Chronic pain is a significant public health problem in the United States, affecting approximately 100 million people. Yet there is a lack of robust biomarkers for clinical use in chronic pain conditions. Downstream effects of environmental, genomic, and proteomic variations in individuals with chronic pain conditions can be identified and quantified using a metabolomic approach.
Aim/Design:
The purpose of this systematic review was to examine the literature for reports of potential metabolomic signatures associated with chronic pain conditions.
Methods:
We searched relevant electronic databases for published studies that used various metabolomic approaches to investigate chronic pain conditions among subjects of all ages.
Results:
Our search identified a total of 586 articles, 18 of which are included in this review. The reviewed studies used metabolomics to investigate fibromyalgia (n = 5), osteoarthritis (n = 4), migraine (n = 3), musculoskeletal pain (n = 2), and other chronic pain conditions (n = 1/condition). Results show that several known and newly identified metabolites differ in individuals with chronic pain conditions compared to those without these conditions. These include amino acids (e.g., glutamine, serine, and phenylalanine) and intermediate products (e.g., succinate, citrate, acetylcarnitine, and N-acetylornithine) of pathways that metabolize various macromolecules.
Conclusion:
Though more high-quality research is needed, this review provides insights into potential biomarkers for future metabolomics studies in people with chronic pain conditions.
AdamsS. B.SettonL. A.KensickiE.BolognesiM. P.TothA. P.NettlesD. L. (2012). Global metabolic profiling of human osteoarthritic synovium. Osteoarthritis and Cartilage, 20(1), 64–67. https://doi.org/10.1016/j.joca.2011.10.010
2.
ArnoldJ. N.SaldovaR.HamidU. M. A.RuddP. M. (2008). Evaluation of the serum N-linked glycome for the diagnosis of cancer and chronic inflammation. Proteomics, 8(16), 3284–3293. https://doi.org/10.1002/pmic.200800163
3.
BilloirE.NavratilV.BlaiseB. J. (2015). Sample size calculation in metabolic phenotyping studies. Briefings in Bioinformatics, 16(5), 813–819. https://doi.org/10.1093/bib/bbu052
4.
BjørndalB.AlteråsE. K.LindquistC.SvardalA.SkorveJ.BergeR. K. (2018). Associations between fatty acid oxidation, hepatic mitochondrial function, and plasma acylcarnitine levels in mice. Nutrition & Metabolism, 15(10). https://doi.org/10.1186/s12986-018-0241-7
5.
CaboniP.LioriB.KumarA.SantoruM. L.AsthanaS.PieroniE.FaisA.EraB.CacaceE.RuggieroV.AtzoriL. (2014). Metabolomics analysis and modeling suggest a lysophosphocholines-PAF receptor interaction in fibromyalgia. PLoS One, 9(9), e107626. https://doi.org/10.1371/journal.pone.0107626
6.
ChinS.-H.HuangW.-L.AkterS.BinksM. (2019). Obesity and pain: A systematic review. International Journal of Obesity, 44, 969–979. https://doi.org/10.1038/s41366-019-0505-y
7.
ClishC. B. (2015). Metabolomics: An emerging but powerful tool for precision medicine. Molecular Case Studies, 1(1), a000588. https://doi.org/10.1101/mcs.a000588
8.
CostelloC. A.HuT.LiuM.ZhangW.FureyA.FanZ.RahmanP.RandellE. W.ZhaiG. (2020). Metabolomics signature for non-responders to total joint replacement surgery in primary osteoarthritis patients: The Newfoundland Osteoarthritis Study. Journal of Orthopaedic Research, 38(4), 793–802. https://doi.org/10.1002/jor.24529
9.
De JongA. J.De Lange-BrokaarB. J.Klein-WieringaI. R.HeijinkM.HoekstraA.EvertsB.KwekkeboomJ. C.BerkersC. R.GieraM.HuizingaT. W.KloppenburgM.ToesR. E.Ioan-FacsinayA. (2016). Synovial CD4+T cells associate with pain in osteoarthritis: Is there a role for fatty acids?Osteoarthritis and Cartilage, 24, S321.
