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A machine-learned analysis suggests non-redundant diagnostic information in olfactory subtests
(2019)
Background: The functional performance of the human sense of smell can be approached via assessment of the olfactory threshold, the ability to discriminate odors or the ability to identify odors. Contemporary clinical test batteries include all or a selection of these components, with some dissent about the required number and choice.
Methods: Olfactory thresholds, odor discrimination and odor identification scores were available from 10,714 subjects (3662 with anomia, 4299 with hyposmia, and 2752 with normal olfactory function). To assess, whether the olfactory subtests confer the same information or each subtest confers at least partly non-redundant information relevant to the olfactory diagnosis, we compared the diagnostic accuracy of supervised machine learning algorithms trained with the complete information from all three subtests with that obtained when performing the training with the information of only two or one subtests.
Results: The training of machine-learned algorithms with the full information about olfactory thresholds, odor discrimination and odor identification from 2/3 of the cases, resulted in a balanced olfactory diagnostic accuracy of 98% or better in the 1/3 remaining cases. The most pronounced decrease in the balanced accuracy, to approximately 85%, was observed when omitting olfactory thresholds from the training, whereas omitting odor discrimination or identification was associated with smaller decreases (balanced accuracies approximately 90%).
Conclusions: Results support partly non-redundant contributions of each olfactory subtest to the clinical olfactory diagnosis. Olfactory thresholds provided the largest amount of non-redundant information to the olfactory diagnosis.
Biomedical data obtained during cell experiments, laboratory animal research, or human studies often display a complex distribution. Statistical identification of subgroups in research data poses an analytical challenge. Here were introduce an interactive R-based bioinformatics tool, called “AdaptGauss”. It enables a valid identification of a biologically-meaningful multimodal structure in the data by fitting a Gaussian mixture model (GMM) to the data. The interface allows a supervised selection of the number of subgroups. This enables the expectation maximization (EM) algorithm to adapt more complex GMM than usually observed with a noninteractive approach. Interactively fitting a GMM to heat pain threshold data acquired from human volunteers revealed a distribution pattern with four Gaussian modes located at temperatures of 32.3, 37.2, 41.4, and 45.4 °C. Noninteractive fitting was unable to identify a meaningful data structure. Obtained results are compatible with known activity temperatures of different TRP ion channels suggesting the mechanistic contribution of different heat sensors to the perception of thermal pain. Thus, sophisticated analysis of the modal structure of biomedical data provides a basis for the mechanistic interpretation of the observations. As it may reflect the involvement of different TRP thermosensory ion channels, the analysis provides a starting point for hypothesis-driven laboratory experiments.
Background: Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain.
Methods: Over 500 demographic, clinical and psychological parameters were acquired up to 6 months after surgery from 1,000 women (aged 28–75 years) who were treated for breast cancer. Pain was assessed using an 11-point numerical rating scale before surgery and at months 1, 6, 12, 24, and 36. The ratings at months 12, 24, and 36 were used to allocate patents to either "persisting pain" or "non-persisting pain" groups. Unsupervised machine learning was applied to map the parameters to these diagnoses.
Results: A symbolic rule-based classifier tool was created that comprised 21 single or aggregated parameters, including demographic features, psychological and pain-related parameters, forming a questionnaire with "yes/no" items (decision rules). If at least 10 of the 21 rules applied, persisting pain was predicted at a cross-validated accuracy of 86% and a negative predictive value of approximately 95%.
Conclusions: The present machine-learned analysis showed that, even with a large set of parameters acquired from a large cohort, early identification of these patients is only partly successful. This indicates that more parameters are needed for accurate prediction of persisting pain. However, with the current parameters it is possible, with a certainty of almost 95%, to exclude the possibility of persistent pain developing in a woman being treated for breast cancer.
Consequences of a human TRPA1 genetic variant on the perception of nociceptive and olfactory stimuli
(2014)
Background: TRPA1 ion channels are involved in nociception and are also excited by pungent odorous substances. Based on reported associations of TRPA1 genetics with increased sensitivity to thermal pain stimuli, we therefore hypothesized that this association also exists for increased olfactory sensitivity.
Methods: Olfactory function and nociception was compared between carriers (n = 38) and non-carriers (n = 43) of TRPA1 variant rs11988795 G.A, a variant known to enhance cold pain perception. Olfactory function was quantified by assessing the odor threshold, odor discrimination and odor identification, and by applying 200-ms pulses of H2S intranasal. Nociception was assessed by measuring pain thresholds to experimental nociceptive stimuli (blunt pressure, electrical stimuli, cold and heat stimuli, and 200-ms intranasal pulses of CO2).
