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Zhang T, Xu L, Wei Y, Cui H, Tang X, Hu Y, Tang Y, Wang Z, Liu H, Chen T, Li C, Wang J. Advancements and Future Directions in Prevention Based on Evaluation for Individuals With Clinical High Risk of Psychosis: Insights From the SHARP Study. Schizophr Bull 2025; 51:343-351. [PMID: 38741342 PMCID: PMC11908854 DOI: 10.1093/schbul/sbae066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
BACKGROUND AND HYPOTHESIS This review examines the evolution and future prospects of prevention based on evaluation (PBE) for individuals at clinical high risk (CHR) of psychosis, drawing insights from the SHARP (Shanghai At Risk for Psychosis) study. It aims to assess the effectiveness of non-pharmacological interventions in preventing psychosis onset among CHR individuals. STUDY DESIGN The review provides an overview of the developmental history of the SHARP study and its contributions to understanding the needs of CHR individuals. It explores the limitations of traditional antipsychotic approaches and introduces PBE as a promising framework for intervention. STUDY RESULTS Three key interventions implemented by the SHARP team are discussed: nutritional supplementation based on niacin skin response blunting, precision transcranial magnetic stimulation targeting cognitive and brain functional abnormalities, and cognitive behavioral therapy for psychotic symptoms addressing symptomatology and impaired insight characteristics. Each intervention is evaluated within the context of PBE, emphasizing the potential for tailored approaches to CHR individuals. CONCLUSIONS The review highlights the strengths and clinical applications of the discussed interventions, underscoring their potential to revolutionize preventive care for CHR individuals. It also provides insights into future directions for PBE in CHR populations, including efforts to expand evaluation techniques and enhance precision in interventions.
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Affiliation(s)
- TianHong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - LiHua Xu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YanYan Wei
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - HuiRu Cui
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - XiaoChen Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YeGang Hu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YingYing Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - ZiXuan Wang
- Department of Psychology, Shanghai Xinlianxin Psychological Counseling Center, Shanghai, China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Ontario, Canada
- Labor and Worklife Program, Harvard University, Cambridge, MA, USA
| | - ChunBo Li
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - JiJun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
- Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, PR China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
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Zhang T, Wei Y, Tang X, Xu L, Hu Y, Liu H, Wang Z, Chen T, Li C, Wang J. Timeframe for Conversion to Psychosis From Individuals at Clinical High-Risk: A Quantile Regression. Schizophr Bull 2024:sbae129. [PMID: 39054751 DOI: 10.1093/schbul/sbae129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
BACKGROUND AND HYPOTHESIS The time taken for an individual who is at the clinical high-risk (CHR) stage to transition to full-blown psychosis may vary from months to years. This temporal aspect, known as the timeframe for conversion to psychosis (TCP), is a crucial but relatively underexplored dimension of psychosis development. STUDY DESIGN The sample consisted of 145 individuals with CHR who completed a 5-year follow-up with a confirmed transition to psychosis within this period. Clinical variables along with functional variables such as the Global Assessment of Function (GAF) score at baseline (GAF baseline) and GAF-drop from the highest score in the past year. The TCP was defined as the duration from CHR identification to psychosis conversion. Participants were categorized into 3 groups based on TCP: "short" (≤6 months, ≤33.3%), "median" (7-17 months, 33.3%-66.6%), and "long" (≥18 months, ≥66.6%). The quantile regression analysis was applied. STUDY RESULTS The overall sample had a median TCP of 11 months. Significant differences among the three TCP groups were observed, particularly in GAF-drop (χ2 = 8.806, P = .012), disorganized symptoms (χ2 = 7.071, P = .029), and general symptoms (χ2 = 6.586, P = .037). Greater disorganized symptoms (odds ratio [OR] = 0.824, P = .009) and GAF-drop (OR = 0.867, P = .011) were significantly associated with a shorter TCP, whereas greater general symptoms (OR = 1.198, P = .012) predicted a longer TCP. Quantile regression analysis demonstrated a positive association between TCP and GAF baseline above the 0.7 quantile and a negative association between TCP rank and GAF drop below the 0.5 quantile. CONCLUSIONS This study underscores the pivotal role of functional characteristics in shaping TCP among individuals with CHR, emphasizing the necessity for a comprehensive consideration of temporal aspects in early prevention efforts.
