We identified cut-offs of annualised global and regional brain volume loss rates able to discriminate between healthy controls and MS patients.
SummaryBackgroundObstructive sleep apnea (OSA) is a condition contributing to oxidative stress. The aim of this study was to ascertain if there is any connection between OSA and novel oxidative stress-related markers. Matrix metalloproteinases 2 and 9 (MMP-2, MMP-9), high sensitive C-reactive protein (hsCRP), pregnancy-associated plasma protein-A (PAPP-A), soluble receptors for advanced glycation end-products (sRAGE), zinc (Zn) and copper (Cu) were measured. Further biochemical markers were evaluated.Material/MethodsFifty-one men suspected for OSA indicated for night polygraphy were included. Apnea/hypopnea index (AHI), oxygen desaturation index (ODI), mean blood hemoglobin oxygen saturation (SpO2) and time of blood hemoglobin oxygen saturation below 90% (SpO2 <90%) were measured. Morning venous blood samples were taken.ResultsFor body mass index (BMI) we found strong positive correlation with levels of Cu, MMP-9, hsCRP and fibrinogen, and negative correlation with sRAGE. Concerning ventilation parameters, we found positive correlation of ODI and SpO2 <90% with markers MMP-9 and hsCRP. sRAGE level correlated with AHI and ODI negatively. SpO2 correlated negatively with Cu, MMP-9, hsCRP and fibrinogen. There was no correlation between ventilation parameters and markers MMP-2, PAPP-A and Zn. Compared to severity of OSA, there was significant difference in levels of hsCRP and Cu between patients with AHI ≤5 and AHI ≥30 independent of BMI.ConclusionsMMP-9, hsCRP, sRAGE and Cu seem to be strong predictors of oxidative stress in OSA patients. The strong correlation between oxidative stress-related markers and OSA is elucidated by connection of these to BMI, which is probably a primary condition of oxidative stress, but OSA is an additive condition.
Background: The best method for titration of continuous positive airway pressure (CPAP) therapy in obstructive sleep apnea (OSA) syndrome has not yet been established. The 90th or 95th percentiles of the pressure titrated over time by automatic CPAP (A-CPAP) have been recommended as reference for prescribing therapeutic fixed CPAP (F-CPAP). We compared A-CPAP to F-CPAP, which was determined by a common prediction formula. Methods: Forty-five patients who were habituated to F-CPAP underwent titration polysomnography. In a double-blind, randomized order, each patient used an A-CPAP device in the autotitration and in the fixed pressure mode during one half of the night. Apnea-hypopnea index (AHI) and pressure profiles were primary outcomes. Bias and precision were additionally assessed for both CPAP modes. Results: No significant differences in various sleep parameters or in subjective sleep quality evaluation were found. The AHI was effectively lowered in both CPAP modes (A-CPAP 7.7 [10.8] events/h versus F-CPAP 5.4 [9.0] events/h, p = 0.061). Comparison of group means showed that F-CPAP closely paralleled mean (Pmean) and median (P50), but not the 95th percentile (P95) pressure, of A-CPAP. While bias was lowest for Pmean and P50, there was a lack of precision in all A-CPAP pressure categories. Conclusions: We confirm that F-CPAP set by prediction formula is not worse in terms of AHI control than A-CPAP. On average, F-CPAP parallels Pmean and P50 but not P95. However, due to imprecise matching, individual F-CPAP values cannot be derived from Pmean or P50.
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