Objective: The objective of the present study was to meta-analyze relevant literature to gain a comprehensive understanding of the potential relationship between serum uric acid levels and risk of benign paroxysmal positional vertigo (BPPV). Methods: The databases of PubMed, Web of Science, Embase, Chinese National Knowledge Infrastructure, Wanfang, and SinoMed were systematically searched for observational case-control studies of the association between BPPV and serum uric acid levels published up to October 2018. Data from eligible studies were meta-analyzed using Stata 12.0. Results: A total of 12 studies were included in the analysis. There was a strong tendency for serum uric acid levels to be associated with risk of BPPV among studies conducted in China (OR 0.69, 95%CI 0.01–1.40, p = 0.053), but not among studies outside China (OR 1.07, 95%CI 1.08–3.22, p = 0.33). Across all studies, serum uric acid level was significantly higher among individuals with BPPV than among controls (OR 0.78, 95%CI 0.15–1.41, p = 0.015), yet it did not independently predict risk of the disorder (OR 1.003, 95%CI 0.995–1.012, p = 0.471). Conclusion: The available evidence suggests that BPPV is associated with elevated levels of serum uric acid, but these levels may not be an independent risk factor of BPPV.

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Our meta-analysis provides the first reliable pooled estimate of RLS prevalence among individuals with migraine, and it provides strong evidence that RLS risk is higher among individuals with migraine than among controls.

: Neurodegenerative diseases are caused by progressive lesions or loss of specific nerve cells, which endanger human health. However, the mechanism by which neurodegeneration manifests remains unclear. Proteomics can shed light on this question as well as help establish diagnostic standards and discover new drug targets. The power of proteomics for understanding neurodegenerative diseases has increased substantially with the application of iTRAQ and TMT labeling techniques. This review focuses on progress in these labeling techniques and their applications in neurodegeneration research.

Objectives Freezing of gait (FOG) is a common and complex disabling episodic gait disturbance in patients with Parkinson's disease (PD). Currently, the treatment of FOG remains a challenge for clinicians. The aim of our study was to develop a nomogram for FOG risk based on data collected from Chinese patients with PD. Materials & Methods A total of 379 PD patients (197 with FOG) from Kunming Medical University were recruited as a training cohort. Additionally, 339 PD patients (166 with FOG) were recruited from West China Hospital of Sichuan University, to serve as the validation cohort. The least absolute shrinkage and selection operator regression model was used to select clinical and demographic characteristics as well as blood markers, which were incorporated into a predictive model using multivariate logistic regression to predict the risk of developing FOG. The model was validated using the validation dataset, and model performance was evaluated using the C‐index, calibration plot, and decision curve analyses. Results The final predictive model included the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) score, Parkinson's Disease Questionnaire (PDQ39), H‐Y stage, and visuospatial function. The model showed good calibration and good discrimination, with a C‐index value of 0.772 against the training cohort and 0.766 against the validation cohort. Decision curve analysis demonstrated the clinical utility of the nomogram. Conclusion A nomogram incorporating RBDSQ, PDQ39, H‐Y stage, and visuospatial function may reliably predict the risk of FOG in PD patients.

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