Institute of Clinical Pharmacology, Goethe - University, Frankfurt am Main, Germany.
Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Project Group Translational Medicine and Pharmacology TMP, Frankfurt am Main, Germany.
Eur J Pain. 2018 May;22(5):862-874. doi: 10.1002/ejp.1173. Epub 2018 Jan 16.
Pain in response to noxious cold has a complex molecular background probably involving several types of sensors. A recent observation has been the multimodal distribution of human cold pain thresholds. This study aimed at analysing reproducibility and stability of this observation and further exploration of data patterns supporting a complex background.
Pain thresholds to noxious cold stimuli (range 32-0 °C, tonic: temperature decrease -1 °C/s, phasic: temperature decrease -8 °C/s) were acquired in 148 healthy volunteers. The probability density distribution was analysed using machine-learning derived methods implemented as Gaussian mixture modeling (GMM), emergent self-organizing maps and self-organizing swarms of data agents.
The probability density function of pain responses was trimodal (mean thresholds at 25.9, 18.4 and 8.0 °C for tonic and 24.5, 18.1 and 7.5 °C for phasic stimuli). Subjects' association with Gaussian modes was consistent between both types of stimuli (weighted Cohen's κ = 0.91). Patterns emerging in self-organizing neuronal maps and swarms could be associated with different trends towards decreasing cold pain sensitivity in different Gaussian modes. On self-organizing maps, the third Gaussian mode emerged as particularly distinct.
Thresholds at, roughly, 25 and 18 °C agree with known working temperatures of TRPM8 and TRPA1 ion channels, respectively, and hint at relative local dominance of either channel in respective subjects. Data patterns suggest involvement of further distinct mechanisms in cold pain perception at lower temperatures. Findings support data science approaches to identify biologically plausible hints at complex molecular mechanisms underlying human pain phenotypes.
Sensitivity to pain is heterogeneous. Data-driven computational research approaches allow the identification of subgroups of subjects with a distinct pattern of sensitivity to cold stimuli. The subgroups are reproducible with different types of noxious cold stimuli. Subgroups show pattern that hints at distinct and inter-individually different types of the underlying molecular background.
对有害冷刺激的疼痛具有复杂的分子背景，可能涉及几种类型的传感器。最近的一个观察结果是人类冷痛阈值的多模态分布。本研究旨在分析这种观察结果的可重复性和稳定性，并进一步探索支持复杂背景的数据模式。
在 148 名健康志愿者中获得有害冷刺激（范围 32-0°C，持续：温度下降-1°C/s，相位：温度下降-8°C/s）的疼痛阈值。使用机器学习衍生的方法分析概率密度分布，方法包括高斯混合建模（GMM）、突发自组织映射和数据代理的自组织群。
疼痛反应的概率密度函数呈三峰分布（持续刺激的平均阈值为 25.9、18.4 和 8.0°C，相位刺激为 24.5、18.1 和 7.5°C）。两种类型刺激下，受试者与高斯模式的关联是一致的（加权 Cohen's κ=0.91）。在自组织神经元地图和群中出现的模式可以与不同高斯模式下冷痛敏感性降低的不同趋势相关联。在自组织映射中，第三个高斯模式特别明显。
大约 25 和 18°C 的阈值与已知的 TRPM8 和 TRPA1 离子通道的工作温度相对应，并且暗示在各自的受试者中相对局部主导着相应的通道。数据模式表明，在较低温度下，冷痛感知涉及到进一步的不同机制。研究结果支持数据科学方法，以确定人类疼痛表型复杂分子机制的生物学上合理的线索。
疼痛敏感性是异质的。数据驱动的计算研究方法允许识别对冷刺激具有不同敏感性模式的受试者亚组。亚组可以通过不同类型的有害冷刺激来重现。亚组表现出的模式暗示了不同的、个体间不同的潜在分子背景类型。