Results: Among the 11 subjects with moderate hyposmia, carriers of the minor A allele (n = 2) were underrepresented (34 carriers among the 70 normosmic subjects; p = 0.049). Moreover, carriers of the A allele discriminated odors significantly better than non-carriers (13.161.5 versus 12.361.6 correct discriminations) and indicated a higher intensity of the H2S stimuli (29.2613.2 versus 21612.8 mm VAS, p = 0.006), which, however, could not be excluded to have involved a trigeminal component during stimulation. Finally, the increased sensitivity to thermal pain could be reproduced.
Conclusions: The findings are in line with a previous association of a human TRPA1 variant with nociceptive parameters and extend the association to the perception of odorants. However, this addresses mainly those stimulants that involve a trigeminal component whereas a pure olfactory effect may remain disputable. Nevertheless, findings suggest that future TRPA1 modulating drugs may modify the perception of odorants.
Background and Aims: Mutations reducing the function of Nav1.7 sodium channels entail diminished pain perception and olfactory acuity, suggesting a link between nociception and olfaction at ion channel level. We hypothesized that if such link exists, it should work in both directions and gain-of-function Nav1.7 mutations known to be associated with increased pain perception should also increase olfactory acuity.
Methods: SCN9A variants were assessed known to enhance pain perception and found more frequently in the average population. Specifically, carriers of SCN9A variants rs41268673C>A (P610T; n = 14) or rs6746030C>T (R1150W; n = 21) were compared with non-carriers (n = 40). Olfactory function was quantified by assessing odor threshold, odor discrimination and odor identification using an established olfactory test. Nociception was assessed by measuring pain thresholds to experimental nociceptive stimuli (punctate and blunt mechanical pressure, heat and electrical stimuli).
Results: The number of carried alleles of the non-mutated SCN9A haplotype rs41268673C/rs6746030C was significantly associated with the comparatively highest olfactory threshold (0 alleles: threshold at phenylethylethanol dilution step 12 of 16 (n = 1), 1 allele: 10.6±2.6 (n = 34), 2 alleles: 9.5±2.1 (n = 40)). The same SCN9A haplotype determined the pain threshold to blunt pressure stimuli (0 alleles: 21.1 N/m2, 1 allele: 29.8±10.4 N/m2, 2 alleles: 33.5±10.2 N/m2).
Conclusions: The findings established a working link between nociception and olfaction via Nav1.7 in the gain-of-function direction. Hence, together with the known reduced olfaction and pain in loss-of-function mutations, a bidirectional genetic functional association between nociception and olfaction exists at Nav1.7 level.
Genetic association studies have shown their usefulness in assessing the role of ion channels in human thermal pain perception. We used machine learning to construct a complex phenotype from pain thresholds to thermal stimuli and associate it with the genetic information derived from the next-generation sequencing (NGS) of 15 ion channel genes which are involved in thermal perception, including ASIC1, ASIC2, ASIC3, ASIC4, TRPA1, TRPC1, TRPM2, TRPM3, TRPM4, TRPM5, TRPM8, TRPV1, TRPV2, TRPV3, and TRPV4. Phenotypic information was complete in 82 subjects and NGS genotypes were available in 67 subjects. A network of artificial neurons, implemented as emergent self-organizing maps, discovered two clusters characterized by high or low pain thresholds for heat and cold pain. A total of 1071 variants were discovered in the 15 ion channel genes. After feature selection, 80 genetic variants were retained for an association analysis based on machine learning. The measured performance of machine learning-mediated phenotype assignment based on this genetic information resulted in an area under the receiver operating characteristic curve of 77.2%, justifying a phenotype classification based on the genetic information. A further item categorization finally resulted in 38 genetic variants that contributed most to the phenotype assignment. Most of them (10) belonged to the TRPV3 gene, followed by TRPM3 (6). Therefore, the analysis successfully identified the particular importance of TRPV3 and TRPM3 for an average pain phenotype defined by the sensitivity to moderate thermal stimuli.
Aim: Exposure to opioids has been associated with epigenetic effects. Studies in rodents suggested a role of varying degrees of DNA methylation in the differential regulation of μ-opioid receptor expression across the brain.
Methods: In a translational investigation, using tissue acquired postmortem from 21 brain regions of former opiate addicts, representing a human cohort with chronic opioid exposure, μ-opioid receptor expression was analyzed at the level of DNA methylation, mRNA and protein.
Results & conclusion: While high or low μ-opioid receptor expression significantly correlated with local OPRM1 mRNA levels, there was no corresponding association with OPRM1 methylation status. Additional experiments in human cell lines showed that changes in DNA methylation associated with changes in μ-opioid expression were an order of magnitude greater than differences in brain. Hence, different degrees of DNA methylation associated with chronic opioid exposure are unlikely to exert a major role in the region-specificity of μ-opioid receptor expression in the human brain.
