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1.
Mult Scler ; 25(4): 541-553, 2019 04.
Article in English | MEDLINE | ID: mdl-29143562

ABSTRACT

BACKGROUND: Volumetric MRI surrogate markers of disease progression are lacking. OBJECTIVE: To establish cut-off values of brain volume loss able to discriminate between healthy controls and MS patients. METHODS: In total, 386 patients after first demyelinating event suggestive of MS (CIS), 964 relapsing-remitting MS (RRMS) patients, 63 secondary-progressive MS (SPMS) patients and 58 healthy controls were included in this longitudinal study. A total of 11,438 MRI scans performed on the same MRI scanner with the same protocol were analysed. Annualised percentage changes of whole brain, grey matter, thalamus and corpus callosum volumes were estimated. We investigated cut-offs able to discriminate between healthy controls and MS patients. RESULTS: At a predefined specificity of 90%, the annualised percentage change cut-off of corpus callosum volume (-0.57%) was able to distinguish between healthy controls and patients with the highest sensitivity (51% in CIS, 48% in RRMS and 42% in SPMS patients). Lower sensitivities (22%-49%) were found for cut-offs of whole brain, grey matter and thalamic volume loss. Among CIS and RRMS patients, cut-offs were associated with greater accumulation of disability. CONCLUSION: We identified cut-offs of annualised global and regional brain volume loss rates able to discriminate between healthy controls and MS patients.


Subject(s)
Brain/pathology , Disease Progression , Multiple Sclerosis/diagnosis , Multiple Sclerosis/pathology , Adolescent , Adult , Aged , Atrophy/pathology , Brain/diagnostic imaging , Corpus Callosum/diagnostic imaging , Corpus Callosum/pathology , Female , Gray Matter/diagnostic imaging , Gray Matter/pathology , Humans , Longitudinal Studies , Magnetic Resonance Imaging/standards , Male , Middle Aged , Multiple Sclerosis/diagnostic imaging , Sensitivity and Specificity , Thalamus/diagnostic imaging , Thalamus/pathology , Young Adult
2.
Med Sci Monit ; 17(9): CR491-7, 2011 Sep.
Article in English | MEDLINE | ID: mdl-21873945

ABSTRACT

BACKGROUND: Obstructive 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/METHODS: Fifty-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. RESULTS: For 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. CONCLUSIONS: MMP-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.


Subject(s)
Biomarkers/metabolism , Oxidative Stress , Sleep Apnea, Obstructive/metabolism , Sleep Apnea, Obstructive/pathology , Adult , C-Reactive Protein/metabolism , Copper/metabolism , Humans , Male , Matrix Metalloproteinase 9/metabolism , Middle Aged , Oxygen/metabolism , Sleep Apnea, Obstructive/enzymology
3.
Sleep Med ; 9(7): 732-8, 2008 Oct.
Article in English | MEDLINE | ID: mdl-17921064

ABSTRACT

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.


Subject(s)
Continuous Positive Airway Pressure/instrumentation , Polysomnography/instrumentation , Sleep Apnea, Obstructive/therapy , Therapy, Computer-Assisted/instrumentation , Adult , Aged , Air Pressure , Airway Resistance/physiology , Arousal/physiology , Cross-Over Studies , Double-Blind Method , Female , Forced Expiratory Volume/physiology , Humans , Male , Manometry/instrumentation , Middle Aged , Sleep Apnea, Obstructive/physiopathology , Sleep Stages/physiology , Snoring/physiopathology , Software , Treatment Outcome , Vital Capacity/physiology
4.
Neuro Endocrinol Lett ; 28(5): 575-9, 2007 Oct.
Article in English | MEDLINE | ID: mdl-17984939

ABSTRACT

OBJECTIVE: Responsible for sleep brain perfusion changes, obstructive sleep apnea (OSA) constitutes a cardiovascular risk. To find out about any diffuse damage to the brain tissue, we studied the S100B protein, whose serum level is known to rise in stroke and craniocerebral trauma. METHODS: 60 men (mean age 51.7+/-11.8 years) referred to us for OSA without any major comorbidity were examined polygraphically. S100B was determined with electrochemiluminiscence immunoassay (ECLIA) from evening and morning blood samples. RESULTS: All sixty men were diagnosed with OSA. The difference between the evening level of S100B 0.068+/-0.030 microg/l and the morning level 0.059+/-0.029 microg/l was significant (p=0.0004). Patients with mild OSA were found to have the evening S100B 0.063+/-0.023 microg/l, the morning level 0.042+/-0.012 microg/l, the difference being significant (p=0.00051). In moderate OSA the difference between the evening -0.070+/-0.017 microg/l and morning levels -0.055+/-0.025 microg/l was less significant (p=0.043). In severe OSA no difference was found between the evening and morning concentrations of S100B (0.070+/-0.036 microg/l and 0.070+/-0.031 microg/l respectively). The difference between the evening and morning S100B levels correlated negatively with AHI and ODI and positively with basal saturation and average minimal oxygen saturation. CONCLUSIONS: Sleep with signs of severe OSA influences S100B protein release.


Subject(s)
Circadian Rhythm/physiology , Nerve Growth Factors/blood , Oxygen/blood , S100 Proteins/blood , Sleep Apnea, Obstructive/blood , Adult , Aged , Humans , Male , Middle Aged , Polysomnography , S100 Calcium Binding Protein beta Subunit , Severity of Illness Index , Statistics, Nonparametric
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