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Using stratified medicine to understand, diagnose, and treat neuropathic pain

Themistocleous, Andreas C.a; Crombez, Geertb; Baskozos, Georgiosa; Bennett, David L.a,*

doi: 10.1097/j.pain.0000000000001301
Biennial Review of Pain
Global Year 2018

aThe Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom,

bDepartment of Experimental-Clinical and Health Psychology, Ghent University, Ghent, Belgium

Corresponding author. Address: West Wing, Level 6, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom. Tel.: +44(0)1865 231512; fax: 01865 234 830. E-mail address: david.bennett@ndcn.ox.ac.uk (D.L. Bennett).

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Received April 30, 2018

Received in revised form May 23, 2018

Accepted May 25, 2018

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1. Introduction

Neuropathic pain (NeuP) is defined as pain arising from a lesion or disease of the somatosensory nervous system.38,86 Neuropathic pain is common, affecting approximately 6% to 8% of the general population,13,84 and currently, treatment is inadequate because of both poor drug efficacy and tolerability.37 Many different types of injury can cause NeuP including genetic (eg, SCN9A gain of function variants), metabolic (eg, diabetic polyneuropathy), infective (eg, HIV-associated neuropathy and hepatitis), traumatic, and toxic (eg, chemotherapy-induced neuropathy) causes. Such injurious events can impact on anatomically distinct regions of the somatosensory nervous system ranging from the terminals of nociceptive afferents (in small fiber neuropathy [SFN]) to the thalamus (in poststroke pain). Classification of NeuP using etiology and location remains an important aspect of routine clinical practice; however, pain medicine is coming to the realization that we need more precision in this classification. The hope is that improved classification will lead to better understanding of risk, prognosis, and optimal treatment of NeuP.

Patient stratification is the process of identifying subgroups of patients, suffering from a disorder (such as NeuP) to better target medical intervention.90 Such subgroups may map to a particular pathogenic mechanism but could also simply be a constellation of clinical symptoms and signs or biomarker, which are predictive of treatment response. Personalized medicine aims to target intervention to individual patients and is therefore even more ambitious in scope.66 Personalized medicine may be possible in rare cases of NeuP (usually associated with specific gene mutations), but for the most part, we will discuss stratified pain medicine in this review.

Both preclinical and clinical science have identified an array of pathogenic mechanisms underlying NeuP ranging from ectopic activity in primary afferents to defective central pain modulation pathway (for a comprehensive review, see Ref. 17). It is not a new idea that we should be trying to understand pain mechanisms in patients,104,105 although there are challenges in being able to assess specific mechanisms in individual patients. Stratification aims to achieve patient subgroupings that have utility in terms of diagnosis, prognosis, or treatment, and this may not relate to a single pathogenic mechanism. Fortunately, our armamentarium for identifying patient subgroups (and in some cases, directly assaying pathogenic mechanisms) in patients has greatly improved. In the first section of this article, we will review the means by which patients with NeuP may be stratified and in the second section, the potential benefits of stratification. Thomas Lewis said, “Diagnosis is a system of more or less accurate guessing, in which the end point achieved is a name. These names applied to disease come to assume the importance of specific entities, whereas they are for the most part no more than insecure and therefore temporary conceptions.” He was likely exaggerating for effect, but we hope that patient stratification will not only reduce the uncertainty in diagnosis but also help improve prevention, prognostication, and treatment.

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2. How can we stratify patients with neuropathic pain?

As in all medicine, detailed clinical history and examination remain important in the assessment of NeuP. An important aspect on history is the temporal course of the pain onset and its relationship to the underlying disease process. The examination should be comprehensive and relevant to the disease process and history. For example, the presence of limb erythema with a diagnosis of erythromelalgia or absent lower limb reflexes as a consequence of peripheral neuropathy. Stratification of patients with NeuP incorporates a multidisciplinary approach. Figure 1 provides a schematic representation of some of the techniques that can be used to stratify patients with NeuP. A detailed description of the techniques will be discussed below.

Figure 1

Figure 1

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2.1. Sensory phenotype

In the past decade, significant advances in techniques to define somatosensory phenotype in the context of NeuP have been developed. These include questionnaires to assess pain quality, psychophysical tools to assess sensory perception, and alteration of experimental pain through conditioned pain modulation (CPM).

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2.1.1. Pain quality

A variety of tools have been developed to both screen and characterize the qualities of NeuP. Screening questionnaires, such as the DN4,11 painDETECT,41 and LANSS,7,8 are used to identify patients with NeuP. The screening questionnaires incorporate descriptors of sensory symptoms to generate a score that helps predict whether the pain is likely to be neuropathic. Examples include “burning” quality to pain or the presence of paresthesias. The DN4 also includes an examination component to test for sensory loss and/or allodynia. The above questionnaires can be used to screen for NeuP at a primary care level.1 For example, the DN4 questionnaire has demonstrated excellent sensitivity and specificity in screening for NeuP in patients diagnosed with diabetic neuropathy (DPN).79,83 The screening questionnaires have been validated to discriminate between NeuP and non-NeuP and translated into over 90 languages.1 The Neuropathic Pain Symptom Inventory (NPSI)12 is a self-administered questionnaire developed to characterize the qualities of NeuP.

A major advantage of these questionnaires is that they are self-administered and can be used to capture data from large cohorts of patients. Analysis of large data sets has shown that NeuP caused by different etiologies shares sensory symptom profiles.5,40 The profiles may reflect different pathophysiological pathways, independent of etiology, that cause NeuP. Hierarchical cluster analysis based on the painDETECT questionnaire of 2100 patients diagnosed with painful DPN or postherpetic neuralgia revealed 5 distinct symptom profile subgroups.5 The different subgroups occurred in both groups of patients. Principal component analysis and hierarchical cluster analysis of individual pain dimensions based on NPSI descriptors completed by 1225 patients (diagnosed with central poststroke pain, painful DPN, painful HIV neuropathy, and posttraumatic peripheral pain) identified 3 clusters with distinct symptom profiles.40 The 3 clusters represented 3 different subgroups of patients who were seen across the different NeuP syndromes. A smaller study identified 6 distinct NeuP profiles, based on the NPSI, among patients with a variety of NeuP syndromes.78 Although different clusters were identified in each study, grouping of patients based on sensory symptom profiles rather than solely etiology may yield new understanding of NeuP neurobiology and improve response to pain therapies. A step forward is the integration of questionnaires and sensory testing to better capture somatosensory profiles.97

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2.1.2. Quantitative sensory testing

Quantitative sensory testing (QST) is a psychophysical tool that assesses evoked sensory perception in response to a defined sensory stimulus.67 The German research network of NeuP (DFNS) developed and validated a standardized QST protocol that tests 13 parameters of sensory function.68 The sensory modalities include small fiber sensory function, such as thermal detection/pain thresholds and pinprick sensitivity, and large fiber sensory function, such as mechanical and vibration detection thresholds. The standardization of QST data collection has significant advantages. Data collected across different centers can be compared against a large control population cohort, controlling for age and sex effects, and be combined to significantly increase statistical power.56,57,96 A limitation of QST is that it requires a significant investment in equipment and examinations are lengthy. In a recent study, QST profiles of 1135 patients collected from multiple centers with peripheral NeuP revealed 3 distinct phenotypes.4 The 3 phenotypes were characterized by sensory loss, thermal hyperalgesia, and mechanical hyperalgesia. These phenotypes can be found across different etiologies of NeuP but vary in frequency.98 For example, the most common phenotype in diabetic polyneuropathy is sensory loss (64%), followed by mechanical hyperalgesia (20%) and thermal hyperalgesia (17%). By contrast, postherpetic neuralgia is characterized by the mechanical hyperalgesia phenotype (45%), followed by thermal hyperalgesia (35%) and sensory loss (20%). Such stratification of NeuP may yield a greater understanding of the pathophysiological mechanisms that are shared across somatosensory phenotypes or specific to etiology. Somatosensory profiles can also be used to predict treatment response (discussed below).

