Serum biomarkers for chronic pancreatitis pain patterns

Abstract

Objectives: Chronic pancreatitis (CP) is associated with debilitating refractory pain. Distinct subtypes of CP pain have been previously characterized based on severity (none, mild-moderate, severe) and temporal (none, intermittent, constant) nature of pain, but no mechanism-based tools are available to guide pain management. This exploratory study was designed to determine if potential pain biomarkers could be detected in patient serum and whether they associate with specific pain patterns.

Methods: Cytokines, chemokines, and peptides associated with nociception and pain were measured in legacy serum samples from CP patients (N = 99) enrolled in the North American Pancreatitis Studies. The unsupervised hierarchical cluster analysis was applied to cluster CP patients based on their biomarker profile. Classification and regression tree was used to assess whether these biomarkers can predict pain outcomes.

Results: The hierarchical cluster analysis revealed a subset of patients with predominantly constant, mild-moderate pain exhibited elevated interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-2 (IL-2), tumor necrosis factor alpha (TNFα), and monocyte chemoattractant protein-1 (MCP1) whereas patients with higher interleukin-4 (IL-4), interleukin-8 (IL-8) and calcitonin gene related peptide (CGRP) were more likely to have severe pain. Interestingly, analyses of each individual biomarker revealed that patients with constant pain had reduced circulating TNFα and fractalkine. Patients with severe pain exhibited a significant reduction in TNFα as well as trends towards lower levels of IL-6 and substance P.

Discussion: The observations from this study indicate that unique pain experiences within the chronic pancreatitis population can be associated with distinct biochemical signatures. These data indicate that further hypothesis-driven analyses combining biochemical measurements and detailed pain phenotyping could be used to develop precision approaches for pain management in patients with chronic pancreatitis.

Keywords: Chronic pancreatitis; Pain biomarkers; Pain frequency; Pain severity; Serum.

Figure 1. Heatmap and hierarchical clustering results

Unsupervised heatmap of biomarker expression across 99 patients. Patients cluster into 3 distinct clusters base upon pattern of expression of putative biomarkers. Each row is an individual patient (patient number indicated on the right side of the map). Color scale indicates relative level of expression with red being the highest level and white the lowest level. The leftmost column indicates temporality, and the second column indicates severity of the patient. However, pain pattern was not included in the clustering algorithm.

Figure 2.. Classification and Regression Tree (CART).

A) A tree showing the probability of patients having no pain, mild/moderate pain, or severe pain predicted by the expression levels of selected markers. Numbers under the leaves refer to the number of observations within that leaf belonging to the class of no pain, mild/moderate pain, and severe pain, respectively. B) A tree showing the probability of patients having intermittent or constant pain predicted by the expression levels of selected markers. Numbers under the leaves refer to the number of observations within that leaf belonging to the class of intermittent and constant pain, respectively. The percentages under the counts refer to the percentage of observations in the leaf. Cutoffs for expression levels of individual markers is given in log2-scale.

Figure 2.. Classification and Regression Tree (CART).

A) A tree showing the probability of patients having no pain, mild/moderate pain, or severe pain predicted by the expression levels of selected markers. Numbers under the leaves refer to the number of observations within that leaf belonging to the class of no pain, mild/moderate pain, and severe pain, respectively. B) A tree showing the probability of patients having intermittent or constant pain predicted by the expression levels of selected markers. Numbers under the leaves refer to the number of observations within that leaf belonging to the class of intermittent and constant pain, respectively. The percentages under the counts refer to the percentage of observations in the leaf. Cutoffs for expression levels of individual markers is given in log2-scale.