Advances in high-throughput technologies have enabled the large-scale generation of biological data across multiple omics layers, including genomics, transcriptomics, proteomics, epigenomics, and metabolomics. Integrating these data types is an emerging strategy to investigate the complex biological mechanisms underlying human diseases, including neurodevelopmental, neurodegenerative, and psychiatric disorders. Despite strong evidence for sex differences in brain development, disease prevalence, and treatment response, the extent to which sex is systematically incorporated into multi-omics data integration remains unclear. A systematic literature search was conducted across five databases, with no restrictions on publication date or language. The search strategy included broad terms related to multi-omics data integration and the neuroscience field. Studies were included if they applied integrative analyses of two or more omics layers. All records were screened by two independent investigators, and disagreements were resolved by a third investigator. A total of 156 studies met the inclusion criteria. Among these, only 57 explicitly reported including data from both males and females, and just 7 performed sex-stratified analyses. When conducted, they revealed female-specific genetic associations, sex-dependent epigenetic changes, and distinct transcriptomic and metabolomic profiles, particularly in Alzheimer’s Disease, which was the most frequently studied condition. Depressive disorders represented the largest non-neurodegenerative studied condition, while Parkinson’s Disease was the second most studied neurodegenerative disorder and ranked third overall. Methodologically, the field is largely dominated by studies integrating only two omics layers. The application of advanced machine learning approaches capable of integrating more than two omics layers remains limited, highlighting a gap in the field. Despite the expansion of multi-omics approaches in neuroscience, the integration of sex as a biological variable remains limited. The lack of sex-specific analyses represents a missed opportunity to gain a deeper understanding of disease mechanisms. Future studies should prioritize transparent reporting of sex and incorporate sex-stratified analyses.
Behrens et al. (Wed,) studied this question.