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Article

Lipidomic Profiling Identifies Serum Lipids Associated with Persistent Multisite Musculoskeletal Pain

by
Canchen Ma
1,†,
Ming Liu
2,†,
Jing Tian
1,
Guangju Zhai
2,
Flavia Cicuttini
3,
Yvette L. Schooneveldt
4,
Peter J. Meikle
4,5,
Graeme Jones
1 and
Feng Pan
1,*
1
Menzies Institute for Medical Research, University of Tasmania, Private Bag 23, Hobart, TAS 7000, Australia
2
Division of Biomedical Sciences (Genetics), Faculty of Medicine, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
3
Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne, VIC 3004, Australia
4
Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
5
Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3004, Australia
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Metabolites 2022, 12(3), 206; https://doi.org/10.3390/metabo12030206
Submission received: 14 January 2022 / Revised: 17 February 2022 / Accepted: 22 February 2022 / Published: 25 February 2022
(This article belongs to the Special Issue Metabolomics of Complex Traits II)
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Versions Notes

Abstract

:
Lipid mediators have been suggested to have a role in pain sensitivity and response; however, longitudinal data on lipid metabolites and persistent multisite musculoskeletal pain (MSMP) are lacking. This study was to identify lipid metabolic markers for persistent MSMP. Lipidomic profiling of 807 lipid species was performed on serum samples of 536 participants from a cohort study. MSMP was measured by a questionnaire and defined as painful sites ≥4. Persistent MSMP was defined as having MSMP at every visit. Logistic regression was used with adjustment for potential confounders. The Benjamini–Hochberg method was used to control for multiple testing. A total of 530 samples with 807 lipid metabolites passed quality control. Mean age at baseline was 61.54 ± 6.57 years and 50% were females. In total, 112 (21%) of the participants had persistent MSMP. Persistent MSMP was significantly associated with lower levels of monohexosylceramide (HexCer)(d18:1/22:0 and d18:1/24:0), acylcarnitine (AC)(26:0) and lysophosphatidylcholine (LPC)(18:1 [sn1], 18:2 [sn1], 18:2 [sn2], and 15-MHDA[sn1] [104_sn1]) after controlling for multiple testing. After adjustment for age, sex, body mass index, comorbidities, and physical activity, HexCer(d18:1/22:0 and d18:1/24:0) and LPC(15-MHDA [sn1] [104_sn1]) were significantly associated with persistent MSMP [Odds Ratio (OR) ranging from 0.25–0.36]. Two lipid classes—HexCer and LPC—were negatively associated with persistent MSMP after adjustment for covariates (OR = 0.22 and 0.27, respectively). This study identified three novel lipid signatures of persistent MSMP, suggesting that lipid metabolism is involved in the pathogenesis of persistent pain.

1. Introduction

Musculoskeletal pain is the most common complaint in the general population and the leading cause of years lost to disability [1,2]. An estimated prevalence of 20% to 40% was reported globally with foreseen increasing individual and societal burden in the next decades [3,4]. A clinically relevant entity of multisite musculoskeletal pain (MSMP) has been defined, as musculoskeletal pain rarely occurs at single site [5]. More detrimental health outcomes associated with MSMP have been reported, such as health-related quality of life, fractures and sleep disturbance [6,7,8,9,10]. However, the initiation and persistence of MSMP is far from being fully understood.
Alteration of metabolic pathways has been suggested to be involved in the pathogenesis of musculoskeletal pain [11,12]. Metabolites are intermediates and end products of cellular regulatory processes and have critical roles in metabolic pathway regulation and signal transductions [11,13,14]. Metabolomics analysis has been used to identify and quantify metabolites, some of which are found to be implicated in the pathogenesis of musculoskeletal pain [15].
It has been suggested that endogenous lipid species as mediators have essential roles in pain severity and response [16,17,18,19,20]. Our recent study identified and replicated one lipid (i.e., sphingomyelin (SM) C18:1) associated with the presence of MSMP in two independent cohorts [21]. In addition, two proinflammatory lipid compounds in association with the presence of MSMP were also identified [22]. This may reflect that dysfunction in lipid metabolism is involved in the MSMP initiation. However, no longitudinal study has examined the roles of lipid mediators in MSMP persistence. Therefore, this study aimed to identify lipid signatures associated with persistent MSMP by using lipidomics.

