Evidence map›Paper›PMID 35563473›Full record

ArticleInternational journal of molecular sciences2022

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

Teemu Miettinen, Anni I Nieminen, Pekka Mäntyselkä, Eija Kalso, Jörn Lötsch

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Molecular Links between Sensory Nerves, Inflammation, and Pain 2.0.International journal of molecular sciences · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Teemu MiettinenPain Clinic, Department of Perioperative Medicine, Intensive Care and Pain Medicine, Helsinki University Hospital and SleepWell Research Programme, University of Helsinki, 00014 Helsinki, Finland.ORCID 0000-0003-3504-4663
Anni I NieminenMetabolomics Unit, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland.ORCID 0000-0001-8999-6040
Pekka MäntyselkäInstitute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Finland, and Primary Health Care Unit, Kuopio University Hospital, 70211 Kuopio, Finland.
Eija KalsoPain Clinic, Department of Perioperative Medicine, Intensive Care and Pain Medicine, Helsinki University Hospital and SleepWell Research Programme, University of Helsinki, 00014 Helsinki, Finland.ORCID 0000-0002-4899-605X
Jörn LötschInstitute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany.ORCID 0000-0002-5818-6958
University of Helsinki · FIGoethe University Frankfurt · DEUniversity of Eastern Finland · FI

Funding

Deutsche Forschungsgemeinschaft LO 612/16-1Governmental Research Funding TYH2014214Landesoffensive zur Entwicklung wissenschaftlich-ökonomischer Exzellenz (LOEWE) Zentrum: Translational Medicine and Pharmacology
6 · The paper itself

Abstract

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.

Indexed as

Chronic PainMetabolomeAdenosine MonophosphateBiomarkersCysteineFemaleHumansMachine LearningMetabolomicsMethionineNADObesityPhenotypeSleep Wake DisordersAdenosine MonophosphateBiomarkersCysteineMethionineNADchronic pain phenotypesmetabolic markersmetabolic pathwaysobesitysleep disorderssupervised machine

Identifiers

PMID35563473
PMCPMC9099732
OpenAlexW4225295960

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.