Assessing and treating pain in nonverbal children with developmental disabilities are a clinical challenge. Current assessment approaches rely on clinical impression and behavioral rating scales completed by proxy report. Given the growing health relevance of the salivary metabolome, we undertook a translational-oriented feasibility study using proton nuclear magnetic resonance (NMR) spectroscopy and neuropeptide/cytokine/hormone detection to compare a set of salivary biomarkers relevant to nociception. Design: Within-group observational design. Setting: Tertiary pediatric rehabilitation hospital. Subjects: Ten nonverbal pediatric patients with cerebral palsy with and without pain. Methods: Unstimulated (passively collected) saliva was collected using oral swabs followed by perchloric acid extraction and analyzed on a Bruker Avance 700MHz NMR spectrometer. We also measured salivary levels of several cytokines, chemokines, hormones, and neuropeptides. Results: Partial least squares discriminant analysis showed separation of those children with/without pain for a number of different biomarkers. The majority of the salivary metabolite, neuropeptide, cytokine, and hormone levels were higher in children with pain vs no pain. Conclusions: The ease of collection and noninvasive manner in which the samples were collected and analyzed support the possibility of the regular predictive use of this novel biomarker-monitoring method in clinical practice.
Bibliographical noteFunding Information:
We are grateful for the families and our participants and their willingness to take part in research; our sincere thanks also to Jody Evenson and Gillette Children's Specialty Healthcare Research Administration for assisting in the coordination of this study and Mike Ehrhardt for the cytokine laboratory work. The work was supported, in part, by a Minnesota Futures Research Grant and NIH Grant Nos. 47201 and 69985.
© 2015 American Academy of Pain Medicine.
- Developmental Disability
- Nuclear Magnetic Resonance (NMR)
- Partial Least Squares Discriminant Analysis (PLS-DA)