2. Results

Among the 536 serum samples profiled, 530 passed quality control (QC) and were included in the subsequent analyses. There were 195, 201 and 215 participants reporting MSMP at 2.6-year (37%), 5.1-year (38%) and 10.7-year (41%) follow-ups, respectively. In total, 112 participants had persistent MSMP (21%) and 418 had non-persistent MSMP (79%). The persistent MSMP group had more females (67% vs. 46%), and was less physically active (7234.72 ± 3009.63 vs. 8034.60 ± 3299.35 steps per day) (Table 1) than the non-persistent MSMP group. The persistent MSMP group had higher prevalence of osteoarthritis (OA) (65% vs. 30%) and emphysema (7% vs. 2%) than the non-persistent MSMP group. There was no difference in age, body mass index (BMI), and presence of rheumatoid arthritis (RA) or diabetes between the two groups (Table 1).

2.1. Lipid Markers and MSMP

In total, 850 ‘lipid measures’ representing 807 lipid species from 49 classes were quantified in all 530 serum samples and passed QC. A total of 53 lipid species and 5 lipid classes were found to be associated with MSMP with p < 0.05 (Supplementary Tables S1 and S2). Lipid species including SM(38:3) (a), SM(40:4), monohexosylceramide (HexCer) (d18:1/24:0) and acylcarnitine (AC) (26:0) were significantly associated with MSMP at 2.6-year follow-up after controlling multiple testing, but these associations became non-significant after adjusting for confounding factors (Table 2). The HexCer class was significantly associated with MSMP at 2.6-year follow-up, but this significant association did not hold after adjusting for confounding factors (Table 2). The ratio of lysophosphatidylcholine (LPC) to phosphatidylcholine (PC) was negatively associated with MSMP at 2.6-year follow-up (odds ratio (OR) = 0.33, 95% confidence interval (CI): 0.13, 0.85), but this association became non-significant after adjustment for confounding factors (OR = 0.74, 95%CI: 0.22, 2.45).

2.2. Lipid Markers for Persistent MSMP

In total, 107 lipid species were found to be associated with persistent MSMP with p < 0.05 (Supplementary Table S3). After controlling multiple testing, 7 lipid species, i.e., HexCer (d18:1/22:0 and d18:1/24:0), AC 26:0 and LPC (18:1 [sn1], 18:2 [sn1], 18:2 [sn2] and 15-MHDA [sn1] [104_sn1]), were found to be negatively associated with persistent MSMP, that is, participants with persistent MSMP had lower levels of these lipid species compared to those without persistent MSMP (Figure 1). Per log nM decrease in these lipid species was associated with a higher risk of persistent MSMP (Table 3). The significances remained for HexCer d18:1/22:0 and d18:1/24:0 and LPC 15-MHDA [sn1] [104_sn1] after adjustment for age, sex, BMI, physical activity and presence of any comorbidities (OR ranged from 0.25 to 0.36, Table 3). Seven lipid classes were associated with persistent MSMP with p < 0.05 (Supplementary Table S4). After controlling multiple testing, 3 lipid classes, HexCer, LPC, and lysoalkylphosphatidylcholine (LPC(O)), were negatively associated with persistent MSMP (Figure 2). Per log nM decrease in these lipid classes was also associated with a higher risk of persistent MSMP (Table 4). The significances remained for HexCer and LPC after adjustment for confounding factors (OR = 0.22 and 0.27, respectively, Table 4). The ratio of LPC to PC was negatively associated with persistent MSMP (OR = 0.14, 95%CI: 0.05, 0.44), but this association became non-significant after adjustment for confounding factors (OR = 0.32, 95%CI: 0.08, 1.30).