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2.1.3. Conditioned pain modulation

Conditioned pain modulation refers to the dynamic psychophysical protocols that provide insight into an individual's inhibitory pain modulation processes.44 If a patient is asked to rate the pain intensity of a certain “test stimulus” (such as contact heat applied to the volar surface of the forearm), and then given the combination of a noxious “conditioning stimulus” (such as immersion of the opposite hand in a hot water bath) and a repeated similar “test stimulus,” the perceived pain intensity of the latter “test stimulus” will generally be lower than when given alone. Conditioned pain modulation efficiency refers to the reduction of pain intensity between the 2 “test stimuli.” Less efficient CPM was reported for patients with chemotherapy-induced NeuP61 and peripheral neuropathy91 when compared with healthy control participants. Thus, impaired inhibitory pain modulation processes may be present in patients suffering from NeuP. There is a growing body of evidence suggesting that CPM may be an important biomarker of chronic pain and a predictor of treatment response. Less efficient preoperative CPM may predict chronic postoperative pain.103,109 Less efficient CPM was observed in a group of patients with painful DPN that reported a larger analgesic response to duloxetine (see below).110 Although CPM holds great promise, limiting factors include the heterogeneity of protocols, significant variability reported in the size and stability of the CPM effect in healthy volunteers, and the inability to disentangle different mechanisms in individuals with different causes of chronic pain.53

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2.2. Physiological measures: electrophysiology and functional brain imaging

Standard neurophysiological techniques, such as nerve conduction studies, investigation of trigeminal reflexes (including the blink reflex), and measurement of somatosensory evoked potentials, are commonly used to investigate NeuP.24 These techniques are broadly designed to assess the nonnociceptive pathways. They are most helpful in confirming a lesion within the peripheral or central somatosensory nervous system. Despite not assessing the pain pathways directly (as C-fibre activity is poorly represented in these outputs), emerging evidence does implicate focal demyelination of nonnociceptive Aβ fibers in NeuP related to carpal tunnel syndrome89 and ophthalmic postherpetic neuralgia,87 as these abnormalities are correlated with paroxysmal pain and abnormal sensations. Laser-evoked potentials are the preferred technique for assessment of nociceptive pathway function, because of ease of use and reliability.23 Pulses of laser-generated radiant heat are used to selectively excite free nerve endings in the superficial skin layers, which activates Aδ and C nociceptors and gives rise to brain-evoked potentials specifically related to activation of ascending thermal-pain systems. Suppression of laser-evoked potentials suggests a diagnosis of NeuP.87–89 Laser-evoked potentials' amplitudes are correlated with the severity of constant pain in patients with carpal tunnel syndrome89 and ophthalmic postherpetic neuralgia.87 Microneurography is a unique neurophysiological technique that uses a microelectrode to record nerve activity directly from a peripheral nerve fascicle. It has been used to directly study nociceptor afferent activity in a wide range of NeuP conditions.30 Abnormal patterns of firing and distribution of nociceptive afferent subclasses have been identified in conditions such as painful DPN,63 painful neuropathy,55 SFN,71 and erythromelalgia.62,64 Such aberrant activity is believed to be a key driver of peripheral NeuP. The functional brain imaging field has adopted stratification of patients to identify pathological mechanisms of pain.85 The descending pain modulatory system (DPMS) is a brainstem–subcortical–cortical network that can modulate nociceptive input to the brain. Preclinical studies have shown that DPMS is important in chronic pain states. Studies that have stratified patients according to NeuP contribution have shown that persistent pain may be linked to an imbalance in DPMS function, either because of a diminished inhibitory or an enhanced facilitatory capacity of the DPMS.45,59,70 Patients with hip osteoarthritis pain45 (before hip replacement surgery) that scored higher on painDETECT (ie, NeuP contribution more likely) demonstrated increased facilitatory DPMS activity when compared with patients who scored lower on painDETECT (ie, NeuP contribution less likely). Furthermore, functional brain imaging has been used to disambiguate the efficacy of different pain treatments using an experimental model of central sensitization, which is a contributory pathomechanism of NeuP.100 After capsaicin induced central sensitization, gabapentin (a NeuP medication), when compared with placebo and ibuprofen (non-NeuP medication), suppressed resting-state connectivity and secondary mechanical hyperalgesia evoked neural response in a region of the brainstem DPMS.

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2.3. Molecular profiling

Genomics is having a growing influence on medical practice in providing a molecular pathogenic link to disease as well as clinically relevant outcomes such as treatment response. There are many genes that have a role in the pathogenesis of NeuP; however, we will focus on variants in the gene SCN9a that provides one of the best examples of modern genomics applied to pain medicine.113 SCN9a encodes Nav1.76,29 that is a voltage-gated sodium channel (VGSC) expressed by sensory neurons. A number of rare pain disorders that are inherited in a Mendelian fashion are associated with mutations in this gene. Biallelic inactivating mutations in Nav1.7 result in congenital insensitivity to pain and anosmia.19 Heterozygous gain of function mutations in the same channel can lead to inherited erythromelalgia108 (IEM, characterized by pain and erythema of the extremities exacerbated by warmth) or paroxysmal extreme pain disorder (associated with episodic pain and erythema of the sacrum and mandible triggered by mechanical stimulation).36 Inherited erythromelalgia provides an excellent example of how a molecular mechanism links to a pathophysiological pain driver. Nav1.7 mutations causing IEM result in gain of function of Nav1.725 (resulting in hyperexcitability of sensory neurons been demonstrated both experimentally and by microneurographic recordings from patients with IEM). There is a broad correlation between the biophysical dysfunction of the ion channel and the associated pain syndrome: IEM mutations causing a greater hyperpolarising shift in the voltage dependence of activation result in a more severe clinical phenotype.21,47

Small fiber neuropathy is a more common condition than IEM presenting with burning pain of the extremities associated with small fiber degeneration.82 A number of rare Nav1.7 variants (which are distinct to those causing IEM) have now also been linked to SFN and lead to gain of function in this ion channel.35 Nav1.7 also provides a good example of how certain gene variants may not cause Mendelian pain disorders but contribute as risk factors for the development and severity of much more common acquired NeuP states. The concept being that such variants would not cause symptoms in the naive state but can contribute to NeuP in the context of an environmental stressor such as the development of DPN. Studying a carefully phenotyped cohort of patients with DPN, there was a higher prevalence of rare Nav1.7 variants in those patients with painful (10% of patients) vs painless DPN (0 patients).10 Two of these novel variants associated with painful DPN were shown to impair inactivation of Nav1.7 resulting in gain of function providing a physiological link to the development of pain.

Because not all Nav1.7 variants are likely to be pathogenic, careful genetic counselling is required and functional analysis of Nav1.7 variants remains critical.101 Genomics is now increasingly been integrated into clinical practice and the “100,000 genomes” project will sequence the whole genomes of 75,000 people suffering from rare disorders (including familial pain disorders) as part of routine NHS care within the United Kingdom.76 In the future, it may become routine to sequence the genomes of large populations to appropriately target health care. The technology for such sequencing is available, although there are still great challenges in information processing and ascribing pathogenicity to the variants found. Techniques are also being developed for high-throughput assessment of epigenetic changes as well as the downstream effects of gene function including mRNA expression (transcriptome), protein expression (proteome), and metabolites (metabolome).69 In the future, these may also be helpful in stratifying patients with NeuP. One issue is that unlike oncology, pathological material from the somatosensory nervous system is not easily accessible. However, it is becoming possible to generate induced pleuripotent stem cells from patients, which can provide a scalable source of sensory neurons16 for molecular, physiological,102 and even pharmacological profiling.15 This really would be an example of “personalized” pain medicine; however, it is likely to have most utility in situations where there are strong genetic drivers of NeuP and the workflow would need to be streamlined before this could be used in routine clinical practice. For now, this is restricted to research practice.

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2.4. Psychological profile and comorbidities

Neuropathic pain, as with every form of pain, alarms, demands attention, and interferes with ongoing activities.31 Consequently, patients with NeuP experience a lower ability to accomplish tasks of daily living, a lower quality of life, a lower mood, and more sleep problems than those without pain.50,77 It may be expected that the presence of NeuP triggers a cascade of psychosocial processes that may finally maintain or exacerbate suffering, distress, and disability.

To a large extent, these processes are similar to those involved in other forms of pain.22,32 Just as with musculoskeletal pain, anxiety or worrying about the pain and its possible consequences may lead to avoidance, and to more pain, distress, and disability.95 Nevertheless, the experience of NeuP has some particularities.26 Avoidance seems to be less triggered by a fear that physical activities will increase pain or worsen their condition. Patients with NeuP may rather avoid social situations because the feeling of clothes against the skin is uncomfortable. The unpredictable nature of paroxysmal pain may turn patients generally anxious and uncertain. These specific features in the phenomenology of NeuP need to be further explored.

