PLoS ONE (Jan 2016)

Reliability of Potential Pain Biomarkers in the Saliva of Healthy Subjects: Inter-Individual Differences and Intersession Variability.

  • Eva M Sobas,
  • Roberto Reinoso,
  • Rubén Cuadrado-Asensio,
  • Itziar Fernández,
  • Miguel J Maldonado,
  • José C Pastor

DOI
https://doi.org/10.1371/journal.pone.0166976
Journal volume & issue
Vol. 11, no. 12
p. e0166976

Abstract

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Salivary cortisol, α-amylase (sAA), secretory IgA (sIgA), testosterone, and soluble fraction of receptor II of TNFα (sTNFαRII) could serve as objective pain measures, but the normal variability of these potential biomarkers is unknown.Saliva was collected with the passive secretion method from 34, pain-free subjects in two single samples at least 24 hours apart. Biomarker variation and intersession reliability were assessed with the intraclass correlation coefficient (ICC). Also, we calculated the within-subject standard deviation (Sw) and the reproducibility (2.77 × Sw) of intersession measures.Salivary cortisol, sAA, sIgA, testosterone, and sTNFαRII yielded the following ICCs: 0.53, 0.003, 0.88, 0.42 and 0.83, respectively. We found no statistically significant systematic differences between sessions in any biomarker except for testosterone, which showed a decrease on the second day (p<0.001). The reproducibility for salivary cortisol, sAA, sIgA, testosterone, and sTNFαRII were 0.46 ng/ml, 12.88 U/ml, 11.7 μg/ml, 14.54 pg/ml and 18.29 pg/ml, respectively. Cortisol, testosterone and TNFαRII measurement variability showed a positive correlation with the magnitude (p<0.002), but no relationship was found for sAA and sIgA.Salivary sIgA and sTNFαRII show a remarkable good reproducibility and, therefore, could be useful as pain biomarkers. When using the passive secretion method, intersession variations in salivary sIgA of more than 11.7 μg/ml may reflect true biomarker change. In the case of sTNFαRII this will depend of the magnitude. The estimates herein provided should help investigators and clinicians differentiate actual biomarker modification from measurement variability.