Acquired and hereditaryperipheral neuropathies are associated with increased functional connectivity of the left precuneus/posterior cingulate cortex in the default mode network. This increased connectivity in the default mode network is correlated with duration of peripheral neuropathy and severity of clinical total neuropathy score. As discussed in the introduction, if used in combination, biomarkers related to pain mechanisms offer the possibility to develop objective pain-related indicators that may help diagnosis, treatment, and understanding of pain pathophysiology. One possible application of such an approach might be to determine if a patient who is not communicative is experiencing pain. Another example may be to help guide selection of treatment for neuropathy, such as whether transcranial magnetic stimulation may alter network activity among those with neuropathy. Modeling pain brain mechanisms can be achieved using multi-modal brain imaging including functional magnetic resonance imaging, structural magnetic resonance imaging, diffusion tensor magnetic resonance imaging, electroencephalography, EMG, and PET. As we have reviewed here, in addition to using imaging biomarkers, composite pain biomarkers can be investigated using a multitude of non-imaging biomarkers. Multiple analytic approaches have been used to investigate composite pain biomarkers: composite algorithms have been investigated , unsupervised and supervised multivariate analyses have been used to distinguish pain groups and non-pain groups , supervised pattern recognition have been used to cluster diagnostic groups for different pain conditions , mechanism-based pharmacokinetic-pharmacodynamic modeling has been used to identify biomarkers that help diagnose pain and predict pain treatment , principal component analysis has been applied to biochemical markers to create distinct pain profiles ,vertical agriculture patterns of inflammatory blood cytokines and chemokines have been used to differentiate pain and non-pain groups , multi-variable data analysis using simultaneous analysis of 92 inflammation-related proteins with pain intensity and pain thresholds were used to identify protein patterns which distinguish pain and non-pain groups , metabolomics have been applied to chronic pain.
As detailed above, chronic pain and neuropathic pain impact multiple organ systems. Advancing the value of pain biomarkers depends on selection of measurements and metrics that are the most mechanistically valid and informative, and combining the selected measurements such that they mechanistically and statistically maximize accurate classification. Advancement of measurement accuracy is vital and the subsequent steps of the approach are entirely contingent upon the success of this step.In the above reviewed literature, we attempted principally to focus on which biological systems and which biomarkers should be the focus of measurement. For effective application of measurements of these domains it is important to discuss approaches for measurement selection. In Figure 2, we provide a significantly abbreviated schematic of key available statistical approaches to handling multi-modal datasets in building composite biomarkers. We have highlighted four general areas of statistics/machine learning: feature reduction , classification , regression , and clustering. Feature reduction can occur during or prior to classification, regression, or clustering. Feature reduction primarily focuses on two primary approaches: integration of measurements toward creation of a composite variable to simplify and enhance model performance, and effective feature reduction through variable selection to use optimal variables. Thus, feature reduction can represent the effective combining of strong measurements to a meaningful and robust latent variable or elimination of unnecessary, or statistically weak, measurements. Some methods, such as random forest, has built in feature reduction. Classification methods are often utilized to build toward categorical variables, however methods like neural networks are also designed for predicting continuous variables. Regression models are often used for the prediction of continuous measures or in the case of canonical approaches this can be with multiple dependent variables predicted simultaneously. Finally, in the case where there is no existent or optimal category or variable that the biomarkers seek to predict unsupervised approaches can be useful.
With all these approaches variables can either be approached as linear or non-linear, although transformations and feature reduction approaches can mitigate these differences. It is important, regardless of approach, to understand the biological mechanisms being modeled by defining a model that best reflects the underlying systems to optimize prediction. Two key methods for statistical reduction of variables are selecting top ranking variables and creation of composite variables by factor or component-based analysis. Random Forest, as depicted in Figure 2, can be utilized to determine importance scores by evaluating the hierarchical functionality of a given variable as a bifurcator for optimizing classification. Random Forest is not alone in its utility to provide variable importance ranking but provides a nice mechanism for this analysis. The statistical creation of composite variables can be done through principal component analysis such that novel values are calculated for a set of variables that account for large swaths of variance with a single value vector. This can substantially increase the efficiency of a model and serve to highlight a robust latent feature. A summary of pain biomarkers discussed in this review article are provided in Table 1. Non-imaging pain biomarkers include opioid pain biomarkers: Beta-endorphin, B-cell opioid receptors, composite genetic, Mu-opioid receptor A118G polymorphisms, migraine opioid PET, and endogenous opioid function. Inflammatory pain biomarkers include cytokines, sICAM-1, cytokines related to back pain, cytokines related to peripheral neuropathy, substance P, and neuropeptides. Endocannabinoid pain biomarkers include: AEA in CRPS, 2-AG in optic neuromyelitis, AEA and 2-AG in headaches, ECB elements in multiple non-neuropathic pain conditions, ECB elements in endogenous opioid function, and ECB elements in gut-brain interactions. There are pain biomarker genes related to neuropathic pain risk. MICRO-RNA dysregulation pain biomarkers are found in neuropathic pain, peripheral neuropathic pain, CRPS, migraine,hydroponic flood table and non-neuropathic pain conditions. Stress related pain biomarkers include allostatic load, Cortisol, DHEA, and allopregnanolone. Measuring saliva contains potentially particularly accessible pain biomarkers.
