The effects of an intermittent fasting diet (IFD) in the general population are still controversial. In this study, we aimed to systematically evaluate the effectiveness of an IFD to reduce body mass index and glucose metabolism in the general population without diabetes mellitus. Cochrane, PubMed, and Embase databases were searched to identify randomized controlled trials and controlled clinical trials that compared an IFD with a regular diet or a continuous calorie restriction diet. The effectiveness of an IFD was estimated by the weighted mean difference (WMD) for several variables associated with glucometabolic parameters including body mass index (BMI) and fasting glucose. The pooled mean differences of outcomes were calculated using a random effects model. From 2814 studies identified through a literature search, we finally selected 12 articles (545 participants). Compared with a control diet, an IFD was associated with a significant decline in BMI (WMD, −0.75 kg/m2; 95% CI, −1.44 to −0.06), fasting glucose level (WMD, −4.16 mg/dL; 95% CI, −6.92 to −1.40), and homeostatic model assessment of insulin resistance (WMD, −0.54; 95% CI, −1.05 to −0.03). Fat mass (WMD, −0.98 kg; 95% CI, −2.32 to 0.36) tended to decrease in the IFD group with a significant increase in adiponectin (WMD, 1008.9 ng/mL; 95% CI, 140.5 to 1877.3) and a decrease in leptin (WMD, −0.51 ng/mL; 95% CI, −0.77 to −0.24) levels. An IFD may provide a significant metabolic benefit by improving glycemic control, insulin resistance, and adipokine concentration with a reduction of BMI in adults.

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Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.