Genes Associated with Altered Brain Structure and Function in Obstructive Sleep Apnea

Exploring the shared genetic basis of attention-deficit/hyperactivity disorder and obstructive sleep apnea: A multi-omics analysis

BACKGROUND

Observational studies have suggested an association between attention-deficit/hyperactivity disorder (ADHD) and obstructive sleep apnea (OSA), but these findings are often inconsistent due to potential biases from medication use, and varying diagnostic criteria. Genetic analyses can help mitigate these confounding factors, providing additional evidence.

METHODS

This study evaluated the genetic correlations between ADHD and OSA using Genome-wide association study (GWAS) summary data, applying linkage disequilibrium score regression (LDSC) and SUPER GeNetic cOVariance Analyzer (SUPERGNOVA). Cross-trait association and colocalization analysis identify potential pleiotropic loci. Tissue enrichment analysis and gene-level analysis of shared genes between OSA and ADHD was conducted. Additionally, bidirectional Mendelian randomization was used to assess potential causal relationships.

RESULTS

We found significant genetic correlations between ADHD and OSA (rg = 0.309, p = 3.252E-27), and identified 8 novel pleiotropic loci through cross-trait association analysis. Tissue enrichment analysis showed that these shared genes were primarily concentrated in brain tissues, particularly in deep gray matter regions, and were associated with immune and inflammatory pathways. Forward Mendelian Randomization analysis showed that ADHD was significantly associated with the risk of OSA (OR 1.070, 95 % CI 1.013-1.130, p = 0.016), and reverse analysis showed that OSA was significantly associated with the risk of ADHD (OR 1.240, 95 % CI 1.106-1.390, p = 2.213E-4).

CONCLUSION

The findings of this study show a significant positive genetic correlation between ADHD and OSA and each is a risk factor for the other. Inflammation in specific brain regions may be the underlying mechanism for their comorbidity.

Brain Imaging and Cognitive Deficits in Psychiatric Disorders

Over the last three decades, various neuroimaging techniques, such as magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), have played an essential role in improving our understanding of the pathogenesis of neuropsychiatric disorders [...].

Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals

Obstructive sleep apnea (OSA) is a disorder which blocks the upper airway during sleep. The severity of OSA will lead heart attack, stroke and end of life. This proposed study explored the classification of OSA and healthy subjects using brain connectivity analysis from electroencephalogram (EEG) signals. Institute of System and Robotics-University of Coimbra (ISRUC) database were used for acquiring 50 EEG signals using 4 channels and noise removal has been accomplished by 50 Hz notch filter. The Institute of System and Robotics-University of Coimbra (ISRUC) database contained 50 EEG signals, with four channels, and a 50 Hz notch filter was applied to remove noise. Wavelet packet decomposition method was performing the segregation of EEG signals into five bands; Gamma (γ), beta (β), alpha (α), theta (θ) and delta (δ). A total of 4 electrode positions were used for the brain connectivity analysis for each EEG band. Pearson correlation method was effectively used for measuring the correlation between healthy and OSA subjects. The nodes and edges were highlighted the connection between brain and subjects. The highest correlation was achieved in delta band of OSA subjects which starts from 0.7331 to 0.9172 respectively. For healthy subjects, the positive correlation achieved was 0.6995. The delta band has been correlated well with brain when compared other bands. It has been noted that the positive correlation well associated with brain in OSA subjects, which classifies OSA from healthy subjects.