An analysis of the potential association between obstructive sleep apnea and osteoporosis from the perspective of transcriptomics and NHANES

• Published in: BMC public health 2024-07, Vol.24 (1), p.1998-13, Article 1998
• Main Authors: Li, Shuzhen, Zan, Yuxin, Li, Fangzhou, Dai, Wenjing, Yang, Liting, Yang, Ruiping, He, Xuejun, Li, Bei
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Apnea; Biomarkers; Body mass index; Body size; Bone density; Bone mineral density; Comorbidity; Complications and side effects; Datasets; Decision trees; Demographic aspects; Disease; Female; Females; Fractures; Gender; Gene expression; Gene Expression Profiling; Genes; Genetic aspects; Genomics; Go/no-go discrimination learning; Humans; Immune system; Machine learning; Male; Metabolism; Middle Aged; NHANES; Nutrition Surveys; Obstructive sleep apnea; Osteoporosis; Osteoporosis - epidemiology; Osteoporosis - genetics; Pathophysiology; Regression analysis; Risk analysis; Risk Factors; Sleep apnea; Sleep apnea syndromes; Sleep Apnea, Obstructive - epidemiology; Sleep Apnea, Obstructive - genetics; Sleep disorders; Transcriptome; Transcriptomics
• ISSN: 1471-2458; 1471-2458
• DOI: 10.1186/s12889-024-19540-4

Obstructive sleep apnea (OSA) and osteoporosis (OP) are prevalent diseases in the elderly. This study aims to reveal the clinical association between OSA and OP and explore potential crosstalk gene targets. Participants diagnosed with OSA in the National Health and Nutrition Examination Survey (NHANES) database (2015-2020) were included, and OP was diagnosed based on bone mineral density (BMD). We explored the association between OSA and OP, and utilized multivariate logistic regression analysis and machine learning algorithms to explore the risk factors for OP in OSA patients. Overlapping genes of comorbidity were explored using differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Random Forest (RF) methods. In the OSA population, the weighted prevalence of OP was 7.0%. The OP group had more females, lower body mass index (BMI), and more low/middle-income individuals compared to the non-OP group. Female gender and lower BMI were identified as independent risk factors for OP in OSA patients. Gene expression profiling revealed 8 overlapping differentially expressed genes in OP and OSA patients. KCNJ1, NPR3 and WT1-AS were identified as shared diagnostic biomarkers or OSA and OP, all of which are associated with immune cell infiltration. This study pinpointed female gender and lower BMI as OP risk factors in OSA patients, and uncovered three pivotal genes linked to OSA and OP comorbidity, offering fresh perspectives and research targets.