Skip to content

Sensory phenotyping and quantitative sensory testing

TL;DR — Bedside examination and QST describe thermal and mechanical loss and gain; they do not identify a molecular mechanism. The DFNS protocol measures 13 parameters in about 30 minutes and normalizes by body site, age and sex (Rolke 2006, PMID 16697110). Phenotypes change after disease-modifying treatment (Kennedy 2021, PMID 33769367). A targeted PubMed and ClinicalTrials.gov search on 2026-08-30 found no replicated phenotype-to-drug rule; SPENDD is prospectively testing that unresolved strategy (NCT06614322).

Purpose

Etiologic labels hide heterogeneity. Phenotyping creates reproducible profiles for trials while lesion grading remains separate (Haanpää 2011, PMID 20851519).

A complete phenotype is multidimensional: pain location, spontaneous and evoked qualities, intensity/time course, negative and positive sensory signs, affect, sleep and function. QST addresses only stimulus–response functions within this wider assessment; it neither measures spontaneous pain nor replaces lesion localization (Fillingim 2016, PMID 27586827).

DFNS domains

The protocol measures cold/warm detection, thermal pain, paradoxical heat, mechanical detection/pain, allodynia, pressure pain and wind-up (Rolke 2006, PMID 16697110). Original norms came from 180 healthy participants; side-to-side comparison increased sensitivity 1.1–2.5-fold (Rolke 2006, PMID 16697110).

The companion feasibility protocol tested 18 healthy participants at face, hand and foot. A full region required 27±2.3 minutes; left–right correlations ranged from 0.78 to 0.97, explaining 61–94% of variance, but several parameters required logarithmic transformation and thresholds differed by region (Rolke 2006, PMID 16291301). High correlation in healthy participants is not equivalent to test–retest reliability in painful, medication-exposed clinical populations.

DFNS parameter family Principal afferent/pathway emphasis Unit/output Major confounder
Cold/warm detection Small-fiber detection Temperature threshold Baseline skin temperature, age
Cold/heat pain Nociceptive gain/loss Temperature threshold Safety ceiling/floor, expectation
Mechanical detection Aβ-mediated touch Force threshold Callus, site, examiner technique
Mechanical pain sensitivity Pinprick gain/loss Rating function Anchoring and local tissue state
Dynamic mechanical allodynia Low-threshold input evoking pain Rating/frequency Brush force and speed
Pressure pain Deep-tissue sensitivity Pressure threshold Muscle bulk and device geometry
Wind-up ratio Repetition-related facilitation Repeated/single rating ratio First-stimulus instability

Interpretation

QST is psychophysical: calibration, instructions, attention, skin temperature, medication and reference strata matter (Rolke 2006, PMID 16291301). An abnormal z-score neither localizes nor proves a lesion (Krumova 2012, PMID 22623149).

Earlier methodological guidance therefore recommended QST for research and structured sensory description while warning against using it as a stand-alone diagnostic test (Hansson 2007, PMID 17451879). The key distinction is between analytical validity (the device delivers/measures the intended stimulus), reference validity (the comparator distribution fits age, sex and body site), and clinical validity (the result identifies a clinically meaningful lesion or predicts outcome). A protocol can satisfy the first two without satisfying the third.

Bedside versus QST

Bedside mapping establishes neuroanatomy quickly; QST quantifies stimulus-response functions. Both need history and confirmatory testing (Backonja 2009, PMID 19692807).

Cluster concepts

Sensory-loss, thermal-hyperalgesia and mechanical-hyperalgesia patterns recur, but cluster membership depends on variables and algorithm (Reimer 2014, PMID 24670811). Genetic associations with sensory profile are emerging in small deeply phenotyped cohorts (Åkerlund 2025, PMID 39471050).

Extreme-phenotype sequencing illustrates enrichment and ascertainment simultaneously. In 205 UK participants selected for pronounced sensory gain or loss, 12% carried medically actionable variants; clinically relevant variants were most frequent in voltage-gated sodium-channel genes (Themistocleous 2023, PMID 36895957). This supports adding genotype to phenotype in selected clinics, but the 12% yield cannot be generalized to ordinary neuropathic-pain cohorts.

What a cluster would need to become prescriptive

Validation step Required evidence Current gap
Derivation stability Similar clusters across resampling and plausible preprocessing Algorithms and input panels vary
Temporal stability Repeat classification before treatment Disease and medication can shift thresholds
External transportability Replication across center, language, age and etiology Norms and recruitment differ
Predictive validity Randomized treatment-by-cluster interaction Most signals are post hoc
Incremental utility Better outcomes than etiology/examination alone Rarely tested
Feasibility Abbreviated protocol with preserved information Speed–precision trade-off unresolved

Prediction standard

A valid precision trial must lock the phenotype, randomize within strata, test treatment-by-phenotype interaction, control multiplicity and replicate externally (Edwards 2023, PMID 36198371). Retrospective subgroup signals are hypothesis-generating (Reimer 2014, PMID 24670811).