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Affiliation(s)
- TianHong Zhang
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - YanYan Wei
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - XiaoChen Tang
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - LiHua Xu
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - YeGang Hu
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - ZiXuan Wang
- Department of Psychology, Shanghai Xinlianxin Psychological Counseling Center, Shanghai, PR China
| | - Tao Chen
- Department of Big Data Research Lab, University of Waterloo, Ontario, Canada
- Department of Labor and Worklife Program, Harvard University, Cambridge, MA, USA
| | - ChunBo Li
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
| | - JiJun Wang
- Department of Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, PR China
- Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, PR China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
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Cao H, Ding A, Wang L, Cao J, Mao H, Tang H, Yang G, Gu J. Factors influencing ruminative thinking behaviours in nurses: a cross-sectional study of 858 subjects in a tertiary care hospital. Gen Psychiatr 2024; 37:e101443. [PMID: 39006242 PMCID: PMC11243117 DOI: 10.1136/gpsych-2023-101443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 05/31/2024] [Indexed: 07/16/2024] Open
Affiliation(s)
- Huling Cao
- Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Aiming Ding
- Department of Nursing, Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Lihua Wang
- Department of Nursing, Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Jianyu Cao
- Youjiang Medical University for Nationalities, Baise, Guangxi, China
| | - Haiyan Mao
- Department of Pediatrics, Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Hui Tang
- Department of Pediatrics, Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Guihong Yang
- Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Junhua Gu
- Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
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Zhang T, Cui H, Tang X, Xu L, Wei Y, Hu Y, Tang Y, Wang Z, Liu H, Chen T, Li C, Wang J. Models of mild cognitive deficits in risk assessment in early psychosis. Psychol Med 2024; 54:2230-2241. [PMID: 38433595 DOI: 10.1017/s0033291724000382] [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] [Indexed: 03/05/2024]
Abstract
BACKGROUND Mild cognitive deficits (MCD) emerge before the first episode of psychosis (FEP) and persist in the clinical high-risk (CHR) stage. This study aims to refine risk prediction by developing MCD models optimized for specific early psychosis stages and target populations. METHODS A comprehensive neuropsychological battery assessed 1059 individuals with FEP, 794 CHR, and 774 matched healthy controls (HCs). CHR subjects, followed up for 2 years, were categorized into converters (CHR-C) and non-converters (CHR-NC). The MATRICS Consensus Cognitive Battery standardized neurocognitive tests were employed. RESULTS Both the CHR and FEP groups exhibited significantly poorer performance compared to the HC group across all neurocognitive tests (all p < 0.001). The CHR-C group demonstrated poorer performance compared to the CHR-NC group on three sub-tests: visuospatial memory (p < 0.001), mazes (p = 0.005), and symbol coding (p = 0.023) tests. Upon adjusting for sex and age, the performance of the MCD model was excellent in differentiating FEP from HC, as evidenced by an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.895 (p < 0.001). However, when applied in the CHR group for predicting CHR-C (AUC = 0.581, p = 0.008), the performance was not satisfactory. To optimize the efficiency of psychotic risk assessment, three distinct MCD models were developed to distinguish FEP from HC, predict CHR-C from CHR-NC, and identify CHR from HC, achieving accuracies of 89.3%, 65.6%, and 80.2%, respectively. CONCLUSIONS The MCD exhibits variations in domains, patterns, and weights across different stages of early psychosis and diverse target populations. Emphasizing precise risk assessment, our findings highlight the importance of tailored MCD models for different stages and risk levels.
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Affiliation(s)
- TianHong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - HuiRu Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - XiaoChen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - LiHua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - YanYan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - YeGang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - YingYing Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - ZiXuan Wang
- Shanghai Xinlianxin Psychological Counseling Co., Ltd, Shanghai, China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada
- Labor and Worklife Program, Harvard University, Cambridge, MA, USA
| | - ChunBo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - JiJun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
- Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, People's Republic of China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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Zhang T, Cui H, Wei Y, Tang X, Xu L, Hu Y, Tang Y, Liu H, Wang Z, Chen T, Li C, Wang J. Duration of Untreated Prodromal Psychosis and Cognitive Impairments. JAMA Netw Open 2024; 7:e2353426. [PMID: 38277145 PMCID: PMC10818213 DOI: 10.1001/jamanetworkopen.2023.53426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/04/2023] [Indexed: 01/27/2024] Open
Abstract
