Serum complement proteins rather than inflammatory factors is effective in predicting psychosis in individuals at clinical high risk

Zhang, Tianhong; Zeng, Jiahui; Ye, JiaYi; Gao, YuQing; Hu, YeGang; Xu, Li-Hua; Wei, Yanyan; Tang, Xiaochen; Liu, HaiChun; Chen, Tao; Li, Chunbo; Wan, Chunling; Wang, Jijun

doi:10.1038/s41398-022-02305-1

Cited by 12 publications

(13 citation statements)

References 49 publications

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“…We now observe a weak but positive relationship between A2M levels and psychosis risk, in line with previously reported differences between individuals with schizophrenia and controls 20 and a prediction model developed in a schizophrenia case-control sample. 19 In agreement with a recent study measuring complement proteins with ELISA in individuals with CHR symptoms, 42 we observe decreased levels of C5 in individuals that transitioned to psychosis.…”

Section: Discussionsupporting

confidence: 92%

Proteomic Biomarkers for the Prediction of Transition to Psychosis in Individuals at Clinical High Risk: A Multi-cohort Model Development Study

Byrne,

Healy,

Föcking

et al. 2024

Schizophrenia Bulletin

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Psychosis risk prediction is one of the leading challenges in psychiatry. Previous investigations have suggested that plasma proteomic data may be useful in accurately predicting transition to psychosis in individuals at clinical high risk (CHR). We hypothesized that an a priori-specified proteomic prediction model would have strong predictive accuracy for psychosis risk and aimed to replicate longitudinal associations between plasma proteins and transition to psychosis. This study used plasma samples from participants in 3 CHR cohorts: the North American Prodrome Longitudinal Studies 2 and 3, and the NEURAPRO randomized control trial (total n = 754). Plasma proteomic data were quantified using mass spectrometry. The primary outcome was transition to psychosis over the study follow-up period. Logistic regression models were internally validated, and optimism-corrected performance metrics derived with a bootstrap procedure. In the overall sample of CHR participants (age: 18.5, SD: 3.9; 51.9% male), 20.4% (n = 154) developed psychosis within 4.4 years. The a priori-specified model showed poor risk-prediction accuracy for the development of psychosis (C-statistic: 0.51 [95% CI: 0.50, 0.59], calibration slope: 0.45). At a group level, Complement C8B, C4B, C5, and leucine-rich α-2 glycoprotein 1 (LRG1) were associated with transition to psychosis but did not surpass correction for multiple comparisons. This study did not confirm the findings from a previous proteomic prediction model of transition from CHR to psychosis. Certain complement proteins may be weakly associated with transition at a group level. Previous findings, derived from small samples, should be interpreted with caution.

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Section: Discussionsupporting

confidence: 92%

Proteomic Biomarkers for the Prediction of Transition to Psychosis in Individuals at Clinical High Risk: A Multi-cohort Model Development Study

Byrne,

Healy,

Föcking

et al. 2024

Schizophrenia Bulletin

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“…Of the 17 included studies, 16 were prognostic model development studies [ 15 – 19 , 36 – 46 ] and one study was a prognostic model external validation study [ 47 ]. All studies were conducted in outpatients.…”

Section: Resultsmentioning

confidence: 99%

Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal

Byrne,

Mongan,

Murphy

et al. 2023

Transl Psychiatry

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Accumulating evidence suggests individuals with psychotic disorder show abnormalities in metabolic and inflammatory processes. Recently, several studies have employed blood-based predictors in models predicting transition to psychotic disorder in risk-enriched populations. A systematic review of the performance and methodology of prognostic models using blood-based biomarkers in the prediction of psychotic disorder from risk-enriched populations is warranted. Databases (PubMed, EMBASE and PsycINFO) were searched for eligible texts from 1998 to 15/05/2023, which detailed model development or validation studies. The checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was used to guide data extraction from eligible texts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the studies. A narrative synthesis of the included studies was performed. Seventeen eligible studies were identified: 16 eligible model development studies and one eligible model validation study. A wide range of biomarkers were assessed, including nucleic acids, proteins, metabolites, and lipids. The range of C-index (area under the curve) estimates reported for the models was 0.67-1.00. No studies assessed model calibration. According to PROBAST criteria, all studies were at high risk of bias in the analysis domain. While a wide range of potentially predictive biomarkers were identified in the included studies, most studies did not account for overfitting in model performance estimates, no studies assessed calibration, and all models were at high risk of bias according to PROBAST criteria. External validation of the models is needed to provide more accurate estimates of their performance. Future studies which follow the latest available methodological and reporting guidelines and adopt strategies to accommodate required sample sizes for model development or validation will clarify the value of including blood-based biomarkers in models predicting psychosis.

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“…While model coefficients were not available in Perkins et al [36], the two studies presented the same direction of effect for IL-8 and opposite directions of effect for Factor VII in univariate analyses. Zhang et al [46] and Mondelli et al [42] did not find evidence for the predictive ability of IL-8 for transition to psychotic disorder. Zhang et al 2022 [44], Zhang et al 2023a [45] and Mondelli et al [42] all included different cytokine ratios in their models (IL-1β/IL-6, IL-2/IL-6 and IL-10/IL-6 respectively) with the aim of capturing inflammatory balance.…”

Section: Resultsmentioning

confidence: 99%

“…All studies were conducted in outpatients. There were five models developed in the Shanghai At Risk for Psychosis (SHARP) study [18,19] and the extension of that study [44][45][46]. There were three model development studies each for the North American Prodrome Longitudinal Study (NAPLS 2) cohort [36,38,41] and for the EU Gene-Environment Interaction (EU-GEI) study [15,16,42].…”

Section: Study Characteristicsmentioning

confidence: 99%

Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal

Byrne,

Mongan,

Murphy

et al. 2023

Preprint

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BackgroundAccumulating evidence suggests individuals with psychotic disorder show abnormalities in metabolic and inflammatory processes. Recently, several studies have employed blood-based predictors in models predicting transition to psychotic disorder in risk-enriched populations. A systematic review of the performance and methodology of prognostic models using blood-based biomarkers in the prediction of psychotic disorder from risk-enriched populations is warranted.MethodsDatabases (PubMed, EMBASE and PsycINFO) were searched for eligible texts from 1998 to 15/05/2023 which detailed model development or validation studies. The checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was used to guide data extraction from eligible texts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. A narrative synthesis of included studies was performed.Results17 eligible studies were identified: 16 eligible model development studies and one eligible model validation study. A wide range of biomarkers were assessed including nucleic acids, proteins, metabolites and lipids. The range of C-index (area under the curve) estimates reported for the models was 0.67-1.00. No studies assessed model calibration. According to PROBAST criteria, all studies were at high risk of bias in the analysis domain.DiscussionWhile a wide range of potentially predictive biomarkers were identified in the included studies, most studies did not account for overfitting in model performance estimates, no studies assessed calibration, and all models were at high risk of bias according to PROBAST criteria. External validation of the models is needed to provide more accurate estimates of their performance. Future studies which follow the latest available methodological and reporting guidelines and adopt strategies to accommodate required sample sizes for model development or validation will clarify the value of including blood-based biomarkers in models predicting psychosis.

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Serum complement proteins rather than inflammatory factors is effective in predicting psychosis in individuals at clinical high risk

Cited by 12 publications

References 49 publications

Proteomic Biomarkers for the Prediction of Transition to Psychosis in Individuals at Clinical High Risk: A Multi-cohort Model Development Study

Proteomic Biomarkers for the Prediction of Transition to Psychosis in Individuals at Clinical High Risk: A Multi-cohort Model Development Study

Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal

Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal

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