OBJECTIVE: The objective of this study was the quantitative analysis of the alkaloid content of areca chewable products from different countries and regions using HPLC-UV, as well as the benefit of their safety evaluation products.
METHOD: An HPLC-UV method was established for qualitative and quantitative analyses of 65 batches of areca chewable products from different countries and regions. Additionally, similarity evaluation of chromatographic fingerprints was applied for data analysis.
RESULTS: These results reveal a significant variation in the levels of areca alkaloids among tested products, specifically guvacoline (0.060-1.216 mg/g), arecoline (0.376-3.592 mg/g), guvacine (0.028-1.184 mg/g), and arecaidine (0.184-1.291 mg/g). There were significant differences in the alkaloid content of areca chewable products from different producing areas.
CONCLUSIONS: The method will be useful in the safety evaluation of different areca chewable products.
HIGHLIGHTS: The established HPLC-UV method can be adopted for safety evaluation of areca chewable products from different countries and regions due to its general applicability.
METHODS: Eight electronic databases (Web of Science, PubMed, ScienceDirect, American Psychological Association PsycNet, Cochrane Library, Scopus, Embase, and Ovid) were searched for the study. Articles published from January 1 to December 31, 2022, were considered for this review. A random-effects meta-analysis and between-study heterogeneity analysis were conducted using Comprehensive Meta-Analysis V3.0 software.
RESULTS: We identified 7829 articles of which 28 met the full inclusion criteria and were included in the systematic review and analyses. Our pooled analysis suggested that participants with MCI can be differentiated from HC by significant P200, P300, and N200 latencies. The P100 and P300 amplitudes were significantly smaller in participants with MCI when compared with those in the HCs, and the patients with MCI showed increased N200 amplitudes. Our findings provide new insights into potential electrophysiological biomarkers for diagnosing MCI.