We should go beyond a documentation of the comorbidities that patients experience. We need to understand how exactly these problems come about. It will be useful to put the assessment and treatment of NeuP within the psychological context of the primary disorder. The patient struggling with diabetes and painful DPN has different needs from the patient with HIV neuropathy, who both have different needs from the patient with postmastectomy pain syndrome. That way, we will identify what exactly patients are worried about, their specific beliefs about illness, pain, and treatment, and how these factors impact their life. Pain management programs will need to be tailored and adapted to account for the specific contexts of NeuP.26 Unfortunately, there are yet insufficient clinical trials allowing us to conclude that psychological treatments for NeuP work.34

Overall, research on the role of psychological variables in NeuP is a relatively unexplored territory. It largely consists of cross-sectional studies. We do not know yet whether and how exactly psychological variables causally contribute to the development or maintenance of NeuP.50 Neuropathic pain may well be a condition in which biobehavioural variables interact from the onset. Anxiety, depression, and stress may have a direct impact on disease processes and pain. No study has yet explored this hypothesis. Notwithstanding, there is strong evidence that anxiety, depression, and stress contribute to the disease onset and may delay wound healing through the immune and neuroendocrine system.54 Psychological factors may also indirectly affect disease. Cognitions and emotions may be obstacles for the adoption of a healthy lifestyle, treatment adherence, and optimal self-management. Each of these pathways may affect underlying disease mechanisms. In diabetes mellitus, patients who are anxious and depressed are less physically active and eat less healthy, exacerbating disease processes. Patients who have a low mood are less adherent to their medication regime.43 Inappropriate beliefs about the illness and treatment may lead to suboptimal treatment and poor self-management.94

A more context sensitive approach to the psychology of chronic NeuP is needed that builds on what we know from general behavioural science and behavioural pain medicine,33 but that translates it to the needs of the specific patient group.

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2.5. Data integration

An important question is to what extent is stratification based on different modalities correlated? Taking genotype and sensory profile as an example, there is a link between the 2, but this is not an exact match. Patients with IEM with known mutations in Nav1.7 actually showed surprising diversity in their sensory profile, although the vast majority did show heat pain hypersensitivity measured by QST at unaffected skin sites.58 In painful DPN, there was a correlation between genotypes and sensory profile but only to one measure: enhanced pressure pain sensitivity was noted in those patients with painful DPN with rare Nav1.7 variants compared to those patients without rare Nav1.7 variants.10 Taking the approach of starting with the sensory profile of patients with NeuP and then sequencing candidate genes, Binder et al.9 showed that variants in TRPA1 (an ion channel activated by environmental irritants and cold) were associated with paradoxical heat sensation.

Ultimately, the intersection between different modalities may be particularly helpful in stratification. We are in the era of “big data” (data generated in large volume, at high velocity, and in a variety of formats) in which bioinformatics approaches can be used to integrate prospective electronic health records, routine investigations, and specialized tests using biobank material.18,69 This requires significant computing power as well as the ability to deal with the security and ethical challenges associated with such large amounts of personal data. It will be extremely powerful in generating hypotheses that can then be tested in focused cohorts providing potent opportunities for future research.

Multivariate analysis enables the study of multiple different, possibly correlated, factors as a cause of variation within a population and their relationship to pain. To provide an example, we undertook principal components analysis in patients with painful DPN.74 This revealed that the relationship between pain and different clinical and psychological factors was dependent on sex in patients with painful DPN. Multivariate principal components analysis showed that anxiety (as measured with the Depression, Anxiety, Positive, Outlook Scale [DAPOS] questionnaire), poor glucose control (high HbA1c), high body mass index, and high 7-Day pain diary scores were more prevalent in females, whereas more severe neuropathy (as assessed using the Toronto Clinical Scoring System [TCSS] and intraepidermal nerve fibre density [IENFD]) was more prevalent in males (Fig. 2). These findings emphasise the importance of one of the simplest forms of stratification: sex, but also the utility in studying multiple variables.

Figure 2

Figure 2

If a stratification measure only has a small effect size or is overly complex and time-consuming, it will not be adopted in clinical practice. For final clinical use, therefore stratification measures will require extensive optimization and field testing.

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3. Utility of patient stratification

3.1. Diagnosis of neuropathic pain

An important step in the stratification of patients is to determine the certainty of NeuP diagnosis on an individual basis. A revised grading of NeuP has been developed by Neuropathic Pain Special Interest Group (NeuPSIG) of the International Association for the Study of Pain to facilitate the correct classification of pain as neuropathic.38 The grading is based on the following criteria. Possible NeuP must fulfil criteria 1 and 2. Probable NeuP must fulfil criteria 1, 2, and 3. Definite NeuP must fulfil all 4 criteria.

  • (1) Pain with a distinct neuroanatomically plausible distribution.
  • (2) A history suggestive of a relevant lesion or disease affecting the peripheral or central somatosensory system.
  • (3) Demonstration of distinct neuroanatomically plausible distribution of NeuP.
  • (4) Demonstration of the relevant lesion or disease by at least one confirmatory test.

Neuropathic pain has been shown to be present in a number of previously poorly understood conditions, such as recessive dystrophic epidermolysis bullosa and nonfreezing cold injury,93,99 in which a neuropathic component may not have been suspected or described. Recessive dystrophic epidermolysis bullosa is an inherited dermal condition characterized by bullous eruption of the skin and is associated with severe, debilitating pain. Application of the new NeupSIG grading system demonstrated that 62% of patients with epidermolysis bullosa had a definite diagnosis of NeuP, 24% had a probable diagnosis of NeuP, and 13.7% had a possible diagnosis of NeuP.99 Based on this finding, inherited epidermolysis bullosa was shown to cause a SFN and patients were started on appropriate NeuP therapies. Nonfreezing cold injury is an umbrella term used to describe an environmental injury in which soldiers who are exposed to cold and wet conditions can develop pain and sensory disturbance of the feet and hands. We showed using detailed clinical examination, QST, and skin biopsy to determine IENFD that the sensory disturbance is caused by a sensory neuropathy, and application of the new NeupSIG grading system demonstrated that 95.2% of patients with nonfreezing cold injury had a definite diagnosis of NeuP (Fig. 3).93 The demonstration of impaired small fiber function in fibromyalgia, in particular, is interesting. Fibromyalgia is a syndrome characterized by widespread pain. Careful phenotyping using the NPSI questionnaire, clinical examination, electrophysiology including pain-evoked potentials, skin biopsy for IENFD, and microneurography demonstrated that the pain experienced in fibromyalgia has a significant neuropathic component caused by dysfunction within small fibers.72,92

Figure 3

Figure 3

The revised NeuP grading is a significant improvement on previous approaches, as it offers a methodical and hierarchical process of diagnosis that can be applied in clinical and research settings. It provides a rational basis to prioritize investigations and to commence appropriate NeuP treatment.

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3.2. Understanding pathogenic mechanisms underlying neuropathic pain in patients

