title: Soluble intercellular adhesion molecule-1: a potential biomarker for pain intensity in chronic pain patients.
abstract: OBJECTIVE
  Pain therapy is strongly guided by patients' self-reporting. However, when self-reporting is not an option, pain assessment becomes a challenge and may lead to undertreatment of painful conditions. Pain is a complex and multifactorial phenomenon. Recent work has connected pain pathophysiology also with the inflammatory system. We therefore hypothesized that pain intensity could be predicted by cytokine-levels.
  
  
  METHODS
  In this observational, single-center study, we investigated 30 serum cytokines to predict pain intensity in a screening/follow-up set of 95 chronic pain patients and controls. We then prospectively validated soluble intercellular adhesion molecule-1 (sICAM-1)'s discriminatory capability (n = 21).
  
  
  CONCLUSIONS
  sICAM-1 was significantly associated with patient-reported pain intensity and yielded differential serum levels in patients of varying degrees of pain intensity. Changes in pain ratings over time correlated with changes in sICAM-1 levels. Our findings suggest the possibility of a clinical use of sICAM-1 as a potential biomarker for pain intensity.
authors.affiliation: Department of Anesthesiology & Pain Medicine, Clinic of the Ludwig-Maximilians University of Munich, Germany.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Luchting
authors.affiliation: Department of Anesthesiology & Pain Medicine, Clinic of the Ludwig-Maximilians University of Munich, Germany.
authors.forename: Ludwig Christian Giuseppe
authors.initials: LC
authors.lastname: Hinske
authors.affiliation: Department of Anesthesiology & Pain Medicine, Clinic of the Ludwig-Maximilians University of Munich, Germany.
authors.forename: Banafscheh
authors.initials: B
authors.lastname: Rachinger-Adam
authors.affiliation: Harvard-MIT Division of Health Science & Technology, Boston, MA 02139, USA.|Intensive Care Unit, Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, MA 02215, USA.
authors.forename: Leo Anthony
authors.initials: LA
authors.lastname: Celi
authors.affiliation: Department of Anesthesiology & Pain Medicine, Clinic of the Ludwig-Maximilians University of Munich, Germany.
authors.forename: Simone
authors.initials: S
authors.lastname: Kreth
authors.affiliation: Department of Anesthesiology & Pain Medicine, Clinic of the Ludwig-Maximilians University of Munich, Germany.
authors.forename: Shahnaz Christina
authors.initials: SC
authors.lastname: Azad
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001499:Bayes Theorem; D015415:Biomarkers / Q000097:blood*; D016022:Case-Control Studies; D059350:Chronic Pain / Q000175:diagnosis* / Q000473:pathology; D016207:Cytokines / Q000097:blood; D004797:Enzyme-Linked Immunosorbent Assay; D005260:Female; D006801:Humans; D018799:Intercellular Adhesion Molecule-1 / Q000097:blood*; D008297:Male; D008875:Middle Aged; D010147:Pain Measurement; D012372:ROC Curve; D055815:Young Adult
chemical_list: D015415:Biomarkers; D016207:Cytokines; D018799:Intercellular Adhesion Molecule-1
keywords: biomarker; chronic pain; cytokines; inflammation; pain assessment; pain intensity; pain management; predictive modeling; sICAM-1