3. Discussion

This study is the first to identify that circulating serum levels of lipid markers are associated with persistent multisite musculoskeletal pain using lipidomics. We found that participants with persistent MSMP had lower levels of HexCer (d18:1/24:0 and d18:1/22:0), AC (26:0) and LPC (18:2 [sn2], 18:2 [sn1], 18:1 [sn1] and 15-MHDA [sn1] [104_sn1] compared to those with non-persistent MSMP. Decreased levels of three of these lipid species (i.e., HexCer d18:1/24:0 and d18:1/22:0, and LPC 15-MHDA [sn1] [104_sn1]) were significantly associated with a higher risk of persistent MSMP after controlling covariates and multiple testing. Furthermore, decreased levels of two lipid classes, HexCer and LPC, were significantly associated with a higher risk of persistent MSMP after controlling covariates and multiple testing. These results suggest that dysregulation of lipid metabolism is implicated in the persistence of pain.
Lipid mediators have been revealed to play bilateral roles in metabolic pathways and signaling transduction of pain [16]. However, only a few metabolomic profiling studies on musculoskeletal pain have been performed, all of which have been cross-sectional. Livshits et al. [23] identified that epiandrosterone sulfate was inversely associated with chronic widespread musculoskeletal pain in two cohorts, but the association did not hold after adjustment for fat mass index. In a case-control study including 22 fibromyalgia patients and 21 healthy controls, Caboni et al. [24] found an over-representation of LPC (14:0/0:0) and LPC (16:0/0:0) compounds in the group of fibromyalgia. There are, to date, two studies from our group identifying MSMP-associated metabolites. We found that two bioactive proinflammatory lipid compounds, LPC (26:0 and 28:1), were positively associated with MSMP using the extreme phenotype sampling strategy [22]. More recently, we identified and replicated the association between an elevated SM C18:1 level and the presence of MSMP in two independent cohorts using different criteria of MSMP [21]. In the current study, we observed that lipid species and class including SM(38:3) (a), SM(40:4), HexCer(d18:1/24:0) and AC(26:0) and the HexCer class were significantly associated with MSMP at 2.6-year follow-up after controlling multiple testing, but these associations became non-significant after adjusting for confounding factors. The discrepancy might be attributed to differences in studied population, MSMP definition and number of lipids included. The current study extended prior studies from cross-sectional to longitudinal analyses and found that the decreased serum levels of predominant lipid species (i.e., HexCer d18:1/22:0 and d18:1/24:0, and LPC 15-MHDA [sn1] [104_sn1]) and two lipid classes (i.e., HexCer and LPC) were associated with increased risk of persistent MSMP. Collectively, our findings together with previous studies indicate that dysfunction in lipid metabolism is not only implicated in the initiation of MSMP but also its persistence. However, differences in cross-sectional and longitudinal findings in this study suggest that lipids involved in the initiation and persistence of MSMP are different.
There are very limited studies that have examined the species and class of HexCex in pain conditions. Our finding that the level of HexCex was decreased in persistent MSMP is supported by a preclinical study [25] which reported a decreased level of HexCer in cancer-induced bone pain, which was characterized by the formation of peripheral neuropathic features at the site of bone tumor, and central neuropathic pathology in the dorsal horn and spinal glia. The HexCer class is one of the complex glycosphingolipids and abundant in biological membranes, particularly in the central nervous system. HexCer is generated from the central metabolite of sphingolipid metabolism (i.e., ceramide) by glycosylated synthase, and plays roles in bioactive pathways and signaling transductions [26]. The HexCer class includes glucosylceramide (GlcCer) and galactosylceramide (GalCer), which serve as precursors for the biosynthesis of more complex glycosphingolipids, such as gangliosides [27,28]. Gangliosides have roles in transferring nociceptive information from the periphery to the central nervous system (central sensitization) through the activation of neurotransmitter receptors or ion channels, such as transient receptor potential vanilloid type 1 (TRPV1) [29]. In addition, sphingosine synthesized from ceramide by ceramidase is a bioactive precursor of sphingosine 1-phosphate (S1P) [30,31], and the dysregulation of S1P pathway contributes to the establishment of central sensitization, which is a key step of developing chronic pain [32]. Although speculative, it is possible that the overactivation of ganglioside and S1P pathways might be implicated in persistent MSMP. The decreased level of the HexCer class in persistent MSMP may be directly due to the overactivation of the ganglioside pathway, resulting in overproduction of gangliosides. Alternatively, the overactivation of S1P pathway results in overproduction of sphingosine from ceramide, which may indirectly reduce the synthesis of HexCer.
