Back

Non-targeted metabolomics analysis reveals distinct metabolic profiles between positive and negative emotional tears of humans

Liang, H.; Wu, S.; Yang, D.; Huang, J.; Yao, X.; Gong, J.; Liu, P.; Duan, L.; Yang, L.; Xu, Q.; Huang, R.; Maimaitituersun, M.; Tao, L.; Peng, Q.

2022-02-02 ophthalmology
10.1101/2022.01.28.22270049 medRxiv
Show abstract

BACKGROUNDAlthough the chemical components of basal, reflex, and emotional tears are different, the presence of distinctions in the tears of different emotions is still unknown. The present study aimed to address the biochemical basis behind emotional tears through non-targeted metabolomics analysis between positive and negative emotional tears of humans. METHODSSamples of reflex (C), negative (S), and positive (M) emotional tears were collected from healthy college participants. Untargeted metabolomics was performed to identify the metabolites in the different types of tears. The differentially altered metabolites were screened and assessed using univariate and multivariate analyses. RESULTSThe global metabolomics signatures classified the C, S, and M emotional tears. A total of 133 significantly differential metabolites of ESI-mode were identified between negative and positive emotional tears. The top 50 differential metabolites between S and M were highly correlated. The significantly altered pathways included porphyrin & chlorophyll metabolism, bile secretion, biotin metabolism, arginine & proline metabolism and among others. CONCLUSIONThe metabolic profiles between reflex, positive, and negative emotional tears of humans are distinct. Secretion of positive and negative emotional tears are distinctive biological activities. Therefore, the present study provides a chemical method to detect human emotions which may become a powerful tool for diagnosis of mental disease and identification of fake tears.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.