Molecular Signatures of Early-Onset Bipolar Disorder and Schizophrenia: Transcriptomic and Machine-Learning Insights...

Area of Science:

• Neuroscience
• Genetics
• Psychiatry

Background:

• Early age of onset in Bipolar Disorder (BD) and Schizophrenia (SCZ) predicts a more severe disease course.
• Biological mechanisms underlying sex- and age-specific vulnerability in early-onset psychiatric disorders remain poorly understood.
• This gap hinders the development of early identification and intervention strategies.

Purpose of the Study:

• To investigate the transcriptomic underpinnings of early age of onset in BD and SCZ.
• To identify biological pathways and potential biomarkers associated with early psychiatric vulnerability.
• To explore sex-specific differences in these mechanisms.

Main Methods:

• Integrative transcriptomic analysis of 369 postmortem dorsolateral prefrontal cortex samples.
• Differential gene expression, Weighted Gene Co-Expression Network Analysis (WGCNA), and gene set enrichment analysis.
• Machine learning framework with Lasso feature selection and L2-regularized logistic classification for predictive modeling.

Main Results:

• Genes and modules associated with earlier onset were enriched in calcium signaling pathways.
• Downregulation of CACNA1C and adenylate-cyclase-related transcripts observed.
• Female-specific analyses identified dysregulation in cyclase-associated pathways; a calcium-enriched module linked to onset and sex was found.
• A predictive model achieved an AUC of 0.63, with top features enriched in calcium signaling.

Conclusions:

• Calcium-cAMP signaling networks are implicated as critical factors in early psychiatric vulnerability.
• Findings suggest potential biomarkers for precision-targeted interventions in early-onset BD and SCZ.
• The study highlights the importance of integrating transcriptomic and machine learning approaches to unravel complex psychiatric disorders.