Integrated quantitative sensory testing and psychological phenotyping dataset for capsaicin-induced neuropathic pain...

Experimental pain models are used to investigate the analgesic effects in healthy volunteers during the early stages of drug development. However, the predictive value of these models for clinical pain requires continuous reinforcement. One question is whether neuropathic pain patterns can be induced in healthy individuals. Another question is whether healthy volunteers can be pre-selected based on the inducibility of neuropathic-like sensory patterns under controlled experimental conditions. A third question is whether psychological traits contribute to this susceptibility. The present dataset originates from a study that addressed these questions by combining standardized sensory and psychological phenotyping of the same participants. The working hypothesis was that patterns of clinical neuropathic pain could be induced in a subgroup of healthy volunteers by applying topical capsaicin and that susceptible individuals could be identified based on their psychological phenotype. To evaluate neuropathic pain inducibility, a standardized quantitative sensory testing (QST) battery, developed by the German Research Network on Neuropathic Pain, was administered to 110 healthy volunteers (46 men aged 18-36 years) at two sites: the untreated contralateral skin and the contralateral skin sensitized with topical capsaicin. The QST battery includes 11 parameters that cover thermal and mechanical detection and pain thresholds, mechanical pain sensitivity, and temporal pain summation (wind-up ratio), as well as vibration and pressure pain thresholds. Z-scores were computed against a published normative reference cohort matched by age and sex, enabling direct comparison of individual sensory profiles with those of patients with clinical neuropathic pain. Additionally, seven validated psychometric questionnaires covering dispositional optimism, depressive mood, somatoform symptoms, state anxiety, pain catastrophizing, pain anxiety, and pain vigilance were administered to all participants. The dataset is provided as three comma-separated values (CSV) files. The first file contains z-transformed QST data in an 110 × 23 matrix (11 QST parameters at two sites per participant plus unique identifier). The second file contains psychometric questionnaire total scores in an 110 × 8 matrix (7 psychological parameters plus unique identifier). The third file contains participant metadata, including age, sex, test and control site coding for QST, and a binary classification variable indicating whether the QST profile at the capsaicin-treated site was consistent with a neuropathic sensory pattern. All three files have consistent subject identifiers. This dataset can serve as a resource for future human pain research investigations. Integrating standardized sensory phenotyping and comprehensive psychological characterization of the same individuals allows for the systematic study of psychological predictors of pain sensitivity and neuropathic pain susceptibility. Precomputed neuropathic pattern classifications facilitate supervised machine learning approaches, and z-transformed QST values enable direct benchmarking against established clinical neuropathic pain profiles. The dataset also supports secondary analyses, such as investigating sex differences in experimental pain sensitivity. Additionally, it can serve as an independent reference cohort for machine learning applications. All data are fully anonymized and structured.