Cargando…

Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes

Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics...

Descripción completa

Detalles Bibliográficos
Autores principales: Miettinen, Teemu, Nieminen, Anni I., Mäntyselkä, Pekka, Kalso, Eija, Lötsch, Jörn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099732/
https://www.ncbi.nlm.nih.gov/pubmed/35563473
http://dx.doi.org/10.3390/ijms23095085
_version_ 1784706679178264576
author Miettinen, Teemu
Nieminen, Anni I.
Mäntyselkä, Pekka
Kalso, Eija
Lötsch, Jörn
author_facet Miettinen, Teemu
Nieminen, Anni I.
Mäntyselkä, Pekka
Kalso, Eija
Lötsch, Jörn
author_sort Miettinen, Teemu
collection PubMed
description Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics markers at the interface of persistent pain, sleep, and obesity in 71 men and 122 women undergoing tertiary pain care. They were examined for patterns in d = 97 metabolomic markers that segregated patients with a relatively benign pain phenotype (low and little bothersome pain) from those with more severe clinical symptoms (high pain intensity, more bothersome pain, and co-occurring problems such as sleep disturbance). Two independent lines of data analysis were pursued. First, a data-driven supervised machine learning-based approach was used to identify the most informative metabolic markers for complex phenotype assignment. This pointed primarily at adenosine monophosphate (AMP), asparagine, deoxycytidine, glucuronic acid, and propionylcarnitine, and secondarily at cysteine and nicotinamide adenine dinucleotide (NAD) as informative for assigning patients to clinical pain phenotypes. After this, a hypothesis-driven analysis of metabolic pathways was performed, including sleep and obesity. In both the first and second line of analysis, three metabolic markers (NAD, AMP, and cysteine) were found to be relevant, including metabolic pathway analysis in obesity, associated with changes in amino acid metabolism, and sleep problems, associated with downregulated methionine metabolism. Taken together, present findings provide evidence that metabolomic changes associated with co-occurring problems may play a role in the development of severe pain. Co-occurring problems may influence each other at the metabolomic level. Because the methionine and glutathione metabolic pathways are physiologically linked, sleep problems appear to be associated with the first metabolic pathway, whereas obesity may be associated with the second.
format Online
Article
Text
id pubmed-9099732
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-90997322022-05-14 Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes Miettinen, Teemu Nieminen, Anni I. Mäntyselkä, Pekka Kalso, Eija Lötsch, Jörn Int J Mol Sci Article Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics markers at the interface of persistent pain, sleep, and obesity in 71 men and 122 women undergoing tertiary pain care. They were examined for patterns in d = 97 metabolomic markers that segregated patients with a relatively benign pain phenotype (low and little bothersome pain) from those with more severe clinical symptoms (high pain intensity, more bothersome pain, and co-occurring problems such as sleep disturbance). Two independent lines of data analysis were pursued. First, a data-driven supervised machine learning-based approach was used to identify the most informative metabolic markers for complex phenotype assignment. This pointed primarily at adenosine monophosphate (AMP), asparagine, deoxycytidine, glucuronic acid, and propionylcarnitine, and secondarily at cysteine and nicotinamide adenine dinucleotide (NAD) as informative for assigning patients to clinical pain phenotypes. After this, a hypothesis-driven analysis of metabolic pathways was performed, including sleep and obesity. In both the first and second line of analysis, three metabolic markers (NAD, AMP, and cysteine) were found to be relevant, including metabolic pathway analysis in obesity, associated with changes in amino acid metabolism, and sleep problems, associated with downregulated methionine metabolism. Taken together, present findings provide evidence that metabolomic changes associated with co-occurring problems may play a role in the development of severe pain. Co-occurring problems may influence each other at the metabolomic level. Because the methionine and glutathione metabolic pathways are physiologically linked, sleep problems appear to be associated with the first metabolic pathway, whereas obesity may be associated with the second. MDPI 2022-05-03 /pmc/articles/PMC9099732/ /pubmed/35563473 http://dx.doi.org/10.3390/ijms23095085 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Miettinen, Teemu
Nieminen, Anni I.
Mäntyselkä, Pekka
Kalso, Eija
Lötsch, Jörn
Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes
title Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes
title_full Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes
title_fullStr Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes
title_full_unstemmed Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes
title_short Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes
title_sort machine learning and pathway analysis-based discovery of metabolomic markers relating to chronic pain phenotypes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099732/
https://www.ncbi.nlm.nih.gov/pubmed/35563473
http://dx.doi.org/10.3390/ijms23095085
work_keys_str_mv AT miettinenteemu machinelearningandpathwayanalysisbaseddiscoveryofmetabolomicmarkersrelatingtochronicpainphenotypes
AT nieminenannii machinelearningandpathwayanalysisbaseddiscoveryofmetabolomicmarkersrelatingtochronicpainphenotypes
AT mantyselkapekka machinelearningandpathwayanalysisbaseddiscoveryofmetabolomicmarkersrelatingtochronicpainphenotypes
AT kalsoeija machinelearningandpathwayanalysisbaseddiscoveryofmetabolomicmarkersrelatingtochronicpainphenotypes
AT lotschjorn machinelearningandpathwayanalysisbaseddiscoveryofmetabolomicmarkersrelatingtochronicpainphenotypes