Neuropathic pain is a complex multidimensional clinical entity, and the underlying pathogenic mechanisms that cause NeuP are not understood. A number of pathogenic mechanisms, based on preclinical studies, are postulated to play a role in acquired NeuP disorders, such as painful DPN.80 We believe that a stratified approach can help translate findings between the clinical and preclinical arenas. As an example of the strength of patient stratification, we describe how a large multicenter observational study incorporated a complex multidisciplinary approach to explore the pathophysiological mechanisms of chronic painful DPN. The first step was the recruitment of a large of cohort of patients who satisfied criteria for definite DPN.81 A total of 191 patients with DPN underwent neurological examination, QST, nerve conduction studies, and skin biopsy for IENFD assessment. A set of questionnaires assessed the presence of pain, pain intensity, pain distribution, and the psychological and functional impact of pain.83 We then used the NeupSIG grading system of NeuP to separate the cohort. Participants were divided into those with painful DPN (NeuP present for at least 3 months) and painless DPN (those without NeuP). We showed that there was a positive correlation between greater neuropathy severity, poorer diabetic control, and the presence (and severity) of NeuP. This link to neuropathy severity has been independently confirmed by Raputova et al.65 Diabetic neuropathy sensory phenotype was characterized by hyposensitivity to applied stimuli, which was more marked in those with more severe painful DPN. Therefore, the sensory profile of patients with painful DPN was distinct from those patients with painless DPN. Once our patient cohort was carefully phenotyped and stratified, we investigated underlying pathogenic mechanisms. We first explored the contribution of genetic variability in NeuP and examined the relationship between variants in Nav1.7 and NeuP.10 No rare variants were found in participants with painless DPN, we identified 12 rare Nav1.7 variants in 10 (of 111) study participants with painful DPN. Five of these variants had previously been described in the context of other NeuP disorders and 7 have not previously been linked to NeuP. Those patients with rare variants reported more severe pain and greater sensitivity to pressure stimuli on QST. In vitro electrophysiological characterisation for 2 of the novel variants demonstrated gain of function changes as a consequence of markedly impaired channel fast inactivation. We were therefore able to link the patient phenotype/genotype to changes within the biophysical properties of Nav1.7. We then went on to use functional brain imaging to study the neural correlates of chronic NeuP in those with painful DPN using a carefully matched group of patients with painless DPN as control.70 We found that the ventrolateral periaqueductal gray that is an important centre for descending pain modulation was dysfunctional in those patients with painful DPN. The dysfunction refers to altered connectivity between the ventrolateral periaqueductal gray and the DPMS that may enhance incoming nociceptive input. The degree of dysfunction correlated with the intensity of spontaneous pain and the size of cortical response to an experimental tonic heat pain. This suggests that a brain-based pain-facilitating mechanism contributes to chronic NeuP in DPN. In aggregate, these findings illustrate how patient stratification and multidisciplinary investigation can yield important insights into potential pathogenic mechanisms underlying NeuP.

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3.3. How can patient stratification aid treatment selection?

The most obvious example where improved patient stratification is already aiding treatment selection is using screening tools and (in some cases) more specialized, investigations to recognize pain as neuropathic (as opposed to nociceptive) to initiate appropriate therapy. Once pain is recognized as neuropathic, how can we better target therapies to optimize the likelihood of response and minimize side effects? Currently, first-line agents for the treatment of NeuP include: tricyclic antidepressants (eg, amitriptyline), dual serotonin and noradrenaline reuptake inhibitors (eg, duloxetine), and the gabapentinoids (eg, pregabalin and gabapentin).37 Unfortunately, for NeuP conditions, these agents have failure rates of ≥70% in painful DPN and postherpetic neuralgia.60 However, when patients do respond this is usually within the first month of treatment, the response is lasting and is often accompanied by improved sleep and mood. Currently, initial treatment selection is usually empirical and is not guided by predicted efficacy but by pragmatic decisions on tolerability and often the personal experience of the prescriber (Fig. 4). Furthermore, there is a growing list of second-line agents such as opiates, the high-dose capsaicin patch, lidocaine plasters, and botulinum toxin.37 In certain cases, antiepileptic drugs that block VGSCs may be beneficial. One good example is trigeminal neuralgia that responds to carbamazepine.111 The hope is that NeuP patient stratification will facilitate initial treatment selection to optimize early pain relief and also reduce exposure to drugs that are unlikely to be effective. Such an approach could also enhance clinical trial design by stratifying patients into those who are most likely to respond to the study medication.

Figure 4

Figure 4

The pain channelopathies provide an excellent example as to how identifying gene mutations, assessing their impact on channel biophysics, pharmacology, and structure in vitro and in silico then enables us to predict treatment response. Inherited erythromelalgia is notoriously difficult to treat. Mexiletine is a drug related to the local anesthetic lidocaine and is active orally. General NeuP treatment guidelines actually advise against the use of mexiletine37 because of poor efficacy and cardiac side effects. However, mexiletine's activity in blocking mutant Nav1.7 demonstrated in vitro for certain IEM-related mutations,20 means that it can be helpful in certain cases of IEM. There are more than 30 mutations that can cause IEM, and there are a variety of drugs that can block VGSCs (including both local anesthetics and antiepileptic drugs). Is there any means of predicting which mutations (and hence which patients) will respond to which drug?

The fact that the structure of VGSCs has recently been solved at near-atomic resolution73 means that we are now able to visualize where a single mutated residue resides within the 3D structure of Nav1.7 and potentially model its impact. Most IEM-related Nav1.7 mutations do not respond to the nonselective VGSC blocker carbamazepine; however, patients with the V400M were found to clinically respond39,107 and the effects of this mutation on the channel (hyperpolarizing the voltage dependence of activation) could be reversed by carbamazepine. Structural modelling of Nav1.7 was used to show that 2 other mutations (S241T and I234T) were in close proximity to V400M in 3D space (but note not in the linear amino acid sequence).106,107 Both of these mutations led to gain of function in Nav1.7 and dorsal root ganglion cell hyperexcitability and in accordance with the structural prediction, these effects were normalized by carbamazepine. The acid test of this hypothesis was the subsequent finding that 2 patients carrying the S241T mutation responded to carbamazepine in a double-blind randomized placebo-controlled study.42 This is a small trial in a rare condition but provides proof of concept as to how molecular genetics and structural modelling can provide insights relevant to distinct ion channels and clinical disorders. Such molecular profiling is now becoming relevant to more common acquired NeuP conditions.10 In genetic analysis of a patient suffering from NeuP secondary to painful DPN, we discovered a mutation (S242T) in Nav1.8. This VGSC is also expressed in sensory neurons and is distinct from but shares homology with Nav1.7. Indeed, this Nav1.8 mutation is homologous to the carbamazepine-responsive S241T mutation in Nav1.7. This variant was found to cause gain of function in Nav1.8 and dorsal root ganglion neuron hyperexcitability by our collaborators Waxman and Dib-Hajj and as predicted from in silico analysis, these changes could be normalized by carbamazepine.48 A recent trial using lacosamide in SFN provides another example of using molecular genetics to stratify patients with NeuP. Lacosamide is an antiepileptic drug that has activity against VGSCs including Nav1.7.52 Lacosamide when used in unstratified NeuP cohorts such as painful DPN has at best limited efficacy.49 This randomized, double-blind, placebo-controlled trial recruited patients with SFN and specifically those with mutation in Nav1.7. Lacosamide treatment in this group showed significant analgesic efficacy in comparison with placebo.27 The fact that more specific blockers of VGSCs are under clinical development112 will give added impetus to using genetics to identify mutations in these ion channels. Pharmacogenomics is not restricted to prediction of efficacy but can also be used as a means of predicting adverse outcomes (to take a topical example, the risk of opiate addiction), and this may be a further application of genomics to pain medicine in the future.46

Sensory profiling is a further stratification measure that may help target treatment.3 This has been incorporated into a number of clinical trials to determine in retrospective analysis whether stratification according to sensory profile at baseline can predict response to the study medication. This has proved the case in a number of studies (for examples, see Refs. 2,75), although the findings vary depending on the drug class analyzed.51 One recent study was designed to test “a priori” that patient stratification using sensory profiling could help predict treatment efficacy.28 Patients with painful neuropathy underwent QST at baseline, which was used to assign patients to an irritable nociceptor or the nonirritable nociceptor group. They were then treated with oxcarbazepine vs placebo. The initial hypothesis was that those patients with irritable nociceptor profile would be more responsive to oxcarbazepine (a drug that blocks VGSCs and reduces ectopic activity). This proved to be the case that there was a significant interaction between phenotype and treatment response with a lower number needed to treat (NNT) for oxcarbazepine in the irritable nociceptor (NNT = 3.9) vs nonirritable nociceptor (NNT = 11) group. Conditioned pain modulation is a means of assessing whether some patients may have insufficient endogenous pain modulation as a pathophysiological driver of NeuP. The mechanism of action of duloxetine is to restore such modulation, and indeed, those patients with defective CPM were found to be more responsive to duloxetine.110

Data on sensory symptoms are easier to collect than QST; however, it is not necessarily a surrogate28 and provides different information about the somatosensory nervous system. For instance, sensory symptoms are more informative about spontaneous pain than evoked pains and are not as effective at assessing sensory loss3; however, assessment of NeuP symptoms (for instance, NPSI) can reveal different responses between distinct drug classes.14

In summary, both genomics and sensory profiling show some promise in predicting treatment efficacy. A stratified approach is not used routinely to inform clinical decision-making; however, if found to be informative in large-scale clinical trials of common acquired NeuP states, this is likely to facilitate clinical adoption. A schematic showing the continuum of improved targeting of pain therapies is shown in Figure 4.

