidence are sparse and controversial. This critical state-of-the-art monography provides all the crucial data on the potential biochemical properties of the protein, along with further evidence on its potential pathobiology, both for its pentameric and monomeric forms, including information for its ligands as well as the possible function of autoantibodies against the protein. Furthermore, the current evidence on its potential utility as a biomarker of various diseases is presented, of all cardiovascular, respiratory, hepatobiliary, gastrointestinal, pancreatic, renal, gynecological, andrological, dental, oral, otorhinolaryngological, ophthalmological, dermatological, musculoskeletal, neurological, mental, splenic, thyroid conditions, as well as infections, autoimmune-supposed conditions and neoplasms, including other possible factors that have been linked with elevated concentrations of that protein. Moreover, data on molecular diagnostics on CRP are discussed, and possible etiologies of false test results are highlighted. Additionally, this review evaluates all current pieces of evidence on CRP and systemic inflammation, and highlights future goals. Finally, a novel diagnostic algorithm to carefully assess the CRP level for a precise diagnosis of a medical condition is illustrated.
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Koceniak P, Chatys-Bogacka Z, Slowik A, Dziedzic T. Reduced ex vivo TNFα synthesis upon whole blood stimulation with endotoxin predicts post-stroke fatigue. J Psychosom Res 2023; 172:111426. [PMID: 37390788 DOI: 10.1016/j.jpsychores.2023.111426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 05/23/2023] [Accepted: 06/23/2023] [Indexed: 07/02/2023]
Abstract
OBJECTIVE Fatigue is a common, debilitating syndrome after stroke. Peripheral inflammation plays a role in the pathogenesis of fatigue of different origin, but its contribution to post-stroke fatigue (PSF) remains unclear. We aimed to determine if there is any association between ex vivo synthesized and circulating cytokines, and risk of PSF. METHODS We included 174 patients with ischemic stroke. We stimulated in vitro blood taken on day 3 after stroke with endotoxin. We measured ex vivo released (TNFα, IP-10, IL-1β, IL-6, IL-8, IL-10, IL-12p70) and plasma (TNFα, IL-6, sIL-6R, IL-1Ra) cytokines. We assessed fatigue at month 3 using Fatigue Severity Scale (FSS). We used logistic regression to assess the relationship between cytokines and fatigue scores. RESULTS Compared with patients with lower fatigue at month 3 (FSS < 36), patients with higher fatigue (FSS ≥ 36) had lower endotoxin-stimulated TNFα release after 24 h (median: 429 vs 581 pg/mL, P = 0.05). Plasma TNFα tended to be higher in patients who developed fatigue (median: 0.8 vs 0.6 pg/mL, P = 0.06). Other cytokines did not differ between groups. After adjusting for pre-stroke fatigue and depressive symptoms, TNFα release <559.7 pg/mL after 24 h was associated with an increased risk of PSF (OR: 2.61, 95%CI: 1.22-5.57, P = 0.01). Plasma TNFα >0.76 pg/mL was associated with higher risk of PSF in univariable (OR: 2.41, 95%CI: 1.13-5.15, P = 0.02), but not multivariable analysis (OR: 2.41, 95%CI: 0.96-6.00, P = 0.06). CONCLUSION Reduced ex vivo TNFα synthesis upon whole blood stimulation with endotoxin in the acute phase of stroke predicted PSF.
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Affiliation(s)
- Piotr Koceniak
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | | | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Tomasz Dziedzic
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland.
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9
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Immune biomarkers are associated with poststroke fatigue at six months in patients with ischemic stroke. J Clin Neurosci 2022; 101:228-233. [DOI: 10.1016/j.jocn.2022.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/04/2022] [Accepted: 05/23/2022] [Indexed: 11/18/2022]
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10
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Cai W, Wang XF, Wei XF, Zhang JR, Hu C, Ma W, Shen WD. Does urinary metabolite signature act as a biomarker of post-stroke depression? Front Psychiatry 2022; 13:928076. [PMID: 36090365 PMCID: PMC9448878 DOI: 10.3389/fpsyt.2022.928076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/14/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND It is difficult to conduct the precise diagnosis of post-stroke depression (PSD) in clinical practice due to the complex psychopathology of depressive disorder. Several studies showed that gas chromatography-mass spectrometry (GC-MS)-identified urinary metabolite biomarkers could significantly discriminate PSD from stroke survivors. METHODS A systematic review was performed for the keywords of "urinary metabolite" and "PSD" using Medline, Cochrane Library, Embase, Web of Science, PsycINFO, Wanfang, CNKI, CBM, and VIP database from inception to 31 March 2022. RESULTS Four related studies were included in the review. Differential urinary metabolites including lactic acid, palmitic acid, azelaic acid, and tyrosine were identified in all the included studies. As a significant deviation in the metabolite biomarker panel, glyceric acid, azelaic acid, phenylalanine, palmitic acid, pseudouridine, and tyrosine were found in at least 2 included studies, which indicated good potential for the differentiation of PSD. CONCLUSION The systematic review provided evidence that differential urinary metabolites analyzed by the GC-MS-based approach might be used as a biomarker for the diagnosis and prognosis of PSD.
