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New Insights into Obstructive Sleep Apnea Research

Study reveals genetic links and implications for OSA diagnosis and treatment.

― 5 min read


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Obstructive Sleep Apnea (OSA) is a common sleep disorder that affects many people, particularly those in middle age. It occurs when a person's breathing is interrupted during sleep due to a blockage in the upper airway. This can lead to various health issues, including heart disease, diabetes, and high blood pressure. Research shows that OSA affects about 17% of women and 34% of men in the United States. Unfortunately, many people with OSA do not realize they have it, leading to underdiagnosis, especially among women and those with milder symptoms.

Risk Factors for OSA

Several factors can increase the likelihood of developing OSA. The main ones include:

  • Obesity: Extra weight can add pressure to the throat, contributing to airway blockages.
  • Sex: Men are more likely than women to have OSA.
  • Age: The risk of OSA increases as people get older.

Genetic Factors and Polygenic Scores (PGS)

Recent research has focused on the genetic aspects of OSA by examining polygenic scores (PGS). PGS refer to a summary of genetic variants that are linked to a particular trait. By considering many genetic markers together, researchers can better understand how genetics may influence the risk of OSA and its relationships with other health conditions.

The development of PGS could help with predicting risk levels, classifying individuals into different risk groups, and enhancing screening efforts for OSA. Understanding genetic contributions to OSA can provide insights into the complex nature of the disorder and its interactions with other chronic diseases.

A Diverse Approach to OSA Research

To study the genetic background of OSA, researchers used data from various biobanks and cohort studies representing different racial and ethnic backgrounds. Some of the notable data sources include the Million Veteran Program, FinnGen study, and others. By including diverse populations, the findings become more applicable to a wider range of people.

Study Design and Methodology

The research involved several steps:

  1. PGS Development: The first phase required gathering genetic data from specific studies and analyzing it to create the PGS.

  2. Evaluation: After developing the PGS, researchers tested its effectiveness in predicting OSA across different populations.

  3. Validation: The next step involved validating the associations found using new independent datasets.

  4. Analysis: Finally, researchers examined how genetic scores correlated with OSA and other related health measures.

The Importance of Body Mass Index (BMI)

BMI, which is a measure of body fat based on height and weight, plays a critical role in understanding OSA. It is often considered the strongest risk factor for developing the disorder. Research emphasizes the need to explore how BMI links to other health issues like cardiovascular diseases.

Findings from the Research

Scientists found that PGSs developed using both BMI-adjusted and unadjusted methods showed strong associations with OSA. The differences noted between the two types of scores revealed the complexity of OSA's relationship with other Health Outcomes.

Analysis Across Different Populations

The study included participants from various backgrounds, enabling researchers to analyze OSA risks under different circumstances. Key findings highlighted that the genetic risk of OSA varies depending on factors such as sex, ethnicity, and age.

TOPMed Study Population

In one of the major datasets, termed the TOPMed study, participants came from four race/ethnic groups: White, Black, Hispanic/Latino, and Asian. The study identified significant differences in characteristics among these groups regarding age, sex distribution, and BMI.

The Role of Genetic Data

Using advanced statistical methods, researchers were able to assess the relationship between PGSs and the severity of OSA in different populations. Results showed that higher PGS values correlated with a higher likelihood of OSA, regardless of race or ethnicity.

Results from Specific Cohorts

In the Geisinger's MyCode project, which involved a substantial number of participants, researchers observed a consistent association between the developed PGS and OSA classification. The findings showed that BMI-adjusted models performed well in determining OSA risk.

Sleep Phenotypes and OSA Relationships

To fully understand OSA, researchers also looked at related sleep measures. They analyzed various factors such as the apnea-hypopnea index (AHI) and oxygen levels during sleep. Notably, the associations were stronger during non-rapid eye movement (NREM) sleep compared to rapid eye movement (REM) sleep.

Implications for Health Outcomes

The research demonstrated how OSA PGSs are linked to multiple health outcomes. For instance, PGSs based on BMI could highlight the associations between OSA and other conditions like hypertension and type 2 diabetes. Understanding these links can help healthcare providers manage OSA more effectively.