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4. Summary and future directions

We have an impressive array of techniques to identify different patient subgroups of patients with NeuP ranging from the relatively simple such as pain symptoms to highly complex genomics. In both cases, we have seen examples of stratified medicine being used in clinical pain practice whether screening for patients with NeuP or identifying those patients with very rare monogenic pain disorders likely to respond to a particular drug. These opportunities are likely to grow, especially with standardized sensory phenotyping, the use of electronic health records, and the increased adoption of large-scale genome sequencing. One challenge will be to understand the relationship between these different stratification methods. For instance, if a patient was found to have a gain of function mutation in a VGSC, would this take precedence over a sensory profile that showed deafferentation, which we would normally predict would reduce the likelihood of response? Data storage and integration within the health service remains a challenge certainly at national scale, which would provide the greatest traction for stratification. Large-scale genomics requires data security and also robust procedures for dealing with incidental findings that may not be relevant to pain but could be highly relevant to the health of the patient and their family. Stratified pain medicine has important implications for the drug development, and these techniques are increasingly being adopted in clinical trials. Although restricting a therapy to a subgroup of patients may initially seem an economic disincentive to pharma companies, the advantage is that this may make the difference between trial success and failure; certainly, the era of targeted biologics in cancer therapy has set a positive precedent for better patient stratification. We hope that Thomas Lewis would be impressed by progress over the past 70 years and in particular that we will be taking some of the “guessing” out of diagnosis; our aspiration is that the end point will have a more tangible link both to the pathophysiological mechanisms driving pain but also be predictive of patient prognosis and treatment response.

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Conflict of interest statement

D.L. Bennett has undertaken consultancy and advisory board work for Oxford innovation—in the past 36 months, this has included work for Abide, Biogen, GSK, Lilly, Mitsubishi Tanabe, Mundipharma, Teva, and Pfizer.

Figure 1 was made using items from the Servier Medical Art Powerpoint Image Bank under a creative commons attribution license. D.L. Bennett, G. Crombez, and A.C. Themistocleous are members of the DOLORisk consortium funded by the European Commission Horizon 2020 (ID633491). D.L. Bennett and A.C. Themistocleous are members of the International Diabetic Neuropathy Consortium, the Novo Nordisk Foundation (Ref. NNF14SA0006). D.L. Bennett is a senior Wellcome clinical scientist (Ref. 202747/Z/16/Z). A.C. Themistocleous is an Honorary Research Fellow of the Brain Function Research Group, School of Physiology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

