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Rischke S, Poor SM, Gurke R, Hahnefeld L, Köhm M, Ultsch A, Geisslinger G, Behrens F, Lötsch J. Machine learning identifies right index finger tenderness as key signal of DAS28-CRP based psoriatic arthritis activity. Sci Rep 2023; 13:22710. [PMID: 38123604 PMCID: PMC10733369 DOI: 10.1038/s41598-023-49574-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 12/09/2023] [Indexed: 12/23/2023] Open
Abstract
Psoriatic arthritis (PsA) is a chronic inflammatory systemic disease whose activity is often assessed using the Disease Activity Score 28 (DAS28-CRP). The present study was designed to investigate the significance of individual components within the score for PsA activity. A cohort of 80 PsA patients (44 women and 36 men, aged 56.3 ± 12 years) with a range of disease activity from remission to moderate was analyzed using unsupervised and supervised methods applied to the DAS28-CRP components. Machine learning-based permutation importance identified tenderness in the metacarpophalangeal joint of the right index finger as the most informative item of the DAS28-CRP for PsA activity staging. This symptom alone allowed a machine learned (random forests) classifier to identify PsA remission with 67% balanced accuracy in new cases. Projection of the DAS28-CRP data onto an emergent self-organizing map of artificial neurons identified outliers, which following augmentation of group sizes by emergent self-organizing maps based generative artificial intelligence (AI) could be defined as subgroups particularly characterized by either tenderness or swelling of specific joints. AI-assisted re-evaluation of the DAS28-CRP for PsA has narrowed the score items to a most relevant symptom, and generative AI has been useful for identifying and characterizing small subgroups of patients whose symptom patterns differ from the majority. These findings represent an important step toward precision medicine that can address outliers.
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Affiliation(s)
- Samuel Rischke
- Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
| | - Sorwe Mojtahed Poor
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
- Department of Rheumatology, Goethe University Frankfurt, University Hospital, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
| | - Robert Gurke
- Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
| | - Lisa Hahnefeld
- Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
| | - Michaela Köhm
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
- Department of Rheumatology, Goethe University Frankfurt, University Hospital, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
| | - Alfred Ultsch
- DataBionics Research Group, University of Marburg, Hans-Meerwein-Straße, 35032, Marburg, Germany
| | - Gerd Geisslinger
- Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
| | - Frank Behrens
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany
- Department of Rheumatology, Goethe University Frankfurt, University Hospital, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
| | - Jörn Lötsch
- Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany.
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt Am Main, Germany.
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Lötsch J, Mayer B, Kringel D. Machine learning analysis predicts a person's sex based on mechanical but not thermal pain thresholds. Sci Rep 2023; 13:7332. [PMID: 37147321 PMCID: PMC10163041 DOI: 10.1038/s41598-023-33337-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/11/2023] [Indexed: 05/07/2023] Open
Abstract
Sex differences in pain perception have been extensively studied, but precision medicine applications such as sex-specific pain pharmacology have barely progressed beyond proof-of-concept. A data set of pain thresholds to mechanical (blunt and punctate pressure) and thermal (heat and cold) stimuli applied to non-sensitized and sensitized (capsaicin, menthol) forearm skin of 69 male and 56 female healthy volunteers was analyzed for data structures contingent with the prior sex structure using unsupervised and supervised approaches. A working hypothesis that the relevance of sex differences could be approached via reversibility of the association, i.e., sex should be identifiable from pain thresholds, was verified with trained machine learning algorithms that could infer a person's sex in a 20% validation sample not seen to the algorithms during training, with balanced accuracy of up to 79%. This was only possible with thresholds for mechanical stimuli, but not for thermal stimuli or sensitization responses, which were not sufficient to train an algorithm that could assign sex better than by guessing or when trained with nonsense (permuted) information. This enabled the translation to the molecular level of nociceptive targets that convert mechanical but not thermal information into signals interpreted as pain, which could eventually be used for pharmacological precision medicine approaches to pain. By exploiting a key feature of machine learning, which allows for the recognition of data structures and the reduction of information to the minimum relevant, experimental human pain data could be characterized in a way that incorporates "non" logic that could be translated directly to the molecular pharmacological level, pointing toward sex-specific precision medicine for pain.
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Affiliation(s)
- Jörn Lötsch
- Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany.
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt, Germany.
| | - Benjamin Mayer
- Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany
| | - Dario Kringel
- Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany
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