Development and Validation of a Metabolite Index for Obstructive Sleep Apnea across Race/Ethnicities

Abstract

BackgroundObstructive sleep apnea (OSA) is a common disorder characterized by recurrent episodes of upper airway obstruction during sleep resulting in oxygen desaturation and sleep fragmentation, and associated with increased risk of adverse health outcomes. Metabolites are being increasingly used for biomarker discovery and evaluation of disease processes and progression. Studying metabolomic associations with OSA in a diverse community-based cohort may provide insights into the pathophysiology of OSA. We aimed to develop and replicate a metabolite index for OSA and identify individual metabolites associated with OSA.Methods and FindingsWe studied 219 metabolites and their associations with the apnea hypopnea index (AHI) and with moderate-severe OSA (AHI≥15) in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (n=3507) using two methods: (1) association analysis of individual metabolites, and (2) least absolute shrinkage and selection operator (LASSO) regression to identify a subset of metabolites jointly associated with OSA, and develop a metabolite index for OSA. Results were validated in the Multi-Ethnic Study of Atherosclerosis (MESA) (n=475). When assessing the associations with individual metabolites, we identified seven metabolites significantly positively associated with OSA in HCHS/SOL (FDR p<0.05), of which four associations - glutamate, oleoyl-linoleoyl-glycerol (18:1/18:2), linoleoyl-linoleoyl- glycerol (18:2/18:2) and phenylalanine, replicated in MESA (one sided-p <0.05). The OSA metabolite index, composed of 14 metabolites, was associated with 50% increase of risk for moderate-severe OSA (OR=1.50 [95% CI: 1.21-1.85] per 1 SD of OSA metabolite index, p<.001) in HCHS/SOL and 44% increased risk (OR=1.55 [95% CI: 1.10-2.20] per 1 SD of OSA metabolite index, p=0.013) in MESA, both adjusted for demographics, lifestyle, and comorbidities. Similar albeit less significant associations were observed for AHI.ConclusionsWe developed a metabolite index that replicated in an independent multi-ethnic dataset, demonstrating the robustness of metabolomic-based OSA index to population heterogeneity. Replicated metabolite associations may provide insights into OSA-related molecular and metabolic mechanisms.

Metabolomic Profiles of Sleep-Disordered Breathing are Associated with Hypertension and Diabetes Mellitus Development: the HCHS/SOL

“…We found that metabolic scores reflecting SDB better predicted adverse cardiometabolic outcomes compared to the physiological phenotypes such as REI or those measuring hypoxia. We developed SDB-MRSs, expanding our earlier work on OSA-MRSs 30 . We now further studied the association of the MRSs with incident cardiometabolic outcomes.…”

“…In a secondary analysis, we also constructed SDB-MRSs based on the SMA results, similar to our prior work 30 . The SMA based SDB-MRSs were weighted sums of metabolite levels where the metabolites were those with FDR < 0.05 among the metabolites modeled as continuous in the SMA batch 1 discovery analysis.…”

“…Finally, survey-weighted Poisson regressions were implemented to assess the associations between incident hypertension and diabetes mellitus among batch 1 and 2 combined study samples with various SDB phenotypes including benchmark singular sleep measures (i.e., REI 3%, hypoxic burden) and our newly developed composite measures (i.e., SDB PCs, and SDB-MRSs), as well as our recently developed OSA-MRS, adjusting for Model 1 and 2 covariates, respectively. The OSA-MRS was trained using LASSO on moderate to severe OSA (defined as REI3 > = 15) in the HCHS/SOL cohort and previously validated in the MESA cohort 30 . We combined the two batches in this analysis to increase statistical power by having a larger sample size.…”

“…Untargeted metabolomic profiling is the comprehensive identification and quantification of small metabolite molecules within the biological system, and has begun to be used in sleep research to understand the cellular process such as sleep/wake regulation 25 , 26 , as a window on peripheral molecular clocks and oscillators 27 , and to detect biomarkers of sleep restriction 28 and neurological degeneration among patients with obstructive sleep apnea (OSA) 29 . In a recent study 30 we identified metabolites associated with moderate to severe OSA (defined as a Respiratory Event Index [REI] > = 15) and constructed an index composed of 14 metabolites, associated with OSA cross-sectionally, in two independent datasets. Another recent study 31 identified metabolites associated with SDB and metabolites that changed levels following SDB treatment using continuous positive airway pressure, though without multiple testing correction.…”