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References

[1]. Attal N, Bouhassira D, Baron R. Diagnosis and assessment of neuropathic pain through questionnaires. Lancet Neurol 2018;17:456–66.
[2]. Attal N, de Andrade DC, Adam F, Ranoux D, Teixeira MJ, Galhardoni R, Raicher I, Uceyler N, Sommer C, Bouhassira D. Safety and efficacy of repeated injections of botulinum toxin A in peripheral neuropathic pain (BOTNEP): a randomised, double-blind, placebo-controlled trial. Lancet Neurol 2016;15:555–65.
[3]. Baron R, Forster M, Binder A. Subgrouping of patients with neuropathic pain according to pain-related sensory abnormalities: a first step to a stratified treatment approach. Lancet Neurol 2012;11:999–1005.
[4]. Baron R, Maier C, Attal N, Binder A, Bouhassira D, Cruccu G, Finnerup NB, Haanpää M, Hansson P, Hüllemann P, Jensen TS, Freynhagen R, Kennedy JD, Magerl W, Mainka T, Reimer M, Rice ASC, Segerdahl M, Serra J, Sindrup S, Sommer C, Tölle T, Vollert J, Treede RD. Peripheral neuropathic pain: a mechanism-related organizing principle based on sensory profiles. PAIN 2017;158:261–72.
[5]. Baron R, Tölle TR, Gockel U, Brosz M, Freynhagen R. A cross-sectional cohort survey in 2100 patients with painful diabetic neuropathy and postherpetic neuralgia: differences in demographic data and sensory symptoms. PAIN 2009;146:34–40.
[6]. Bennett DL, Woods CG. Painful and painless channelopathies. Lancet Neurol 2014;13:587–99.
[7]. Bennett M. The LANSS Pain Scale: the Leeds assessment of neuropathic symptoms and signs. PAIN 2001;92:147–57.
[8]. Bennett MI, Smith BH, Torrance N, Potter J. The S-LANSS score for identifying pain of predominantly neuropathic origin: validation for use in clinical and postal research. J Pain 2005;6:149–58.
[9]. Binder A, May D, Baron R, Maier C, Tolle TR, Treede RD, Berthele A, Faltraco F, Flor H, Gierthmuhlen J, Haenisch S, Huge V, Magerl W, Maihofner C, Richter H, Rolke R, Scherens A, Uceyler N, Ufer M, Wasner G, Zhu J, Cascorbi I. Transient receptor potential channel polymorphisms are associated with the somatosensory function in neuropathic pain patients. PLoS One 2011;6:e17387.
[10]. Blesneac I, Themistocleous AC, Fratter C, Conrad LJ, Ramirez JD, Cox JJ, Tesfaye S, Shillo PR, Rice ASC, Tucker SJ, Bennett DLH. Rare Nav1.7 variants associated with painful diabetic peripheral neuropathy. PAIN 2018;159:469–80.
[11]. Bouhassira D, Attal N, Alchaar H, Boureau F, Brochet B, Bruxelle J, Cunin G, Fermanian J, Ginies P, Grun-Overdyking A, Jafari-Schluep H, Lantéri-Minet M, Laurent B, Mick G, Serrie A, Valade D, Vicaut E. Comparison of pain syndromes associated with nervous or somatic lesions and development of a new neuropathic pain diagnostic questionnaire (DN4). PAIN 2005;114:29–36.
[12]. Bouhassira D, Attal N, Fermanian J, Alchaar H, Gautron M, Masquelier E, Rostaing S, Lanteri-Minet M, Collin E, Grisart J, Boureau F. Development and validation of the Neuropathic Pain Symptom Inventory. PAIN 2004;108:248–57.
[13]. Bouhassira D, Lanteri-Minet M, Attal N, Laurent B, Touboul C. Prevalence of chronic pain with neuropathic characteristics in the general population. PAIN 2008;136:380–7.
[14]. Bouhassira D, Wilhelm S, Schacht A, Perrot S, Kosek E, Cruccu G, Freynhagen R, Tesfaye S, Lledo A, Choy E, Marchettini P, Mico JA, Spaeth M, Skljarevski V, Tolle T. Neuropathic pain phenotyping as a predictor of treatment response in painful diabetic neuropathy: data from the randomized, double-blind, COMBO-DN study. PAIN 2014;155:2171–9.
[15]. Cao L, McDonnell A, Nitzsche A, Alexandrou A, Saintot PP, Loucif AJ, Brown AR, Young G, Mis M, Randall A, Waxman SG, Stanley P, Kirby S, Tarabar S, Gutteridge A, Butt R, McKernan RM, Whiting P, Ali Z, Bilsland J, Stevens EB. Pharmacological reversal of a pain phenotype in iPSC-derived sensory neurons and patients with inherited erythromelalgia. Sci Transl Med 2016;8:335ra356.
[16]. Chambers SM, Qi Y, Mica Y, Lee G, Zhang XJ, Niu L, Bilsland J, Cao L, Stevens E, Whiting P, Shi SH, Studer L. Combined small-molecule inhibition accelerates developmental timing and converts human pluripotent stem cells into nociceptors. Nat Biotechnol 2012;30:715–20.
[17]. Colloca L, Ludman T, Bouhassira D, Baron R, Dickenson AH, Yarnitsky D, Freeman R, Truini A, Attal N, Finnerup NB, Eccleston C, Kalso E, Bennett DL, Dworkin RH, Raja SN. Neuropathic pain. Nat Rev Dis Primers 2017;3:17002.
[18]. Costa FF. Big data in biomedicine. Drug Discov Today 2014;19:433–40.
[19]. Cox JJ, Reimann F, Nicholas AK, Thornton G, Roberts E, Springell K, Karbani G, Jafri H, Mannan J, Raashid Y, Al-Gazali L, Hamamy H, Valente EM, Gorman S, Williams R, McHale DP, Wood JN, Gribble FM, Woods CG. An SCN9A channelopathy causes congenital inability to experience pain. Nature 2006;444:894–8.
[20]. Cregg R, Cox JJ, Bennett DL, Wood JN, Werdehausen R. Mexiletine as a treatment for primary erythromelalgia: normalization of biophysical properties of mutant L858F NaV 1.7 sodium channels. Br J Pharmacol 2014;171:4455–63.
[21]. Cregg R, Laguda B, Werdehausen R, Cox JJ, Linley JE, Ramirez JD, Bodi I, Markiewicz M, Howell KJ, Chen YC, Agnew K, Houlden H, Lunn MP, Bennett DL, Wood JN, Kinali M. Novel mutations mapping to the fourth sodium channel domain of Nav1.7 result in variable clinical manifestations of primary erythromelalgia. Neuromolecular Med 2013;15:265–78.
[22]. Crombez G, Eccleston C, Van Damme S, Vlaeyen JW, Karoly P. Fear-avoidance model of chronic pain: the next generation. Clin J Pain 2012;28:475–83.
[23]. Cruccu G, Aminoff MJ, Curio G, Guerit JM, Kakigi R, Mauguiere F, Rossini PM, Treede RD, Garcia-Larrea L. Recommendations for the clinical use of somatosensory-evoked potentials. Clin Neurophysiol 2008;119:1705–19.
[24]. Cruccu G, Anand P, Attal N, Garcia-Larrea L, Haanpää M, Jørum E, Serra J, Jensen TS. EFNS guidelines on neuropathic pain assessment. Eur J Neurol 2004;11:153–62.
[25]. Cummins TR, Dib-Hajj SD, Waxman SG. Electrophysiological properties of mutant Nav1.7 sodium channels in a painful inherited neuropathy. J Neurosci 2004;24:8232–6.
[26]. Daniel HC, Narewska J, Serpell M, Hoggart B, Johnson R, Rice AS. Comparison of psychological and physical function in neuropathic pain and nociceptive pain: implications for cognitive behavioral pain management programs. Eur J Pain 2008;12:731–41.
[27]. de Greef BTA, Geerts M, Faber CG, Merkies ISJ, Hoeijmakers JGJ. A randomized controlled trial of the efficacy, safety, and tolerability of Lacosamide in patients with gain-of-function Nav1.7 mutation-related small fiber neuropathy: the LENSS study. J Peripher Nerv Syst 2017;22:268.
[28]. Demant DT, Lund K, Vollert J, Maier C, Segerdahl M, Finnerup NB, Jensen TS, Sindrup SH. The effect of oxcarbazepine in peripheral neuropathic pain depends on pain phenotype: a randomised, double-blind, placebo-controlled phenotype-stratified study. PAIN 2014;155:2263–73.
[29]. Dib-Hajj SD, Cummins TR, Black JA, Waxman SG. From genes to pain: Na v 1.7 and human pain disorders. Trends Neurosci 2007;30:555–63.
[30]. Donadio V, Liguori R. Microneurographic recording from unmyelinated nerve fibers in neurological disorders: an update. Clin Neurophysiol 2015;126:437–45.
[31]. Eccleston C, Crombez G. Pain demands attention: a cognitive-affective model of the interruptive function of pain. Psychol Bull 1999;125:356–66.
[32]. Eccleston C, Crombez G. Worry and chronic pain: a misdirected problem solving model. PAIN 2007;132:233–6.
[33]. Eccleston C, Crombez G. Advancing psychological therapies for chronic pain. F1000Res 2017;6:461.
[34]. Eccleston C, Hearn L, Williams AC. Psychological therapies for the management of chronic neuropathic pain in adults. Cochrane Database Syst Rev 2015;10:CD011259.
[35]. Faber CG, Hoeijmakers JG, Ahn HS, Cheng X, Han C, Choi JS, Estacion M, Lauria G, Vanhoutte EK, Gerrits MM, Dib-Hajj S, Drenth JP, Waxman SG, Merkies IS. Gain of function Nanu1.7 mutations in idiopathic small fiber neuropathy. Ann Neurol 2012;71:26–39.
[36]. Fertleman CR, Ferrie CD, Aicardi J, Bednarek NA, Eeg-Olofsson O, Elmslie FV, Griesemer DA, Goutieres F, Kirkpatrick M, Malmros IN, Pollitzer M, Rossiter M, Roulet-Perez E, Schubert R, Smith VV, Testard H, Wong V, Stephenson JB. Paroxysmal extreme pain disorder (previously familial rectal pain syndrome). Neurology 2007;69:586–95.
