Sarcopenia in patients with multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune neurodegenerative disease of the central nervous system. Sarcopenia, which is characterized by the loss of physical performance and poor outcomes, has recently become the focus of research. However, the relationship between sarcopenia and MS has not yet been investigated. This study aims to determine the prevalence of sarcopenia in MS patients and investigate the factors associated with sarcopenia. METHODS: One hundred and one MS patients who can walk without assistance and 55 healthy controls were included. Handgrip strength (HGS) and gait speed tests were applied to all participants. Additionally, anterior thigh muscle thickness (anterior TMT) and skeletal muscle mass index (SSMI) were estimated by ultrasound and bioelectrical impedance analysis (BIA), respectively. According to these tests, MS patients were grouped as either sarcopenic or non-sarcopenic. The groups were compared using clinical and laboratory data, handgrip strength and performance test, Modified Fatigue Impact Scale (MFIS), and the Godin leisure-time exercise questionnaire (GLTEQ). RESULTS: HGS, gait speed, fat free mass (FFM), SMMI, anterior TMT, and sonographic thigh adjustment ratio (STAR) values in patients with MS were significantly lower than healthy controls for both sexes (for female, p:0.001, p:0.001, p:0.010, p:0.049, p:0.001, and p:0.101, respectively; for male, all p:0.001). Compared with healthy controls, MS patients had a significantly lower GLTEQ score (p:0.001), while the MFIS score (p:0.001) was higher. According to STAR, HGS, and gait speed, sarcopenia was diagnosed in 12 (17.64%) female and 7 (21.21%) male patients with MS. Whole-body sarcopenia was diagnosed in only 11 (10.9%) of the patients by BIA. HGS, gait speed, FFM, anterior TMT, and STAR values in sarcopenic MS patients were significantly lower than in non-sarcopenic for females (p:0.001, p:0.001, p:0.004, p:0.001, and p:0.001, respectively) and males (p:0.001, p:0.001, p:0.011, p:0.003, and p:0.001, respectively). MFIS score was significantly higher in sarcopenic patients than non-sarcopenic for both females (p:0.001) and males (p:0.036), but only the physical fatigue subscale was significantly higher. While the physical fatigue score was negatively correlated with GLTEQ in MS patients (r:-0.276, p:0.005), it was positively correlated with the expanded disability status scale (r:0.409, p:0.001). CONCLUSION: We detected that approximately one-fifth of MS patients have sarcopenia. Regional sarcopenia was more prevalent than whole body sarcopenia. We found a high degree of fatigue and lack of exercise in sarcopenic MS patients.

The yield of electroencephalography in syncope.

BACKGROUND AND PURPOSE: Syncope is defined as a brief transient loss of consciousness due to cerebral hypoperfusion. Although the diagnosis of syncope is based on a thorough history and examination, electroencaphalography (EEG) is also an important investigational tool in the differential diagnosis in this group of patients. In this study we aimed to identify the diagnostic value of EEG in patients with syncope. METHODS: We retrospectively examined EEG recordings of 288 patients with the diagnosis of syncope referred to the Cankiri State Hospital EEG laboratory, from January 2014 to January 2016. The EEG findings were classified into 6 groups as normal, epileptiform discharges (spike and sharp waves), generalized background slowing, focal slowing, hemispherical asymmetries, and low amplitude EEG tracing. The EEGs were separated according to gender and age. RESULTS: Total of 288 patients were included in this study, 148 were females (51.4%) and 140 (48.6%) were males. Among all the EEG reports, 203 (70.5%) were normal, 8 of them (2.8%) showed generalized background slowing and 7 (2.4%) demonstrated focal slow waves. Epileptiform discharges occured among 13 patients (4.5%). Hemispherical asymmetries were detected in 10 patients (3.5%) and low amplitude EEG tracing in 47 patients (16.3%). There was no significant difference between age groups in EEG findings (p=0.3). Also no significant difference was detected in EEG results by gender (p=0.2). CONCLUSION: Although the diagnosis of syncope, epilepsy and non-epileptic seizures is clinical diagnosis, EEG still remains additional method.