State dependence

After carpal-tunnel surgery, 92% of 76 participants reported a good outcome and thermal/mechanical QST and cluster membership changed (Kennedy 2021, PMID 33769367). This makes phenotype useful longitudinally but not immutable.

State dependence is not merely nuisance. If a lesion-directed intervention changes a prespecified sensory measure in parallel with clinical recovery, that measure may be a response biomarker even when it is unsuitable as a diagnostic marker. Conversely, regression to the mean, unblinded expectation and repeated-testing familiarity can mimic biological normalization; longitudinal studies need untreated or sham comparators where feasible.

Other probes

Conditioned pain modulation and temporal summation estimate inhibition and facilitation; capsaicin/UV-B models create controlled hypersensitivity but are not chronic lesions (Arendt-Nielsen 2018, PMID 29105941); (Lötsch 2018, PMID 28700537).

Small-fiber workups often combine QST with structural and autonomic measures. Historical reviews reported sensitivities of 59–88% across epidermal nerve-fiber density, sudomotor, quantitative sensory and cardiovagal tests, emphasizing complementarity rather than equivalence (Lacomis 2002, PMID 12210380). Contemporary reviews retain skin biopsy and QST within Besta-style criteria while distinguishing clinical diagnosis from stricter trial criteria (Kool 2024, PMID 39580213). Neither framework converts a thermal threshold into proof that the measured abnormality causes pain.

Measurement table

Domain Loss Gain
Thermal detection Elevated threshold Rare detection gain
Thermal pain Hypoalgesia Hyperalgesia
Mechanical detection Touch/vibration loss Hyperesthesia
Mechanical pain Pinprick loss Hyperalgesia
Dynamic touch Reduced touch Brush allodynia
Repetition Reduced response Enhanced wind-up

Evidence interpretation map

The table makes the evidence role and inferential boundary explicit; it is not a replacement for the full reports.

PMID Year Evidence role What it cannot establish alone
16291301 2006 Protocol feasibility and within-person side correlation Clinical diagnostic accuracy
17451879 2007 Methodological limits of QST A lesion, etiology or molecular mechanism
36895957 2023 Genotype yield in an extreme-phenotype cohort Population-wide genetic yield
12210380 2002 Complementary small-fiber test synthesis Modern pooled accuracy under one reference standard
39580213 2024 Contemporary small-fiber diagnostic framework That an abnormal threshold generates pain

Minimum reporting controls

Domain Required report
Case definition Possible, probable or definite neuropathic pain
Etiology Lesion/disease and diagnostic evidence
Distribution Focal, length-dependent, dermatomal, at-level or below-level
Baseline phenotype Negative and positive sensory signs
Comparator Placebo/sham, active care or natural history
Exposure Dose, duration, adherence and co-interventions
Benefit Mean change plus ≥30% and ≥50% responders where applicable
Function Sleep, mobility, participation and patient global change
Harm Adverse events, withdrawals and serious events
Durability Follow-up after treatment and attrition
Subgroups Prespecified interaction test, not within-group significance
Missingness Denominator and imputation method

Reporting cautions

  • Do not infer lesion presence from a symptom descriptor.
  • Do not convert a group-average association into an individual diagnostic rule.
  • Do not treat statistical significance as clinically important benefit.
  • Do not compare NNTs without checking outcome threshold, duration and population.
  • Do not interpret an inactive or completed registry record as proof of efficacy.
  • Do not merge painful and painless neuropathy outcomes.
  • Do not omit adverse-event withdrawals from responder interpretation.
  • Do not call a post hoc subgroup predictive without an interaction test.
  • Do not generalize a focal peripheral result to central neuropathic pain.
  • State when evidence is short-term, indirect or restricted to a selected cohort.

Open questions

  • OQ-1: Can prespecified QST strata improve absolute response?
  • OQ-2: How stable are clusters across days and medication changes?
  • OQ-3: Which abbreviated protocol retains clinical value?
  • OQ-4: Do genotype, QST and microneurography converge?