[37]. Finnerup NB, Attal N, Haroutounian S, McNicol E, Baron R, Dworkin RH, Gilron I, Haanpaa M, Hansson P, Jensen TS, Kamerman PR, Lund K, Moore A, Raja SN, Rice AS, Rowbotham M, Sena E, Siddall P, Smith BH, Wallace M. Pharmacotherapy for neuropathic pain in adults: a systematic review and meta-analysis. Lancet Neurol 2015;14:162–73.
[38]. Finnerup NB, Haroutounian S, Kamerman P, Baron R, Bennett DL, Bouhassira D, Cruccu G, Freeman R, Hansson P, Nurmikko T, Raja SN, Rice AS, Serra J, Smith BH, Treede RD, Jensen TS. Neuropathic pain: an updated grading system for research and clinical practice. PAIN 2016;157:1599–606.
[39]. Fischer TZ, Gilmore ES, Estacion M, Eastman E, Taylor S, Melanson M, Dib-Hajj SD, Waxman SG. A novel Nav1.7 mutation producing carbamazepine-responsive erythromelalgia. Ann Neurol 2009;65:733–41.
[40]. Freeman R, Baron R, Bouhassira D, Cabrera J, Emir B. Sensory profiles of patients with neuropathic pain based on the neuropathic pain symptoms and signs. PAIN 2014;155:367–76.
[41]. Freynhagen R, Baron R, Gockel U, Tölle TR. painDETECT: a new screening questionnaire to identify neuropathic components in patients with back pain. Curr Med Res Opin 2006;22:1911–20.
[42]. Geha P, Yang Y, Estacion M, Schulman BR, Tokuno H, Apkarian AV, Dib-Hajj SD, Waxman SG. Pharmacotherapy for pain in a family with inherited erythromelalgia guided by genomic analysis and functional profiling. JAMA Neurol 2016;73:659–67.
[43]. Gonzalez JS, Peyrot M, McCarl LA, Collins EM, Serpa L, Mimiaga MJ, Safren SA. Depression and diabetes treatment nonadherence: a meta-analysis. Diabetes Care 2008;31:2398–403.
[44]. Granovsky Y. Conditioned pain modulation: a predictor for development and treatment of neuropathic pain. Curr Pain Headache Rep 2013;17:361.
[45]. Gwilym SE, Keltner JR, Warnaby CE, Carr AJ, Chizh B, Chessell I, Tracey I. Psychophysical and functional imaging evidence supporting the presence of central sensitization in a cohort of osteoarthritis patients. Arthritis Care Res 2009;61:1226–34.
[46]. Haerian BS, Haerian MS. OPRM1 rs1799971 polymorphism and opioid dependence: evidence from a meta-analysis. Pharmacogenomics 2013;14:813–24.
[47]. Han C, Dib-Hajj SD, Lin Z, Li Y, Eastman EM, Tyrrell L, Cao X, Yang Y, Waxman SG. Early- and late-onset inherited erythromelalgia: genotype-phenotype correlation. Brain 2009;132:1711–22.
[48]. Han C, Themistocleous AC, Macala LJ, Blesneac I, Fratter C, Bennett DL, Dib-Hajj SD, Waxman SG. From Nav1.7 to Nav1.8: a case for precision medicine. Proc Soc Neurosci 2017;288.08:D29.
[49]. Hearn L, Derry S, Moore RA. Lacosamide for neuropathic pain and fibromyalgia in adults. Cochrane Database Syst Rev 2012;2:CD009318.
[50]. Hebert HL, Veluchamy A, Torrance N, Smith BH. Risk factors for neuropathic pain in diabetes mellitus. PAIN 2017;158:560–8.
[51]. Holbech JV, Bach FW, Finnerup NB, Jensen TS, Sindrup SH. Pain phenotype as a predictor for drug response in painful polyneuropathy-a retrospective analysis of data from controlled clinical trials. PAIN 2016;157:1305–13.
[52]. Jo S, Bean BP. Lacosamide inhibition of Nav1.7 voltage-gated sodium channels: slow binding to fast-inactivated states. Mol Pharmacol 2017;91:277–86.
[53]. Kennedy DL, Kemp HI, Ridout D, Yarnitsky D, Rice ASC. Reliability of conditioned pain modulation: a systematic review. PAIN 2016;157:2410–19.
[54]. Kiecolt-Glaser JK, McGuire L, Robles TF, Glaser R. Emotions, morbidity, and mortality: new perspectives from psychoneuroimmunology. Annu Rev Psychol 2002;53:83–107.
[55]. Kleggetveit IP, Namer B, Schmidt R, Helås T, Rückel M, Ørstavik K, Schmelz M, Jørum E. High spontaneous activity of C-nociceptors in painful polyneuropathy. PAIN 2012;153:2040–7.
[56]. Magerl W, Krumova EK, Baron R, Tölle T, Treede RD, Maier C. Reference data for quantitative sensory testing (QST): refined stratification for age and a novel method for statistical comparison of group data. PAIN 2010;151:598–605.
[57]. Maier C, Baron R, Tölle TR, Binder A, Birbaumer N, Birklein F, Gierthmühlen J, Flor H, Geber C, Huge V, Krumova EK, Landwehrmeyer GB, Magerl W, Maihöfner C, Richter H, Rolke R, Scherens A, Schwarz A, Sommer C, Tronnier V, Uçeyler N, Valet M, Wasner G, Treede RDD. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): somatosensory abnormalities in 1236 patients with different neuropathic pain syndromes. PAIN 2010;150:439–50.
[58]. McDonnell A, Schulman B, Ali Z, Dib-Hajj SD, Brock F, Cobain S, Mainka T, Vollert J, Tarabar S, Waxman SG. Inherited erythromelalgia due to mutations in SCN9A: natural history, clinical phenotype and somatosensory profile. Brain 2016;139:1052–65.
[59]. Mills EP, Di Pietro F, Alshelh Z, Peck CC, Murray GM, Vickers ER, Henderson LA. Brainstem pain-control circuitry connectivity in chronic neuropathic pain. J Neurosci 2018;38:465–73.
[60]. Moore A, Derry S, Eccleston C, Kalso E. Expect analgesic failure; pursue analgesic success. BMJ 2013;346:f2690.
[61]. Nahman-Averbuch H, Yarnitsky D, Granovsky Y, Sprecher E, Steiner M, Tzuk-Shina T, Pud D. Pronociceptive pain modulation in patients with painful chemotherapy-induced polyneuropathy. J Pain Symptom Manage 2011;42:229–38.
[62]. Namer B, Ørstavik K, Schmidt R, Kleggetveit IP, Weidner C, Mørk C, Kvernebo MS, Kvernebo K, Salter H, Carr TH, Segerdahl M, Quiding H, Waxman SG, Handwerker HO, Torebjörk HE, Jørum E, Schmelz M. Specific changes in conduction velocity recovery cycles of single nociceptors in a patient with erythromelalgia with the I848T gain-of-function mutation of Nav1.7. PAIN 2015;156:1637–46.
[63]. Ørstavik K, Namer B, Schmidt R, Schmelz M, Hilliges M, Weidner C, Carr RW, Handwerker H, Jørum E, Torebjörk HE. Abnormal function of C-Fibers in patients with diabetic neuropathy. J Neurosci 2006;26:11287–94.
[64]. Ørstavik K, Weidner C, Schmidt R, Schmelz M, Hilliges M, Jørum E, Handwerker H, Torebjörk E. Pathological C-fibres in patients with a chronic painful condition. Brain 2003;126:567–78.
[65]. Raputova J, Srotova I, Vlckova E, Sommer C, Üçeyler N, Birklein F, Rittner HL, Rebhorn C, Adamova B, Kovalova I, Kralickova Nekvapilova E, Forer L, Belobradkova J, Olsovsky J, Weber P, Dusek L, Jarkovsky J, Bednarik J. Sensory phenotype and risk factors for painful diabetic neuropathy: a cross-sectional observational study. PAIN 2017;158:2340–53.
[66]. Redekop WK, Mladsi D. The faces of personalized medicine: a framework for understanding its meaning and scope. Value Health 2013;16:S4–9.
[67]. Rolke R, Baron R, Maier C, Tölle TR, Treede RDD, Beyer A, Binder A, Birbaumer N, Birklein F, Bötefür IC, Braune S, Flor H, Huge V, Klug R, Landwehrmeyer GB, Magerl W, Maihöfner C, Rolko C, Schaub C, Scherens A, Sprenger T, Valet M, Wasserka B. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): standardized protocol and reference values. PAIN 2006;123:231–43.
[68]. Rolke R, Magerl W, Campbell KA, Schalber C, Caspari S, Birklein F, Treede RD. Quantitative sensory testing: a comprehensive protocol for clinical trials. Eur J Pain 2006;10:77–88.
[69]. Sandhu C, Qureshi A, Emili A. Panomics for precision medicine. Trends Mol Med 2018;24:85–101.
[70]. Segerdahl AR, Themistocleous AC, Fido D, Bennett DL, Tracey I. A brain-based pain facilitation mechanism contributes to painful diabetic polyneuropathy. Brain 2018;141:357–64.
[71]. Serra J, Bostock H, Solà R, Aleu J, García E, Cokic B, Navarro X, Quiles C. Microneurographic identification of spontaneous activity in C-nociceptors in neuropathic pain states in humans and rats. PAIN 2012;153:42–55.
[72]. Serra J, Collado A, Solà R, Antonelli F, Torres X, Salgueiro M, Quiles C, Bostock H. Hyperexcitable C nociceptors in fibromyalgia. Ann Neurol 2014;75:196–208.
[73]. Shen H, Zhou Q, Pan X, Li Z, Wu J, Yan N. Structure of a eukaryotic voltage-gated sodium channel at near-atomic resolution. Science 2017;355:eaal4326.
[74]. Sieberg CB, Taras C, Gomaa A, Nickerson C, Wong C, Ward C, Baskozos G, Tesfaye S, Rice ASC, Bennett DLH, Ramirez JD, Shillo PR, Themistocleous AC, Edwards RR, Andrews N, Berde C, Costigan M. Neuropathic pain drives anxiety behavior in mice and diabetic neuropathy patients. PAIN Rep 2018;3:e651.
[75]. Simpson DM, Schifitto G, Clifford DB, Murphy TK, Durso-De Cruz E, Glue P, Whalen E, Emir B, Scott GN, Freeman R; Group HIVNS. Pregabalin for painful HIV neuropathy: a randomized, double-blind, placebo-controlled trial. Neurology 2010;74:413–20.