Strengths of the Study

The diverse dataset and several independent cohorts made this study robust. The use of both types of PGS allowed for a thorough investigation of genetic factors influencing OSA. The results provide valuable information for future studies aimed at unraveling the complexities of this disorder.

Challenges and Considerations

Despite the strengths, challenges persist when diagnosing OSA. The study highlighted the need for better screening methods, especially among underrepresented groups. The underdiagnosis of OSA is a significant issue, which may lead to inadequate treatment and management options.

Summary and Future Directions

In conclusion, this research has significantly contributed to our understanding of OSA by integrating genetic factors into the analysis. The development of BMI-adjusted and unadjusted PGSs has opened new avenues for predicting OSA risk and associated health outcomes. Future studies should continue to focus on diverse populations and improve screening techniques to reduce the underdiagnosis of this common disorder.

The insights gained may pave the way for more personalized treatment options and better management strategies for individuals living with OSA, ultimately improving health outcomes across various populations.

Original Source

Title: Polygenic scores for obstructive sleep apnea based on BMI- adjusted and - unadjusted genetic associations reveal pathways contributing to cardiovascular disease

Abstract: BackgroundObstructive sleep apnea (OSA) is a heterogeneous disease, with obesity a significant risk factor via increased airway collapsibility, reduced lung volumes, and possibly body fat distribution. MethodsUsing race/ethnic diverse samples from the Million Veteran Program, FinnGen, TOPMed, All of Us (AoU), Geisingers MyCode, MGB Biobank, and the Human Phenotype Project (HPP), we developed, selected, and assessed polygenic scores (PGSs) for OSA, relying on genome-wide association studies both adjusted and unadjusted for BMI: BMIadjOSA- and BMIunadjOSA-PGS. We tested their associations with CVD in AoU. ResultsAdjusted odds ratios (ORs) for OSA per 1 standard deviation of the PGSs ranged from 1.38 to 2.75. The associations of BMIadjOSA- and BMIunadjOSA-PGSs with CVD outcomes in AoU shared both common and distinct patterns. For example, BMIunadjOSA-PGS was associated with type 2 diabetes, heart failure, and coronary artery disease, but the associations of BMIadjOSA-PGS with these outcomes were statistically insignificant with estimated OR close to 1. In contrast, both BMIadjOSA- and BMIunadjOSA-PGSs were associated with hypertension and stroke. Sex stratified analyses revealed that BMIadjOSA-PGS association with hypertension was driven by data from OR=1.1, p-value=0.002, OR=1.01 p-value=0.2 in males). OSA PGSs were also associated with dual-energy X-ray absorptiometry (DXA) body fat measures with some sex-specific associations. ConclusionsDistinct components of OSA genetic risk are related to obesity and body fat distribution, and may influence clinical outcomes. These may explain differing OSA risks and associations with cardiometabolic morbidities between sex groups.

Authors: Tamar Sofer, N. Kurniansyah, S. Strausz, G. Chittoor, S. Gupta, A. E. Justice, Y. Hrytsenko, B. T. Keenan, B. E. Cade, B. W. Spitzer, H. Wang, J. E. Huffman, M. Moll, B. Haring, S. Y. Jung, L. M. Raffield, R. C. Kaplan, J. Rotter, S. S. Rich, S. A. Gharib, T. M. Bartz, P. Y. Liu, H. Chen, M. Fornage, L. Hou, D. Levy, A. Morrison, H. M. Ochs-Balcom, B. M. Psaty, P. W. Wilson, K. Cho, A. I. Pack, H. M. Ollila, S. Redline, D. J. Gottlieb, FinnGen, the Trans-Omics in Precision Medicine Consortium, the VA Million Veteran Program

Last Update: Oct 21, 2024

Language: English

Source URL: https://www.medrxiv.org/content/10.1101/2024.10.20.24315783

Source PDF: https://www.medrxiv.org/content/10.1101/2024.10.20.24315783.full.pdf

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to medrxiv for use of its open access interoperability.

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