Obstructive Sleep Apnea (OSA) is a prevalent chronic respiratory disorder affecting nearly 1 billion people worldwide. Certain countries show a particularly high prevalence rate which exceeds 50%. Studies have established a strong correlation between OSA and a 2-3-fold increased risk of cardiovascular and metabolic diseases. Common complications associated with OSA include hypertension, coronary heart disease, diabetes, heart failure, and cognitive impairment. Despite the significant impact of OSA, therapeutic interventions remain limited, and a definitive pharmacological treatment remains elusive. Hence, it is critical to identify novel biomarkers and develop novel therapeutic targets.
Recent studies highlighted the significance of bioinformatics analysis in advancing our understanding of OSA and its underlying mechanisms. By utilizing computational tools and analyzing large-scale data sets, researchers can gain insights into the key candidate genes and signaling pathways of OSA, leading to improved diagnostic accuracy, early detection, and effective treatment approaches. Furthermore, bioinformatics analysis has empowered researchers to explore the genetic basis of OSA through approaches such as genome-wide association studies (GWAS).
This Research Topic aims to bridge the gap between basic research on OSA and its application in clinical practice. We strive to disseminate scientific knowledge, share impactful discoveries, and advance awareness of OSA and its hazards. Our goal is to drive progress in OSA diagnosis and treatment decision-making.
The following article types are welcome to be submitted for this Research Topic: Brief Research Report, Case Report, Clinical Trial, Data Report, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Study Protocol, and Systematic Review. Specific topics of interest include but are not limited to the following.
? Latest research progress of the pathophysiology of OSA
? OSA-related diseases and complications
? Screening and identification of potential biomarkers for OSA Detection via bioinformatics analysis
? Multidisciplinary approach based on bioinformatics towards the treatment of OSA
Obstructive Sleep Apnea (OSA) is a prevalent chronic respiratory disorder affecting nearly 1 billion people worldwide. Certain countries show a particularly high prevalence rate which exceeds 50%. Studies have established a strong correlation between OSA and a 2-3-fold increased risk of cardiovascular and metabolic diseases. Common complications associated with OSA include hypertension, coronary heart disease, diabetes, heart failure, and cognitive impairment. Despite the significant impact of OSA, therapeutic interventions remain limited, and a definitive pharmacological treatment remains elusive. Hence, it is critical to identify novel biomarkers and develop novel therapeutic targets.
Recent studies highlighted the significance of bioinformatics analysis in advancing our understanding of OSA and its underlying mechanisms. By utilizing computational tools and analyzing large-scale data sets, researchers can gain insights into the key candidate genes and signaling pathways of OSA, leading to improved diagnostic accuracy, early detection, and effective treatment approaches. Furthermore, bioinformatics analysis has empowered researchers to explore the genetic basis of OSA through approaches such as genome-wide association studies (GWAS).
This Research Topic aims to bridge the gap between basic research on OSA and its application in clinical practice. We strive to disseminate scientific knowledge, share impactful discoveries, and advance awareness of OSA and its hazards. Our goal is to drive progress in OSA diagnosis and treatment decision-making.
The following article types are welcome to be submitted for this Research Topic: Brief Research Report, Case Report, Clinical Trial, Data Report, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Study Protocol, and Systematic Review. Specific topics of interest include but are not limited to the following.
? Latest research progress of the pathophysiology of OSA
? OSA-related diseases and complications
? Screening and identification of potential biomarkers for OSA Detection via bioinformatics analysis
? Multidisciplinary approach based on bioinformatics towards the treatment of OSA