Importance The possible association between the duration of untreated prodromal symptoms (DUPrS) and cognitive functioning in individuals at clinical high risk (CHR) for psychosis remains underexplored. Objective To investigate the intricate interplay between DUPrS, cognitive performance, and conversion outcomes, shedding light on the potential role of DUPrS in shaping cognitive trajectories and psychosis risk in individuals at CHR for psychosis. Design, Setting, and Participants This cohort study of individuals at CHR for psychosis was conducted at the Shanghai Mental Health Center in China from January 10, 2016, to December 29, 2021. Participants at CHR for psychosis typically exhibit attenuated positive symptoms; they were identified according to the Structured Interview for Prodromal Syndromes, underwent baseline neuropsychological assessments, and were evaluated at a 3-year clinical follow-up. Data were analyzed from August 25, 2021, to May 10, 2023. Exposure Duration of untreated prodromal symptoms and cognitive impairments in individuals at CHR for psychosis. Main Outcomes and Measures The primary study outcome was conversion to psychosis. The DUPrS was categorized into 3 groups based on percentiles (33rd percentile for short [≤3 months], 34th-66th percentile for median [4-9 months], and 67th-100th percentile for long [≥10 months]). The DUPrS, cognitive variables, and the risk of conversion to psychosis were explored through quantile regression and Cox proportional hazards regression analyses. Results This study included 506 individuals (median age, 19 [IQR, 16-21] years; 53.6% [n = 271] women). The mean (SD) DUPrS was 7.8 (6.857) months, and the median (IQR) was 6 (3-11) months. The short and median DUPrS groups displayed poorer cognitive performance than the long DUPrS group in the Brief Visuospatial Memory Test-Revised (BVMT-R) (Kruskal-Wallis χ2 = 8.801; P = .01) and Category Fluency Test (CFT) (Kruskal-Wallis χ2 = 6.670; P = .04). Quantile regression analysis revealed positive correlations between DUPrS rank and BVMT-R scores (<90th percentile of DUPrS rank) and CFT scores (within the 20th-70th percentile range of DUPrS rank). Among the 506 participants, 20.8% (95% CI, 17.4%-24.5%) converted to psychosis within 3 years. Cox proportional hazards regression analysis identified lower educational attainment (hazard ratio [HR], 0.912; 95% CI, 0.834-0.998), pronounced negative symptoms (HR, 1.044; 95% CI, 1.005-1.084), and impaired performance on the Neuropsychological Assessment Battery: Mazes (HR, 0.961; 95% CI, 0.924-0.999) and BVMT-R (HR, 0.949; 95% CI, 0.916-0.984) tests as factors associated with conversion. Conclusions and Relevance The finding of this cohort study suggest the intricate interplay between DUPrS, cognitive performance, and conversion risk in individuals at CHR for psychosis. The findings emphasize the importance of considering both DUPrS and cognitive functioning in assessing the trajectory of these individuals.
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Affiliation(s)
- TianHong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - HuiRu Cui
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YanYan Wei
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - XiaoChen Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - LiHua Xu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YeGang Hu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YingYing Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - ZiXuan Wang
- Shanghai Xinlianxin Psychological Counseling Co Ltd, Shanghai, PR China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada
- Labor and Worklife Program, Harvard University, Cambridge, Massachusetts
| | - ChunBo Li
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - JiJun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, PR China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
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Orfei MD, Porcari DE, D’Arcangelo S, Maggi F, Russignaga D, Ricciardi E. A New Look on Long-COVID Effects: The Functional Brain Fog Syndrome. J Clin Med 2022; 11:5529. [PMID: 36233392 PMCID: PMC9573330 DOI: 10.3390/jcm11195529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/14/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
Epidemiological data and etiopathogenesis of brain fog are very heterogeneous in the literature, preventing adequate diagnosis and treatment. Our study aimed to explore the relationship between brain fog, neuropsychiatric and cognitive symptoms in the general population. A sample of 441 subjects underwent a web-based survey, including the PANAS, the DASS-21, the IES-R, the Beck Cognitive Insight Scale, and a questionnaire investigating demographic information, brain fog, subjective cognitive impairments (Scc) and sleep disorders. ANOVA, ANCOVA, correlation and multiple stepwise regression analyses were performed. In our sample, 33% of participants were defined as Healthy Subjects (HS; no brain fog, no Scc), 27% as Probable Brain Fog (PBF; brain fog or Scc), and 40% as Functional Brain Fog (FBF; brain fog plus Scc). PBF and FBF showed higher levels of neuropsychiatric symptoms than HS, and FBF showed the worst psychological outcome. Moreover, worse cognitive symptoms were related to the female gender, greater neuropsychiatric symptoms, sleep disorders, and rumination/indecision. Being a woman and more severe neuropsychiatric symptoms were predictors of FBF severity. Our data pointed out a high prevalence and various levels of severity and impairments of brain fog, suggesting a classificatory proposal and a multifaceted etiopathogenic model, thus facilitating adequate diagnostic and therapeutic approaches.
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Affiliation(s)
- Maria Donata Orfei
- Molecular Mind Laboratory (MoMiLab), IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy
| | - Desirée Estela Porcari
- Molecular Mind Laboratory (MoMiLab), IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy
| | - Sonia D’Arcangelo
- Intesa Sanpaolo Innovation Center SpA Neuroscience Lab, Via Inghilterra 3, 10138 Turin, Italy
| | - Francesca Maggi
- Intesa Sanpaolo Innovation Center SpA Neuroscience Lab, Via Inghilterra 3, 10138 Turin, Italy
| | - Dario Russignaga
- Intesa Sanpaolo S.p.A., HSE Office, Via Lorenteggio 266, 20152 Milan, Italy
| | - Emiliano Ricciardi
- Molecular Mind Laboratory (MoMiLab), IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy
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