Sweat metabolism changes can aid in determining the severity of sleep apnea. For the first time, a collaboration between the University of Córdoba and IMIBIC uses changes in sweat metabolism to diagnose the severity of sleep apnea.
The Greek word apnea (ἄπνoια) means “absence of breathing.” As a result, obstructive sleep apnea is a disease defined by breathing interruptions that occur while the patient is sleeping. Patients experience symptoms such as shortness of breath, fatigue, and drowsiness. This disease is also linked to an increase in the prevalence of cardiovascular disorders, so an accurate diagnosis of the disease’s severity is required to address these issues.
The metabolism of people with sleep apnea is crucial in determining the severity of the disease. These changes are typically detected in blood or urine. However, in search of a less invasive and more accessible alternative, a team from the Department of Analytical Chemistry at the University of Córdoba and the Maimonides Institute for Biomedical Research in Córdoba (IMIBIC), formed by researchers Laura Castillo, Mónica Calderón, Feliciano Priego and Bernabé Jurado, has verified, for the first time, the potential of sweat samples to ascertain the severity of sleep apnea.
By analyzing sweat metabolome and its alterations, mainly at night, we were able to see what stage of the disease the patients were in, it is a non-invasive and clean sample since, unlike the case with blood, we don’t have to remove proteins, and it’s much easier to analyze and detect metabolites.
Laura Castillo
“By analyzing sweat metabolome and its alterations, mainly at night, we were able to see what stage of the disease the patients were in,” explains Laura Castillo, the study’s lead author. For her, the advantages of using sweat over other samples are clear: “it is a non-invasive and clean sample since, unlike the case with blood, we don’t have to remove proteins, and it’s much easier to analyze and detect metabolites.”
In this study, sweat samples from before and after sleep were analyzed from a series of individuals with sleep apnea at different stages, as well as from a control group without the disease.
78-metabolites were identified and studied in these samples using the gas chromatography technique in conjunction with high-resolution mass spectrometry, most of which were related to energy production and oxidative stress. “We could see how the sweat metabolism itself indicates those alterations during sleep as a result of which the person’s energy production worsens and their oxidative stress increases,” Castillo said.
Thus, with a personalized follow-up using a person’s sweat excreted during sleep, the disease’s progression and potential consequences, such as cardiovascular problems, can be tracked. This metabolomic profile also allowed the trial to distinguish between those who had the disease and those who did not and were in the control group.
An index to learn more about the disease
This study not only establishes sweat as a good sentinel for determining the stage of the disease, but it also demonstrates the importance of taking the oxygen desaturation index into account when diagnosing it.
The Apnea-Hypopnea Index (AHI) is currently used to diagnose sleep apnea. The AHI measures sleep apnea based on the number of episodes of shortness of breath per hour (for example, the disease is considered severe if 30 or more episodes occur per hour). The team claims that this index “does not provide all of the information about the disease or the patient’s situation at a given time” because it counts the number of events but not their severity.
As a result, in their study, they also validate the importance of using the oxygen desaturation index, which measures the number of events in which oxygen saturation has decreased by more than 3% to determine how serious the episodes are. After confirming the linear relationship between this index and the AHI, its validity was confirmed because, in addition to the data provided by the AHI, it also measures severity by taking oxygen saturation loss into account.
