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Abstract Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.

Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics., (© 2024. The Author(s).)

Objective: To examine cold (based on logical reasoning) versus hot (having emotional components) executive function processes in groups with high individual schizotypal traits., Method: Two-hundred and forty-seven participants were administered the Schizotypal Personality Questionnaire and were allocated into schizotypal (cognitive-perceptual, paranoid, negative, disorganized) or control groups according to pre-specified criteria. Participants were also administered a battery of tasks examining working memory, complex selective attention, response inhibition, decision-making and fluid intelligence and their affective counterparts. The outcome measures of each task were reduced to one composite variable thus formulating five cold and five hot cognitive domains. Between-group differences in the cognitive domains were examined with repeated measures analyses of covariance., Results: For working memory, the control and the cognitive-perceptual groups outperformed negative schizotypes, while for affective working memory controls outperformed the disorganized group. Controls also scored higher compared with the disorganized group in complex selective attention, while both the control and the cognitive-perceptual groups outperformed negative schizotypes in complex affective selective attention. Negative schizotypes also had striking difficulties in response inhibition, as they scored lower compared with all other groups. Despite the lack of differences in fluid intelligence, controls scored higher compared with all schizotypal groups (except from cognitive-perceptual schizotypes) in emotional intelligence; the latter group reported higher emotional intelligence compared with negative schizotypes., Conclusion: Results indicate that there is no categorical association between the different schizotypal dimensions with solely cold or hot executive function processes and support impoverished emotional intelligence as a core feature of schizotypy.