References

  1. Rolke R, Baron R, Maier C, et al.. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): standardized protocol and reference values. Pain. 2006;123:231-243. PMID 16697110
  2. Rolke R, Magerl W, Campbell KA, et al.. Quantitative sensory testing: a comprehensive protocol for clinical trials. Eur J Pain. 2006;10:77-88. PMID 16291301
  3. Krumova EK, Geber C, Westermann A, et al.. Neuropathic pain: is quantitative sensory testing helpful? Curr Diab Rep. 2012;12:393-402. PMID 22623149
  4. Backonja MM, Walk D, Edwards RR, et al.. Quantitative sensory testing in measurement of neuropathic pain phenomena and other sensory abnormalities. Clin J Pain. 2009;25:641-7. PMID 19692807
  5. Reimer M, Helfert SM, Baron R. Phenotyping neuropathic pain patients: implications for individual therapy and clinical trials. Curr Opin Support Palliat Care. 2014;8:124-9. PMID 24670811
  6. Kennedy DL, Vollert J, Ridout D, et al.. Responsiveness of quantitative sensory testing-derived sensory phenotype to disease-modifying intervention in patients with entrapment neuropathy: a longitudinal study. Pain. 2021;162:2881-2893. PMID 33769367
  7. Haanpää M, Attal N, Backonja M, et al.. NeuPSIG guidelines on neuropathic pain assessment. Pain. 2011;152:14-27. PMID 20851519
  8. Åkerlund M, Baskozos G, Li W, et al.. Genetic associations of neuropathic pain and sensory profile in a deeply phenotyped neuropathy cohort. Pain. 2025;166:1354-1368. PMID 39471050
  9. Edwards RR, Schreiber KL, Dworkin RH, et al.. Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations. J Pain. 2023;24:204-225. PMID 36198371
  10. Arendt-Nielsen L, Morlion B, Perrot S, et al.. Assessment and manifestation of central sensitisation across different chronic pain conditions. Eur J Pain. 2018;22:216-241. PMID 29105941
  11. Lötsch J, Geisslinger G, Heinemann S, et al.. Quantitative sensory testing response patterns to capsaicin- and ultraviolet-B-induced local skin hypersensitization in healthy subjects: a machine-learned analysis. Pain. 2018;159:11-24. PMID 28700537
  12. Truini A, Aleksovska K, Anderson CC, et al.. Joint European Academy of Neurology-European Pain Federation-Neuropathic Pain Special Interest Group of the International Association for the Study of Pain guidelines on neuropathic pain assessment. Eur J Neurol. 2023;30:2177-2196. PMID 37253688
  13. Pfau DB, Geber C, Birklein F, et al.. Quantitative sensory testing of neuropathic pain patients: potential mechanistic and therapeutic implications. Curr Pain Headache Rep. 2012;16:199-206. PMID 22535540
  14. Adler M, Taxer B. [Quantitative sensory testing for neuropathic pain and its relevance for physiotherapy]. Schmerz. 2022;36:437-446. PMID 34424391
  15. Sachau J, Bruckmueller H, Gierthmühlen J, et al.. SIGMA-1 Receptor Gene Variants Affect the Somatosensory Phenotype in Neuropathic Pain Patients. J Pain. 2019;20:201-214. PMID 30266269
  16. Binder A, Baron R. Mechanism-based therapy for neuropathic pain-a concept in danger? Pain. 2015;156:2113-2114. PMID 26307854
  17. Bannister K, Sachau J, Baron R, et al.. Neuropathic Pain: Mechanism-Based Therapeutics. Annu Rev Pharmacol Toxicol. 2020;60:257-274. PMID 31914896
  18. Colloca L, Ludman T, Bouhassira D, et al.. Neuropathic pain. Nat Rev Dis Primers. 2017;3:17002. PMID 28205574
  19. Finnerup NB, Haroutounian S, Kamerman P, et al.. Neuropathic pain: an updated grading system for research and clinical practice. Pain. 2016;157:1599-1606. PMID 27115670
  20. Lauria G, Hsieh ST, Johansson O, et al.. European Federation of Neurological Societies/Peripheral Nerve Society Guideline on the use of skin biopsy in the diagnosis of small fiber neuropathy. Report of a joint task force of the European Federation of Neurological Societies and the Peripheral Nerve Society. Eur J Neurol. 2010;17:903-12, e44-9. PMID 20642627
  21. Haroutounian S, Todorovic MS, Leinders M, et al.. Diagnostic criteria for idiopathic small fiber neuropathy: A systematic review. Muscle Nerve. 2021;63:170-177. PMID 32989823
  22. Strand N, Wie C, Peck J, et al.. Small Fiber Neuropathy. Curr Pain Headache Rep. 2022;26:429-438. PMID 35384587
  23. Campbell CM, Jamison RN, Edwards RR. Psychological screening/phenotyping as predictors for spinal cord stimulation. Curr Pain Headache Rep. 2013;17:307. PMID 23247806
  24. Edwards RR, Dolman AJ, Martel MO, et al.. Variability in conditioned pain modulation predicts response to NSAID treatment in patients with knee osteoarthritis. BMC Musculoskelet Disord. 2016;17:284. PMID 27412526
  25. Rosenberger DC, Blechschmidt V, Timmerman H, et al.. Challenges of neuropathic pain: focus on diabetic neuropathy. J Neural Transm (Vienna). 2020;127:589-624. PMID 32036431
  26. Fillingim RB, et al. Assessment of chronic pain: domains, methods, and mechanisms. J Pain. 2016;17:T10-T20. PMID 27586827
  27. Hansson P, et al. Usefulness and limitations of quantitative sensory testing: clinical and research application in neuropathic pain states. Pain. 2007;129:256-259. PMID 17451879
  28. Themistocleous AC, et al. Investigating genotype-phenotype relationship of extreme neuropathic pain disorders in a UK national cohort. Brain Commun. 2023;5:fcad037. PMID 36895957
  29. Lacomis D. Small-fiber neuropathy. Muscle Nerve. 2002;26:173-188. PMID 12210380
  30. Kool D, et al. Small fiber neuropathy. Int Rev Neurobiol. 2024;179:181-231. PMID 39580213