Routine cerebrospinal fluid parameters as biomarkers in first-episode psychosis: A prospective observational study

Giné-Serven, Eloi; Martínez-Ramírez, M.J.; Boix-Quintana, Ester; Davi-Loscos, Eva; Guanyabens, Nicolau; Casado, Virginia; Muriana, Desirée; Torres-Rivas, Cristina; Crespo‐Facorro, Benedicto; Labad, Javier

doi:10.1016/j.pnpbp.2021.110424

Cited by 15 publications

(19 citation statements)

References 43 publications

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“…We reviewed 29 studies that reported on findings we further organized into 10 classes of potential CSF bio-patients, covering both non-affective and affective psychoses. Among these, only one [34] analyzed the differences between the two subgroups, finding significant intergroup differences and confirming earlier observations [31]. Future studies should either include homogenous populations or analyze differences between subgroups in heterogeneous ones.…”

Section: Discussionsupporting

confidence: 80%

“…In addition, one study comparing non-affective pFEP with recurrent/chronic psychoses found no significant group differences in WBC count, protein level, or OCBs [27]. Moreover, Giné-Servén et al [34] found that protein levels were more frequently elevated in affective vs. non-affective pFEP, confirming earlier observations of Endres et al [31]. In the largest available non-affective pFEP cohort to date, Campana et al [24] found OCBs in 21.8% of patients, with 12.4% of them presenting OCBs type 2 or 3.…”

Section: Inflammatory Biomarkersmentioning

confidence: 96%

“…While altered neurotransmission is consistently reported in pFEP, other studies hint to various disrupted metabolic pathways. In a study analyzing routine CSF parameters in a large pFEP cohort, Giné-Servén et al [34] observed a correlation between lower lactate dehydrogenase levels and prodromal symptoms along with social withdrawal. Moreover, they also found that higher glucose levels were associated with depressive symptoms and disorganized/concrete symptoms, while lower levels predicted a schizophrenia spectrum diagnosis at follow-up.…”

Section: Metabolomic Biomarkersmentioning

confidence: 99%

“…Moreover, they also found that higher glucose levels were associated with depressive symptoms and disorganized/concrete symptoms, while lower levels predicted a schizophrenia spectrum diagnosis at follow-up. Thus, Giné-Servén et al [34] conclude that biomarkers involving bioenergetic systems are associated with both prodromal symptoms and phenotype of psychotic disorders during the early disease stages. Another study conducted by Steiner et al [39] found significantly higher levels of the proinflammatory S100B protein in FES patients.…”

Section: Metabolomic Biomarkersmentioning

confidence: 99%

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The Importance of Cerebrospinal Fluid Investigation in First-episode Psychosis

Pavǎl

Gherghel-Pavăl²,

Căpățînă

et al. 2023

Yale J Biol Med

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Despite being a reliable first-hand source of data on neuronal pathology, cerebrospinal fluid (CSF) analysis remains an often-overlooked evaluation method in first-episode psychosis (FEP). In this paper, we begin by discussing the current role of CSF testing during FEP evaluation in clinical practice. Given that anti-N-methyl-D-aspartate receptor encephalitis presents with a clinical picture indistinguishable from FEP in >85% of cases, we debate the importance of testing for CSF neuronal antibodies in at least a subset of patients. Then, we continue by reviewing the most important recent studies which sought to identify potential CSF biomarkers in FEP caused by a primary psychiatric disorder. By circumventing traditional psychiatric classifications, characteristic biomarker profiles have the potential to become integral components of early diagnosis, disease stratification, treatment choice, and outcome prediction. Along these lines, we aim to provide an updated perspective on the importance of CSF investigation in FEP.

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Section: Discussionsupporting

confidence: 80%

Section: Inflammatory Biomarkersmentioning

confidence: 96%

Section: Metabolomic Biomarkersmentioning

confidence: 99%

Section: Metabolomic Biomarkersmentioning

confidence: 99%

See 2 more Smart Citations

The Importance of Cerebrospinal Fluid Investigation in First-episode Psychosis

Pavǎl

Gherghel-Pavăl²,

Căpățînă

et al. 2023

Yale J Biol Med

View full text Add to dashboard Cite

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“…In most scenarios, LDH is elevated, and the underlying mechanism has been well elucidated. However, it had been claimed that the decrease of LDH had no clinical significance until it was found to decrease in psychological depression ( 33 ). There still has been reported that LDH might be dropped within people who presented the depressive symptoms when working in stressful environments ( 34 ).…”

Section: Discussionmentioning

confidence: 99%

A Retrospective and Multicenter Study on COVID-19 in Inner Mongolia: Evaluating the Influence of Sampling Locations on Nucleic Acid Test and the Dynamics of Clinical and Prognostic Indexes

Lan

Wang

et al. 2022

Front. Med.

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COVID-19 is spreading widely, and the pandemic is seriously threatening public health throughout the world. A comprehensive study on the optimal sampling types and timing for an efficient SARS-CoV-2 test has not been reported. We collected clinical information and the values of 55 biochemical indices for 237 COVID-19 patients, with 37 matched non-COVID-19 pneumonia patients and 131 healthy people in Inner Mongolia as control. In addition, the results of dynamic detection of SARS-CoV-2 using oropharynx swab, pharynx swab, and feces were collected from 197 COVID-19 patients. SARS-CoV-2 RNA positive in feces specimen was present in approximately one-third of COVID-19 patients. The positive detection rate of SARS-CoV-2 RNA in feces was significantly higher than both in the oropharynx and nasopharynx swab (P < 0.05) in the late period of the disease, which is not the case in the early period of the disease. There were statistically significant differences in the levels of blood LDH, CRP, platelet count, neutrophilic granulocyte count, white blood cell number, and lymphocyte count between COVID-19 and non-COVID-19 pneumonia patients. Finally, we developed and compared five machine-learning models to predict the prognosis of COVID-19 patients based on biochemical indices at disease onset and demographic characteristics. The best model achieved an area under the curve of 0.853 in the 10-fold cross-validation.

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