10.
Dinis-OliveiraR. J. (2019). Metabolism and metabolomics of opiates: A long way of forensic implications to unravel. Journal of Forensic and Legal Medicine, 61, 128–140. https://doi.org/10.1016/j.jflm.2018.12.005
11.
FillingimR. (2015). Biopsychosocial contributions to sex differences in pain. Royal College of Obstetricians and Gynaecologists, 122(6), 769–769. https://doi.org/doi:10.1111/1471-0528.13337
12.
FincoG.LocciE.MuraP.MassaR.NotoA.MusuM.LandoniG.d’AlojaE.De-GiorgioF.ScanoP.EvangelistaM. (2016). Can urine metabolomics be helpful in differentiating neuropathic and nociceptive pain? A proof-of-concept study. PLoS One, 11(3), e0150476. https://doi.org/10.1371/journal.pone.0150476
13.
FinleyC. R.ChanD. S.GarrisonS.KorownykC.KolberM. R.CampbellS.EurichD. T.LindbladA. J.VandermeerB.AllanG. M. (2018). What are the most common conditions in primary care? Systematic review. Canadian Family Physician, 64(11), 832–840.
14.
ForouzanA.MasoumiK.RahimF.MoezziM.KhavaninA.RanjbariN.Amal SakiM.Fallah AmoliA.AkhianiN.GhourchianF. (2018). Diagnostic accuracy of serum and urine S100A8/A9 and serum amyloid A in probable acute abdominal pain at emergency department. Disease Markers, 2018, 6457347. https://doi.org/10.1155/2018/6457347
GerraM. C.DagostinoC.D’AgnelliS.BoggianiL.RizzaV.MarchesiniM.AllegriM.FanelliG. (2017). Omics as a potential tool to identify biomarkers and to clarify the mechanism of chronic pain development. Scandinavian Journal of Pain, 16(1), 187. https://doi.org/10.1016/j.sjpain.2017.04.064
17.
Giorgi-CollS.AmaralA. I.HutchinsonP. J. A.KotterM. R.CarpenterK. L. H. (2017). Succinate supplementation improves metabolic performance of mixed glial cell cultures with mitochondrial dysfunction. Scientific Reports, 7(1), 1003. https://doi.org/10.1038/s41598-017-01149-w
18.
Guasch-FerréM.HrubyA.ToledoE.ClishC. B.Martínez-GonzálezM. A.Salas-SalvadóJ.HuF. B. (2016). Metabolomics in prediabetes and diabetes: A systematic review and meta-analysis. Diabetes Care, 39(5), 833–846.
19.
HackshawK. V.AykasD. P.SigurdsonG. T.PlansM.MadiaiF.YuL.BuffingtonC. A. T.GiustiM. M.Rodriguez-SaonaL. (2019). Metabolic fingerprinting for diagnosis of fibromyalgia and other rheumatologic disorders. Journal of Biological Chemistry, 294(7), 2555–2568. https://doi.org/10.1074/jbc.RA118.005816
20.
InquimbertP.MollM.LatremoliereA.TongC. K.WhangJ.SheehanG. F.SmithB. M.KorbE.AthiéM. C. P.BabaniyiO.GhasemlouN.YanagawaY.AllisC. D.HofP. R.ScholzJ. (2018). NMDA receptor activation underlies the loss of spinal dorsal horn neurons and the transition to persistent pain after peripheral nerve injury. Cell Reports, 23(9), 2678–2689. https://doi.org/10.1016/j.celrep.2018.04.107
21.
Institute of Medicine. (2011). Relieving pain in America: A blueprint for transforming prevention, care, education, and research. National Academies Press. https://doi.org/10.17226/13172
LivshitsG.MalkinI.BowyerR. C. E.VerdiS.BellJ. T.MenniC.WilliamsF. M. K.StevesC. J. (2018). Multi-OMICS analyses of frailty and chronic widespread musculoskeletal pain suggest involvement of shared neurological pathways. Pain, 159(12), 2565–2572. https://doi.org/10.1097/j.pain.0000000000001364
25.