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Affiliation(s)
- Wa Cai
- Department of Acupuncture, Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xia-Fei Wang
- Department of Neurology, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xi-Fang Wei
- Department of Acupuncture, Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jing-Ruo Zhang
- Department of Acupuncture, Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chen Hu
- Department of Acupuncture, Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wen Ma
- Department of Acupuncture, Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wei-Dong Shen
- Department of Acupuncture, Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Analysis of Factors Affecting Post-Stroke Fatigue: An Observational, Cross-Sectional, Retrospective Chart Review Study. Healthcare (Basel) 2021; 9:healthcare9111586. [PMID: 34828631 PMCID: PMC8621383 DOI: 10.3390/healthcare9111586] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/15/2021] [Accepted: 11/17/2021] [Indexed: 12/16/2022] Open
Abstract
Post-stroke fatigue (PSF) is one of the most common emotional and mood disorders in stroke survivors. Several studies have suggested associations between PSF and various factors. However, they describe conflicting results. Therefore, this study aimed to evaluate the factors affecting PSF. We retrospectively reviewed the medical records of 178 hospitalized stroke patients. The collected data were compared between the PSF and control groups. To evaluate the association between factors and PSF, regression analysis was conducted. A total of 96 patients (53.9%) were assigned to the PSF group, and 82 patients were assigned to the control group. Age, neurological deficits, cognitive dysfunction, degree of depression, hs-CRP, and ESR differed significantly between the two groups. For both types of stroke, multiple linear regression analyses showed that degree of depression and degree of inflammation were significantly associated with PSF. Through subgroup analysis, multiple linear regression analyses showed that the degree of depression in ischemic and hemorrhagic stroke and the platelet-to-lymphocyte ratio in hemorrhagic stroke had a significant association with PSF. In conclusion, post-stroke depression and degree of inflammation could be clinically significant predictors of PSF in all types of stroke patients. However, larger, prospective studies are required to obtain more concrete results.
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Guo J, Wang J, Sun W, Liu X. The advances of post-stroke depression: 2021 update. J Neurol 2021; 269:1236-1249. [PMID: 34052887 DOI: 10.1007/s00415-021-10597-4] [Citation(s) in RCA: 108] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 05/02/2021] [Accepted: 05/04/2021] [Indexed: 12/13/2022]
Abstract
Post-stroke depression (PSD) is one of common and serious sequelae of stroke. Approximately, one in three stroke survivors suffered from depression after stroke. It heavily affected functional rehabilitation, which leaded to poor quality of life. What is worse, it is strongly associated with high mortality. In this review, we aimed to derive a comprehensive and integrated understanding of PSD according to recently published papers and previous classic articles. Based on the considerable number of studies, we found that within 2 years incidence of PSD has a range from 11 to 41%. Many factors contribute to the occurrence of PSD, including the history of depression, stroke severity, lesion location, and so on. Currently, the diagnosis of PSD is mainly based on the DSM guidelines and combined with various depression scales. Unfortunately, we lack a unified mechanism to explain PSD which mechanisms now involve dysregulation of hypothalamic-pituitary-adrenal (HPA) axis, increased inflammatory factors, decreased levels of monoamines, glutamate-mediated excitotoxicity, and abnormal neurotrophic response. At present, both pharmacotherapy and psychological therapies are employed in treating PSD. Although great advance has been made by researchers, there are still a lot of issues need to be addressed. Especially, the mechanism of PSD is not completely clear.
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Affiliation(s)
- Jianglong Guo
- Stroke Center and Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, People's Republic of China
| | - Jinjing Wang
- Department of Neurology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wen Sun
- Stroke Center and Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, People's Republic of China
| | - Xinfeng Liu
- Stroke Center and Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, People's Republic of China.
- Department of Neurology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
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Peng Q, Hou J, Wang S, Zhou F, E Y, Wang W, Huang T, Wang M, Huang S, Zhou J, Chen N, Zhang Y. Hypersensitive C-reactive protein-albumin ratio predicts symptomatic intracranial hemorrhage after endovascular therapy in acute ischemic stroke patients. BMC Neurol 2021; 21:47. [PMID: 33522912 PMCID: PMC7849085 DOI: 10.1186/s12883-021-02066-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 01/20/2021] [Indexed: 11/15/2022] Open
Abstract
Background Approximately 10% of patients would develop symptomatic intracranial hemorrhage (sICH) after endovascular therapy. The aim of our study was to explore the ability of hypersensitive C-reactive protein-albumin ratio (HAR) in predicting sICH after endovascular therapy. Methods From April 2016 to December 2018, 334 consecutive patients with anterior circulation infarction undergoing endovascular therapy were enrolled in our study. sICH was defined using Heidelberg bleeding classification after endovascular therapy. Multiple regression analysis was used to investigate the potential risk factors of sICH after endovascular therapy. We used receiver operating characteristic curve analysis and nomogram analysis to assess the overall discriminative ability of the HAR in predicting sICH after endovascular therapy. Results Among these 334 patients enrolled, 37 (11.1%) patients with anterior circulation infarction were identified with sICH after endovascular therapy. Univariate logistic regression analysis demonstrated that patients with higher levels of HAR may be inclined to develop sICH (odds ratio, 10.994; 95% confidence interval, 4.567–26.463; P = 0.001). This association remained significant even after adjustment for potential confounders. Also, a cutoff value of 0.526× 10− 3 for HAR was detected in predicting sICH (area under curve, 0.763). Furthermore, nomogram analysis also suggested that HAR was an indicator of sICH (c-index was 0.890, P< 0.001). Conclusions This study showed that high levels of HAR could predict sICH after endovascular therapy. Supplementary Information The online version contains supplementary material available at 10.1186/s12883-021-02066-2.
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Affiliation(s)
- Qiang Peng
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China
| | - Jiankang Hou
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China
| | - Siyu Wang
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China
| | - Feng Zhou
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China
| | - Yan E
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China
| | - Wei Wang
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China
| | - Ting Huang
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China
| | - Meng Wang
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China
| | - Shi Huang
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China
| | - Junshan Zhou
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China.
| | - Nihong Chen
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China. .,Department of Neurology, Nanjing Yuhua Hospital, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, Jiangsu, China.
| | - Yingdong Zhang
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, P.R. China.
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