Inverted perceptual judgment of nociceptive stimuli at threshold level following inconsistent cues
(2015)
Objective: The perception of pain is susceptible to modulation by psychological and contextual factors. It has been shown that subjects judge noxious stimuli as more painful in a respective suggestive context, which disappears when the modifying context is resolved. However, a context in which subjects judge the painfulness of a nociceptive stimulus in exactly the opposite direction to that of the cues has never been shown so far.
Methods: Nociceptive stimuli (300 ms intranasal gaseous CO2) at the individual pain threshold level were applied after a visual cue announcing the stimulus as either "no pain", merely a "stimulus", or "pain". Among the stimuli at threshold level, other CO2 stimuli that were clearly below or above pain threshold were randomly interspersed. These were announced beforehand in 12 subjects randomly with correct or incorrect cues, i.e., clearly painful or clearly non-painful stimuli were announced equally often as not painful or painful. By contrast, in a subsequent group of another 12 subjects, the stimuli were always announced correctly with respect to the evoked pain.
Results: The random and often incorrect announcement of stimuli clearly below or above pain threshold caused the subjects to rate the stimuli at pain-threshold level in the opposite direction of the cue, i.e., when the stimuli were announced as "pain" significantly more often than as non-painful and vice versa (p < 10-4). By contrast, in the absence of incongruence between announcement and perception of the far-from-threshold stimuli, stimuli at pain threshold were rated in the cued direction.
Conclusions: The present study revealed the induction of associations incongruent with a given message in the perception of pain. We created a context of unreliable cues whereby subjects perceived the stimulus opposite to that suggested by a prior cue, i.e., potentially nociceptive stimuli at pain threshold level that were announced as painful were judged as non-painful and vice versa. These findings are consistent with reported data on the effects of distrust on non-painful cognitive responses.
Background: Prevention of persistent pain after breast cancer surgery, via early identification of patients at high risk, is a clinical need. Psychological factors are among the most consistently proposed predictive parameters for the development of persistent pain. However, repeated use of long psychological questionnaires in this context may be exhaustive for a patient and inconvenient in everyday clinical practice.
Methods: Supervised machine learning was used to create a short form of questionnaires that would provide the same predictive performance of pain persistence as the full questionnaires in a cohort of 1000 women followed up for 3 yr after breast cancer surgery. Machine-learned predictors were first trained with the full-item set of Beck's Depression Inventory (BDI), Spielberger's State–Trait Anxiety Inventory (STAI), and the State–Trait Anger Expression Inventory (STAXI-2). Subsequently, features were selected from the questionnaires to create predictors having a reduced set of items.
Results: A combined seven-item set of 10% of the original psychological questions from STAI and BDI, provided the same predictive performance parameters as the full questionnaires for the development of persistent postsurgical pain. The seven-item version offers a shorter and at least as accurate identification of women in whom pain persistence is unlikely (almost 95% negative predictive value).
Conclusions: Using a data-driven machine-learning approach, a short list of seven items from BDI and STAI is proposed as a basis for a predictive tool for the persistence of pain after breast cancer surgery.
The comprehensive assessment of pain-related human phenotypes requires combinations of nociceptive measures that produce complex high-dimensional data, posing challenges to bioinformatic analysis. In this study, we assessed established experimental models of heat hyperalgesia of the skin, consisting of local ultraviolet-B (UV-B) irradiation or capsaicin application, in 82 healthy subjects using a variety of noxious stimuli. We extended the original heat stimulation by applying cold and mechanical stimuli and assessing the hypersensitization effects with a clinically established quantitative sensory testing (QST) battery (German Research Network on Neuropathic Pain). This study provided a 246 × 10-sized data matrix (82 subjects assessed at baseline, following UV-B application, and following capsaicin application) with respect to 10 QST parameters, which we analyzed using machine-learning techniques. We observed statistically significant effects of the hypersensitization treatments in 9 different QST parameters. Supervised machine-learned analysis implemented as random forests followed by ABC analysis pointed to heat pain thresholds as the most relevantly affected QST parameter. However, decision tree analysis indicated that UV-B additionally modulated sensitivity to cold. Unsupervised machine-learning techniques, implemented as emergent self-organizing maps, hinted at subgroups responding to topical application of capsaicin. The distinction among subgroups was based on sensitivity to pressure pain, which could be attributed to sex differences, with women being more sensitive than men. Thus, while UV-B and capsaicin share a major component of heat pain sensitization, they differ in their effects on QST parameter patterns in healthy subjects, suggesting a lack of redundancy between these models.