MacDonaldK.KrishnanA.CervenkaE.HuG.GuadagnoE.TrakadisY. (2019). Biomarkers for major depressive and bipolar disorders using metabolomics: A systematic review. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 180(2), 122–137. https://doi.org/https://doi/full/10.1002/ajmg.b.32680
26.
MackiewiczA.MackiewiczK. (1995). Glycoforms of serum α1-acid glycoprotein as markers of inflammation and cancer. Glycoconjugate Journal, 12(3), 241–247. https://doi.org/10.1007/bf00731326
27.
MalatjiB. G.MasonS.MienieL. J.WeversR. A.MeyerH.van ReenenM.ReineckeC. J. (2019). The GC–MS metabolomics signature in patients with fibromyalgia syndrome directs to dysbiosis as an aspect contributing factor of FMS pathophysiology. Metabolomics, 15. https://doi.org/10.1007/s11306-019-1513-6
28.
MalatjiB. G.MeyerH.MasonS.EngelkeU. F. H.WeversR. A.ReenenM.ReineckeC. J. (2017). A diagnostic biomarker profile for fibromyalgia syndrome based on an NMR metabolomics study of selected patients and controls. BMC Neurology, 17. https://doi.org/10.1186/s12883-017-0863-9
29.
MantyselkaP.Ali-SistoT.KautiainenH.NiskanenL.ViinamakiH.VelagapudiV.LehtoS. M. (2017). The association between musculoskeletal pain and circulating ornithine: A population-based study. Pain Medicine, 18(6), 1145–1151. https://doi.org/10.1093/pm/pnw285
30.
MedeirosP.Negrini-FerrariS. E.PalazzoE.MaioneS.FerreiraS. H.de FreitasR. L.CoimbraN. C. (2019). N-methyl-D-aspartate receptors in the prelimbic cortex are critical for the maintenance of neuropathic pain. Neurochemistry Research, 44(9), 2068–2080. https://doi.org/10.1007/s11064-019-02843-z
31.
MeissnerA.van der PlasA. A.van DasselaarN. T.DeelderA. M.van HiltenJ. J.MayborodaO. A. (2014). 1H-NMR metabolic profiling of cerebrospinal fluid in patients with complex regional pain syndrome-related dystonia. Pain, 155(1), 190–196. https://doi.org/10.1016/j.pain.2013.10.005
32.
MenziesV.StarkweatherA.YaoY.ThackerL. R.GarrettT. J.Swift-ScanlanT.KellyD. L.PatelP.LyonD. E. (2020). Metabolomic differentials in women with and without fibromyalgia. Clinical and Translational Science, 13(1), 67–77. https://doi.org/10.1111/cts.12679
33.
MickiewiczB.KellyJ. J.LudwigT. E.WeljieA. M.WileyJ. P.SchmidtT. A.VogelH. J. (2015). Metabolic analysis of knee synovial fluid as a potential diagnostic approach for osteoarthritis. Journal of Orthopaedic Research, 33(11), 1631–1638. https://doi.org/10.1002/jor.22949
34.
MoherD.ShamseerL.ClarkeM.GhersiD.LiberatiA.PetticrewM.ShekelleP.StewartL. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4, 1. https://doi.org/10.1186/2046-4053-4-1
35.
NyamundandaG.GormleyI. C.FanY.GallagherW. M.BrennanL. (2013). MetSizeR: Selecting the optimal sample size for metabolomic studies using an analysis based approach. BMC Bioinformatics, 14, 338–338. https://doi.org/10.1186/1471-2105-14-338
36.
