Here we present data gathered by Dr. Jon Berner MD PhD in the course of treatment. Data were collected by Baylor College of Medicine through an untargeted metabolomic analysis with cerebrospinal fluid. Patient data were collected from treatment refractory patients with chronic pain and comorbid psychiatric conditions including: bipolar, depression, and anxiety. Through the use of novel technological methods, Dr. Berner hopes to elucidate underlying metabolic contributors and secondary effects of chronic pain.

Original patient data as gathered by Baylor are published below. Data collection methodology is described in a paper by Miller et al. All z-scores were calculated using an internal database at Baylor representing a large number of reference data (N>300). Data will be presented at the 2021 AAPM Conference, a preliminary abstract is included below.

Patient 1

Methods

Miller MJ, Kennedy AD, Eckhart AD, Burrage LC, Wulff JE, Miller LA, Milburn MV, Ryals JA, Beaudet AL, Sun Q, Sutton VR, Elsea SH “Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism.” J Inherit Metab Dis. 2015 April 15; 38 : 1029-39. Pubmed PMID: 25875217

Polyol pathway activation in the CSF uniquely correlates with pain and opioid use
Kyla Shade BS, Jon Berner MD PhD, Sarah Elsea PhD

Although molecular signatures of chronic pain have previously been identified in the CSF looking at small subset of markers (NGF, substance P, 5-HIAA) , examination of the high dimensional metabolome (N=300) in clinical populations has only recently become technically feasible. We report pilot data from a small clinical sample (N=27) of refractory patients.

Exploratory data analysis reviewed the correlation matrix defined by demographic variables (age, gender), structured symptoms checklists (disability, anxiety, depression, autism), concurrent medication use (antidepressants, stimulants, benzodiazepines, lithium, opioids, ketamine), chief complaint (pain, bipolar disorder) and a subset of 18 metabolic variables z-scores. These variables were selected out of the larger metabolome given frequency of appearance greater or lower than 2 z-scores in an individual patient metabolome profile.

Activation of the polyol pathway was uniquely seen in pain patients on opioids. Sorbitol z-scores correlated highly with both pain (r. = 0.451, p.=0. 018) and opioid use (r. =0.573, p.=0.002). Upstream (glucose) and downstream metabolites (fructose) had consistent r. values for both pain (glucose r. = 0.33, fructose r. = 0.11) and opioid use (glucose r.= 0.36, fructose r. = 0.38) although p. values were less compelling (3/4 < p. = 0.1).

Although direction of causation cannot be established with correlational analysis, there is a wealth of evidence implicating polyol pathway activation in painful diabetic neuropathy, subtypes of Charcot-Marie-Tooth Disease, leukoencephalopathy in bipolar disorder, and disability status in multiple sclerosis. Further research is indicated to replicate exploratory univariate analysis, create more sensitive and robust multivariate tools, define possible predictive validity for opioid and/or aldose reductase inhibitor response in drug-naive patients, and potentially link this clinical data with basic science findings on TRP regulation of the sickness behavioral response driven by osmolality changes during hibernation and/or starvation.