Hypersomnias of central origin are defined as the inability to maintain wakefulness and alertness during the major waking episodes of the day， with the manifestation of irrepressible drowsiness or even unprovoked sleep attacks. This spectrum of disorders mainly includes narcolepsy type 1， narcolepsy type 2， idiopathic hypersomnia， and Kleine-Levin syndrome. Currently， the clinical diagnosis of hypersomnias of central origin mainly depends on subjective medical history， sleepiness scales， and electrophysiological assessments， and these conventional diagnostic methods are easily affected by confounding factors. A reduction in the level of hypocretin-1 in cerebrospinal fluid （CSF） is the gold standard for the diagnosis of narcolepsy type 1， while there is still a lack of specific and Objective laboratory markers for the other subtypes， resulting in the high rates of diagnostic delay and misdiagnosis. As Objective and quantifiable indicators for pathophysiological processes， biomarkers have an important clinical value in the early screening， precise phenotyping， and longitudinal monitoring of hypersomnias of central origin， as well as in the development of targeted therapies for this group of sleep disorders. This article systematically reviews the research advances in biomarkers associated with hypersomnias of central origin from the five dimensions of polysomnography and daytime functional assessment， peripheral serology， cerebrospinal fluid， neuroimaging， and autonomic nervous function， in order to provide a theoretical framework and evidence-based support for constructing a precise diagnosis and treatment system for these disorders.
Narcolepsy

Objective To analyze the influencing factors of frailty in elderly patients with lower extremity osteoarthritis,and to construct and validate the risk assessment model.Methods Convenient sampling method was used to select 535 elderly patients with lower extremity osteoarthritis from tertiary hospitals and community health service centers in Hangzhou from January to September 2022 as the survey subjects including 357 in the modeling group and 178 in the validation group.Univariate and multivariate logistic regression analysis were used to determine the risk factors of frailty,construct a risk assessment model and draw a nomogram.The discrimination and calibration of the model were evaluated by the area under the receiver operating characteristic curve and the Hosmer-Lemeshow test.The Bootstrap method was used for intemal validation of the model,and the time verification method was used for external validation.Results The model variables included the number of affected joints,age-adjusted Charlson comorbidity index,pain,nutritional status,sedentary time,activity of daily living,osteoarthritis index,lower limb muscle strength,and Social Support Rating Scale score.The Hosmer-Lemeshow test results of the model showed that P=0.202,the area under the receiver operating characteristic curve was 0.942,the optimal critical value was 0.392,the sensitivity was 0.914,the specificity was 0.893,and the accuracy rate was 0.902.The internal and external validation showed that the C-statistics were 0.935 and 0.919,respectively,and the calibration curve showed good fitting.Conclusion The risk assessment model has a good degree of discrimination and calibration,which can more intuitively and easily screen elderly patients with lower extremity osteoarthritis at high risk of frailty,and provide references for early monitoring,identification,prevention and control.