(1) Background: Metabolic syndrome (MetS) is a cluster-based disorder comprising several pre-disease or pre-clinical statuses for diabetes, hypertension, dyslipidemia, cardiovascular risk, and mortality. Appendicular skeletal muscle (ASM), or lean mass, is considered the main site of insulin-mediated glucose utilization. Therefore, we aimed to reveal the association between lower appendicular skeletal muscle mass to upper appendicular skeletal muscle mass ratio (LUR) and risk for MetS. (2) Methods: We analyzed the 2008–2011 Korean National Health Examination and Nutrition Survey (KNHANES) data. Quintiles of lower ASM to upper ASM ratio (LUR) were categorized as follows: Q1: ≤2.65, Q2: 2.66–2.80, Q3: 2.81–2.94, Q4: 2.95–3.11, and Q5: ≥3.12 in men and Q1: ≤3.00, Q2: 3.01–3.18, Q3: 3.19–3.36, Q4: 3.37–3.60, and Q5: ≥3.61 in women. Multivariate logistic regression models were used after setting MetS and the LUR quintiles as the independent and dependent variables and adjusting for covariates. (3) Result: In men, MetS in accordance with the LUR quintiles exhibits a reverse J-curve. All groups from Q2 to Q5 had a lower odds ratio (OR) (95% CI) for MetS compared to the Q1 group. The lowest OR (95% CI) of 0.85 (0.80–0.91) was observed in Q4. However, in women, the figure shows a sine curve. Compared to the Q1 group, the Q2 and Q3 groups had a higher OR, while the Q4 and Q5 groups presented a lower OR. Among them, the OR (95% CI) in the Q4 group was lowest, at 0.83 (0.76–0.91). (4) Conclusions: While total appendicular skeletal muscle mass is important to prevent MetS, it is necessary to maintain an optimal ratio of muscle mass between the upper and lower appendicular skeletal muscle mass.
Distinguishing syncope from epileptic seizures in patients with sudden loss of consciousness is important. Various blood tests have been used to indicate epileptic seizures in patients with impaired consciousness. This retrospective study aimed to predict the diagnosis of epilepsy in patients with transient loss of consciousness using the initial blood test results. A seizure classification model was constructed using logistic regression, and predictors were selected from a cohort of 260 patients using domain knowledge and statistical methods. The study defined the diagnosis of seizures and syncope based on the consistency of the diagnosis made by an emergency medicine specialist at the first visit to the emergency room and the diagnosis made by an epileptologist or cardiologist at the first outpatient visit using the International Classification of Diseases 10th revision (ICD-10) code. Univariate analysis showed higher levels of white blood cells, red blood cells, hemoglobin, hematocrit, delta neutrophil index, creatinine kinase, and ammonia levels in the seizure group. The ammonia level had the highest correlation with the diagnosis of epileptic seizures in the prediction model. Therefore, it is recommended to be included in the first examination at the emergency room.
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