Other pain biomarkers can be accessed via QST, skin conductance, pupil dilation, fatty acid pain biomarkers , neurotrophic factors, and serum neurotransmitters. Brain imaging pain biomarkers for measuring pain can be evaluated using three different MRI brain methods: gray matter structural imaging, white matter diffusion tensor imaging, and functional brain activation. Brain circuits related to pain mechanisms include an ascending brain circuit, a descending pain modulation circuit, the default mode circuit, the executive network brain circuit, and finally the salience network. Pain mechanisms in the brain can be measured via modulation in brain circuits: acute pain machine learning measures of chronic pain, pain rumination, pain mind wandering, placebo mechanisms, pain traits and states, and resilience. HIV peripheral neuropathy changes in the brain include reduced total cortical gray matter and reduced posterior cingulate cortex volume in particular, white matter degeneration, altered resting state networks, and aberrant expectation of pain relief. By focusing on a broad array of mechanisms and biomarkers, we can uncover important mechanistic connections and interactions across systems. Neuropathic pain is a debilitating condition that has primary, and cascading affects across body systems. Assessment and understanding in an appropriately comprehensive approach are challenging due to the vast and diverse literature and the complexity measurement. This review aims to facilitate navigation of this literature and the appropriate selection of biomarkers for future research. Although tobacco use has declined in the general population it remains high among people experiencing homelessness ; prevalence of tobacco use among PEH is five times that of the general population. Cancer and heart disease caused by smoking are the leading causes of death among PEH over age 50, and the incidence of these conditions among PEH under 50 is higher than in the age-matched general population.Smoking prevalence among individuals with SMI is 44%-64% compared to 13% in the general population. While population smoking prevalence has decreased, rates have not declined among individuals with SMI. Smoking cessation is particularly challenging among persons living with SMI who may have high levels of nicotine dependence. Persons living with severe depression may experience an increase in depressive symptoms after cessation, increasing relapse to smoking. Nicotine mitigates the neurocognitive deficits associated with schizophrenia; smoking cessation worsens these deficits. Environmental cues to smoking, including the presence of cigarette litter or smoke breaks can create a culture of tobacco use in homeless service settings, negatively impacting quit attempts. Partial smoke-free policies, meaning smoking is not permitted indoors but allowed outdoors on shelter grounds, are acceptable to residents and associated with increased interest in smoking cessation. Tobacco product marketing to PEH and inadequate access to smoking cessation treatment may also contribute to tobacco use and lower quit rates. Although PEH make quit attempts at the same rate as housed populations they are less successful at achieving abstinence. Ten randomized controlled trials of smoking cessation interventions for PEH have included behavioral counseling, pharmacotherapy, and adjunctive treatments like contingent reinforcements. RCTs that used behavioral counseling and pharmacotherapy reported abstinence rates of 9%-17% at 6 months follow-up. Studies using contingent reinforcements for smoking cessation reported higher abstinence rates: 22% at 4 weeks follow-up and 48% at 8 weeks followup. Although these studies established that engaging PEH in cessation trials is feasible, none were integrated with homeless service providers nor did they utilize ancillary staff, such as pharmacists, to provide access to medications or counseling. A recent systematic review of 11 studies explored healthcare professional delivery interventions for PEH outside clinical settings. Only two studies in the UK and Scotland involved pharmacists. In the Scotland PHOENIx study, hospitalized PEH were referred to a pharmacist upon discharge to receive medications, health checks, and referrals. Pharmacist outreach was associated with increased prescribing of medications including anti-hypertensives, diabetes medications, antidepressants, and wound dressings; however, no study involved pharmacists delivering smoking cessation services. In California and other US states, pharmacists can prescribe NRT; in some states pharmacists can also prescribe bupropion and varenicline.Care models for PEH that include pharmacists could increase access to cessation services and medications. In this study, we developed and tested a community pharmacy linked smoking cessation program integrated within two homeless shelters in San Francisco, California. The program included the provision of ad-hoc brief cessation counseling by shelter staff, a cessation counseling session with a pharmacist, and provision of a 3-month supply of NRT delivered on-site. We hypothesized that engagement in the pilot program would increase quit attempts and reduce daily cigarette consumption. This uncontrolled trial was divided into three phases that took place sequentially at each site: 1) training shelter staff how to provide cessation counseling, 2) training shelter staff to become Cessation Champions, 3) and pilot testing medication assistance programs in two shelters in San Francisco, California. Shelter staff included anyone that interfaced with clientele, including case managers, program managers, peer counselors, eligibility workers, and mental health specialists. Phase 1 took place between September 2019 and February 2020. We partnered with eight shelters in San Francisco that collectively housed nearly 1000 PEH nightly. Shelters that agreed to participate selected a time that worked for their staff to receive an in-person training session. At each site, we conducted a 1-hour training with shelter staff who interfaced with clients on how to provide smoking cessation counseling to PEH. The training for shelter staff was provided by a Masters-level Tobacco Treatment Specialist. The training for the shelter staff was developed by the PI and adapted from prior capacity building interventions to increase shelters’ and permanent supportive housing’s capacity to provide smoking cessation services. The training focused on how to provide cessation counseling, relying on the clinical practice guidelines for smoking cessation. Topics included tobacco use among PEH, nicotine addiction, tobacco cessation counseling using the ask, advise, and refer model as well as the 5A’s for smoking cessation, a brief introduction to tobacco cessation medications, local cessation resources and tobacco policy initiatives. Shelter staff were not compensated for attending the training. From the eight sites in Phase 1, we identified two shelters willing to participate in Phase 2. Phase 2 took place between February 2020 and January 2021 and involved training a case manager to be a Cessation Champion at each of the two shelter sites. The Cessation Champion at the first site attended the training during Phase 1 and 2; however, in the second site, the Champion was a newer employee who attended the training only in Phase 2. The Cessation Champion training was provided by the study’s co-investigator, a Doctor of Pharmacy pharmacist with expertise in smoking cessation.