A detection model for cognitive dysfunction based on volatile organic compounds from a large Chinese community cohort

Abstract

IntroductionWe explored whether volatile organic compound (VOC) detection can serve as a screening tool to distinguish cognitive dysfunction (CD) from cognitively normal (CN) individuals.MethodsThe cognitive function of 1467 participants was assessed and their VOCs were detected. Six machine learning algorithms were conducted and the performance was determined. The plasma neurofilament light chain (NfL) was measured.ResultsDistinguished VOC patterns existed between CD and CN groups. The CD detection model showed good accuracy with an area under the receiver‐operating characteristic curve (AUC) of 0.876. In addition, we found that 10 VOC ions showed significant differences between CD and CN individuals (p < 0.05); three VOCs were significantly related to plasma NfL (p < 0.005). Moreover, a combination of VOCs with NfL showed the best discriminating power (AUC = 0.877).DiscussionDetection of VOCs from exhaled breath samples has the potential to provide a novel solution for the dilemma of CD screening.

Exhaled breath analysis for the discrimination of asthma and chronic obstructive pulmonary disease

“…For example, the concentration of isoprene increases significantly during exercise [8]. VOCs detection has shown good application value for the diagnosis of lung cancer [9], esophageal cancer [10], COPD [11], tuberculosis [12], COVID-19 [13], cognitive dysfunction [14], and other diseases [15]. Until now, there have been few studies on the differentiation of COPD and asthma by VOCs, and whether they can be distinguished is controversial [16][17][18][19].…”