[76]. Siva N. UK gears up to decode 100,000 genomes from NHS patients. Lancet 2015;385:103–4.
[77]. Smith BH, Torrance N. Epidemiology of neuropathic pain and its impact on quality of life. Curr Pain Headache Rep 2012;16:191–8.
[78]. Sommer C, Richter H, Rogausch JP, Frettlöh J, Lungenhausen M, Maier C. A modified score to identify and discriminate neuropathic pain: a study on the German version of the Neuropathic Pain Symptom Inventory (NPSI). BMC Neurol 2011;11:104.
[79]. Spallone V, Morganti R, D'Amato C, Greco C, Cacciotti L, Marfia GA. Validation of DN4 as a screening tool for neuropathic pain in painful diabetic polyneuropathy. Diabet Med 2012;29:578–85.
[80]. Tesfaye S, Boulton AJ, Dickenson AH. Mechanisms and management of diabetic painful distal symmetrical polyneuropathy. Diabetes Care 2013;36:2456–65.
[81]. Tesfaye S, Boulton AJ, Dyck PJ, Freeman R, Horowitz M, Kempler P, Lauria G, Malik RA, Spallone V, Vinik A, Bernardi L, Valensi P. Diabetic neuropathies: update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes Care 2010;33:2285–93.
[82]. Themistocleous AC, Ramirez JD, Serra J, Bennett DL. The clinical approach to small fibre neuropathy and painful channelopathy. Pract Neurol 2014;14:368–79.
[83]. Themistocleous AC, Ramirez JD, Shillo PR, Lees JG, Selvarajah D, Orengo C, Tesfaye S, Rice ASC, Bennett DLH. The Pain in Neuropathy Study (PiNS): a cross-sectional observational study determining the somatosensory phenotype of painful and painless diabetic neuropathy. PAIN 2016;157:1132–45.
[84]. Torrance N, Smith BH, Bennett MI, Lee AJ. The epidemiology of chronic pain of predominantly neuropathic origin. Results from a general population survey. J Pain 2006;7:281–9.
[85]. Tracey I. Neuroimaging mechanisms in pain: from discovery to translation. PAIN 2017;158:S115–22.
[86]. Treede RD, Jensen TS, Campbell JN, Cruccu G, Dostrovsky JO, Griffin JW, Hansson P, Hughes R, Nurmikko T, Serra J. Neuropathic pain: redefinition and a grading system for clinical and research purposes. Neurology 2008;70:1630–5.
[87]. Truini A, Galeotti F, Haanpaa M, Zucchi R, Albanesi A, Biasiotta A, Gatti A, Cruccu G. Pathophysiology of pain in postherpetic neuralgia: a clinical and neurophysiological study. PAIN 2008;140:405–10.
[88]. Truini A, Garcia-Larrea L, Cruccu G. Reappraising neuropathic pain in humans—how symptoms help disclose mechanisms. Nat Rev Neurol 2013;9:572.
[89]. Truini A, Padua L, Biasiotta A, Caliandro P, Pazzaglia C, Galeotti F, Inghilleri M, Cruccu G. Differential involvement of A-delta and A-beta fibres in neuropathic pain related to carpal tunnel syndrome. PAIN 2009;145:105–9.
[90]. Trusheim MR, Berndt ER, Douglas FL. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat Rev Drug Discov 2007;6:287–93.
[91]. Tuveson B, Leffler AS, Hansson P. Heterotopic noxious conditioning stimulation (HNCS) reduced the intensity of spontaneous pain, but not of allodynia in painful peripheral neuropathy. Eur J Pain 2007;11:452–62.
[92]. Üçeyler N, Zeller D, Kahn AK, Kewenig S, Kittel-Schneider S, Schmid A, Casanova-Molla J, Reiners K, Sommer C. Small fibre pathology in patients with fibromyalgia syndrome. Brain 2013;136:1857–67.
[93]. Vale TA, Symmonds M, Polydefkis M, Byrnes K, Rice ASC, Themistocleous AC, Bennett DLH. Chronic non-freezing cold injury results in neuropathic pain due to a sensory neuropathy. Brain 2017;140:2557–69.
[94]. Vedhara K, Dawe K, Wetherell MA, Miles JN, Cullum N, Dayan C, Drake N, Price P, Tarlton J, Weinman J, Day A, Campbell R. Illness beliefs predict self-care behaviours in patients with diabetic foot ulcers: a prospective study. Diabetes Res Clin Pract 2014;106:67–72.
[95]. Vlaeyen JW, Linton SJ. Fear-avoidance and its consequences in chronic musculoskeletal pain: a state of the art. PAIN 2000;85:317–32.
[96]. Vollert J, Attal N, Baron R, Freynhagen R, Haanpää M, Hansson P, Jensen TS, Rice ASC, Segerdahl M, Serra J, Sindrup SH, Tölle TR, Treede RD, Maier C. Quantitative sensory testing using DFNS protocol in Europe: an evaluation of heterogeneity across multiple centers in patients with peripheral neuropathic pain and healthy subjects. PAIN 2016;157:750–8.
[97]. Vollert J, Kramer M, Barroso A, Freynhagen R, Haanpää M, Hansson P, Jensen TS, Kuehler BM, Maier C, Mainka T, Reimer M, Segerdahl M, Serra J, Solà R, Tölle TR, Treede RD, Baron R. Symptom profiles in the painDETECT Questionnaire in patients with peripheral neuropathic pain stratified according to sensory loss in quantitative sensory testing. PAIN 2016;157:1810–18.
[98]. Vollert J, Maier C, Attal N, Bennett DLH, Bouhassira D, Enax-Krumova EK, Finnerup NB, Freynhagen R, Gierthmühlen J, Haanpää M, Hansson P, Hüllemann P, Jensen TS, Magerl W, Ramirez JD, Rice ASC, Schuh-Hofer S, Segerdahl M, Serra J, Shillo PR, Sindrup S, Tesfaye S, Themistocleous AC, Tölle TR, Treede RD, Baron R. Stratifying patients with peripheral neuropathic pain based on sensory profiles: algorithm and sample size recommendations. PAIN 2017;158:1446–55.
[99]. von Bischhoffshausen S, Ivulic D, Alvarez P, Schuffeneger VC, Idiaquez J, Fuentes C, Morande P, Fuentes I, Palisson F, Bennett DLH, Calvo M. Recessive dystrophic epidermolysis bullosa results in painful small fibre neuropathy. Brain 2017;140:1238–51.
[100]. Wanigasekera V, Mezue M, Andersson J, Kong Y, Tracey I. Disambiguating pharmacodynamic efficacy from behaviour with neuroimaging: implications for analgesic drug development. Anesthesiology 2016;124:159–68.
[101]. Waxman SG, Merkies IS, Gerrits MM, Dib-Hajj SD, Lauria G, Cox JJ, Wood JN, Woods CG, Drenth JP, Faber CG. Sodium channel genes in pain-related disorders: phenotype-genotype associations and recommendations for clinical use. Lancet Neurol 2014;13:1152–60.
[102]. Weir GA, Middleton SJ, Clark AJ, Daniel T, Khovanov N, McMahon SB, Bennett DL. Using an engineered glutamate-gated chloride channel to silence sensory neurons and treat neuropathic pain at the source. Brain 2017;140:2570–85.
[103]. Wilder-Smith OH, Schreyer T, Scheffer GJ, Arendt-Nielsen L. Patients with chronic pain after abdominal surgery show less preoperative endogenous pain inhibition and more postoperative hyperalgesia: a pilot study. J Pain Palliat Care Pharmacother 2010;24:119–28.
[104]. Woolf CJ, Bennett GJ, Doherty M, Dubner R, Kidd B, Koltzenburg M, Lipton R, Loeser JD, Payne R, Torebjork E. Towards a mechanism-based classification of pain? PAIN 1998;77:227–9.
[105]. Woolf CJ, Decosterd I. Implications of recent advances in the understanding of pain pathophysiology for the assessment of pain in patients. PAIN 1999;82(suppl 6):S141–147.
[106]. Yang Y, Adi T, Effraim PR, Chen L, Dib-Hajj SD, Waxman SG. Reverse pharmacogenomics: carbamazepine normalizes activation and attenuates thermal hyperexcitability of sensory neurons due to Nav 1.7 mutation I234T. Br J Pharmacol 2018;175:2261–71.
[107]. Yang Y, Dib-Hajj SD, Zhang J, Zhang Y, Tyrrell L, Estacion M, Waxman SG. Structural modelling and mutant cycle analysis predict pharmacoresponsiveness of a Na(V)1.7 mutant channel. Nat Commun 2012;3:1186.
[108]. Yang Y, Wang Y, Li S, Xu Z, Li H, Ma L, Fan J, Bu D, Liu B, Fan Z, Wu G, Jin J, Ding B, Zhu X, Shen Y. Mutations in SCN9A, encoding a sodium channel alpha subunit, in patients with primary erythermalgia. J Med Genet 2004;41:171–4.
[109]. Yarnitsky D, Crispel Y, Eisenberg E, Granovsky Y, Ben-Nun A, Sprecher E, Best LA, Granot M. Prediction of chronic post-operative pain: pre-operative DNIC testing identifies patients at risk. PAIN 2008;138:22–8.
[110]. Yarnitsky D, Granot M, Nahman-Averbuch H, Khamaisi M, Granovsky Y. Conditioned pain modulation predicts duloxetine efficacy in painful diabetic neuropathy. PAIN 2012;153:1193–8.
[111]. Zakrzewska JM, Linskey ME. Trigeminal neuralgia. BMJ Clin Evid 2014;1207.
[112]. Zakrzewska JM, Palmer J, Morisset V, Giblin GM, Obermann M, Ettlin DA, Cruccu G, Bendtsen L, Estacion M, Derjean D, Waxman SG, Layton G, Gunn K, Tate S; study investigators. Safety and efficacy of a Nav1.7 selective sodium channel blocker in patients with trigeminal neuralgia: a double-blind, placebo-controlled, randomised withdrawal phase 2a trial. Lancet Neurol 2017;16:291–300.
[113]. Zorina-Lichtenwalter K, Parisien M, Diatchenko L. Genetic studies of human neuropathic pain conditions: a review. PAIN 2018;159:583–94.
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