OnderwaterG. L. J.LigthartL.BotM.DemirkanA.FuJ.van der KallenC. J. H.VijfhuizenL. S.PoolR.LiuJ.VanmolkotF. H. M.BeekmanM.WenK.-X.AminN.ThesingC. S.PijpersJ. A.KiesD. A.ZielmanR.de BoerI.van GreevenbroekM. M. J…van den MaagdenbergA. M. J. M. (on behalf of the BBMRI Metabolomics Consortium). (2019). Large-scale plasma metabolome analysis reveals alterations in HDL metabolism in migraine. Neurology, 92(16), e1899–e1911. https://doi.org/10.1212/WNL.0000000000007313
37.
ParkerK. S.CrowleyJ. R.Stephens-ShieldsA. J.van BokhovenA.LuciaM. S.LaiH. H.AndrioleG. L.HootonT. M.MullinsC.HendersonJ. P. (2016). Urinary metabolomics identifies a molecular correlate of interstitial cystitis/bladder pain syndrome in a Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network cohort. EBioMedicine, 7, 167–174. https://doi.org/10.1016/j.ebiom.2016.03.040
38.
QuansahE.PeelaertsW.LangstonJ. W.SimonD. K.ColcaJ.BrundinP. (2018). Targeting energy metabolism via the mitochondrial pyruvate carrier as a novel approach to attenuate neurodegeneration. Molecular Neurodegeneration, 13(1), 28. https://doi.org/10.1186/s13024-018-0260-x
39.
ShimadaM.HibinoM.TakeshitaA. (2017). Dietary supplementation with myo-inositol reduces hepatic triglyceride accumulation and expression of both fructolytic and lipogenic genes in rats fed a high-fructose diet. Nutrition Research, 47, 21–27. https://doi.org/10.1016/j.nutres.2017.08.005
SmithL.Villaret-CazadamontJ.ClausS. P.CanletC.GuillouH.CabatonN. J.Ellero-SimatosS. (2020). Important considerations for sample collection in metabolomics studies with a special focus on applications to liver functions. Metabolites, 10(3), 104. https://doi.org/https://doi.org/10.3390/metabo10030104
42.
SmolenskaZ.SmolenskiR. T.ZdrojewskiZ. (2016). Plasma concentrations of amino acid and nicotinamide metabolites in rheumatoid arthritis—potential biomarkers of disease activity and drug treatment. Biomarkers, 21(3), 218–224. https://doi.org/10.3109/1354750X.2015.1130746
43.
Trbojevic-AkmacicI.VuckovicF.VilajM.SkelinA.KarssenL. C.KristicJ.JuricJ.MomcilovicA.SimunovicJ.ManginoM.De GregoriM.MarchesiniM.DagostinoC.StambukJ.NovokmetM.RauckR.AulchenkoY. S.PrimoracD.KapuralL.…LaucG. (2018). Plasma N-glycome composition associates with chronic low back pain. Biochimica et Biophysica Acta (BBA) - General Subjects, 1862(10), 2124–2133. https://doi.org/10.1016/j.bbagen.2018.07.003
XuZ.ChenT.LuoJ.DingS.GaoS.ZhangJ. (2017). Cartilaginous metabolomic study reveals potential mechanisms of osteophyte formation in osteoarthritis. Journal of Proteome Research, 16(4), 1425–1435. https://doi.org/10.1021/acs.jproteome.6b00676
46.
ZielmanR.PostmaR.BakelsF.Van OosterhoutW. P. J.Van Der SarS. A.TerwindtG. M.Van Der MaagdenbergA. M. J. M.DeelderA. M.MayborodaO. A.MeissnerA.FerrarM. D. (2013). Metabolomics of migraine: H-NMR study of cerebrospinal fluid [Conference Abstract]. Cephalalgia, 33, 150–151. https://doi.org/10.1177/0333102413490487
47.
ZielmanR.PostmaR.VerhoevenA.BakelsF.van OosterhoutW. P.MeissnerA.van den MaagdenbergA. M.TerwindtG. M.MayborodaO. A.FerrariM. D. (2016). Metabolomic changes in CSF of migraine patients measured with 1H-NMR spectroscopy. Molecular BioSystems, 12(12), 3674–3682. https://doi.org/doi:10.1039/c6mb00424e