The current study found that a lower level of LPC 15-MHDA [sn1] [104_sn1] was associated with persistent MSMP, which was contrary to prior studies on fibromyalgia and neuropathic pain. Hung et al. [33] reported that an increased level of LPC 16:0 in fibromyalgia patients modestly correlated with pain symptoms, and hyperalgesia in the mouse model. Rimola et al. [34] also reported elevated levels of LPC 16:0 and 18:1 in oxaliplatin-induced acute peripheral pain. This discrepancy may reflect different roles of lipid mediators in pain induction and maintenance, as numerous lipid mediators, including LPC, are second messengers and intermediates in lipid metabolism and have various roles in pathways of pain induction and maintenance [16,35,36]. LPC is a subgroup of the lysophospholipid family and mainly derived from PC by phospholipase A2 (PLA2) [37]. LPC, as a proalgesic mediator, induces pain by increasing nitric oxide synthase in dorsal root ganglion (DRG) or activating the acid-sensing ion channel 3 [33,38]. LPC can also be hydrolyzed by autotaxin (ATX), a secreted exoenzyme with lysophospholipase D activity, to generate LPA, a compound that has been reported to be profoundly involved in the initiation and maintenance of chronic pain [17,39]. LPA mediates the activation of ion channels such as TRPV1 in DRG neurons to induce central sensitization [40]. Through the activation of G protein-coupled receptors, LPA also mediates the demyelination of central neurons, which is associated with the development of chronic pain [41,42]. The activation of ATX-LPA axis signaling pathway is likely involved in the maintenance of pain, which could have led to the decreased level of LPC class in persistent MSMP patients observed in our study. Future studies on serum LPA level and ATX activity in persistent MSMP are needed to provide more information.
The ratio of LPC to PC has been reported to be associated with inflammation in severe OA and RA [43,44]. Consistent with one previous cross-sectional study [22], no association between the ratio of LPC to PC and MSMP was observed in the current study. Furthermore, our longitudinal analysis found no associations between ratio of LPC to PC and persistent MSMP. On the contrary, Zhang et al. [45] reported the increased ratio of LPC to PC, driven by significantly higher LPC and lower PC levels in knee OA patients than in healthy controls, as an important marker for predicting advanced knee OA. This may suggest that persistent MSMP may be largely driven by central sensitization other than inflammation.
There are several limitations in this study. First, the majority of lipid species were intermediate metabolites. The key downstream products, such as LPA, were not quantified in this study. However, this study is the first to explore lipidomic profiles in persistent MSMP. The identified lipid signatures of persistent MSMP shed light on underlying pathogenesis of persistent MSMP and provide robust data for future studies on its biological pathways. Second, lipidomics was only performed on serum samples at a 2.6-year follow-up, so we are unable to examine whether variations of lipid species from 2.6 to 10.7 years are associated with persistent MSMP. Third, this study included 536 subsamples with MSMP data at each follow-up time-point and blood samples available from the Tasmanian Older Adult Cohort study (TASOAC) cohort. However, there were no differences in the characteristics between those included in this study and the whole cohort, suggesting minimization of the bias. Fourth, pain was assessed via a simple questionnaire (yes/no); therefore, we are unable to investigate whether lipids are associated with other pain features such as pain duration and frequency in this study. Fifth, given the nature of observational study, we are unable to determine whether the way to modify lipids level by diet, hormonal regulation and exercise management can affect MSMP; thus, future trials are needed to examine whether interventions to modify lipid levels can affect MSMP.

4. Materials and Methods

4.1. Study Participants

The study participants were derived from the TASOAC, which is a prospective, population-based cohort study. The TASOAC study recruited 1100 participants aged 50–80 years who were randomly selected from the electoral roll in southern Tasmania (population n = 229,000), Australia, between 2002–2004. A total of 1099 participants had their baseline examinations including questionnaire, general interview, clinical assessment and blood tests. Participants were subsequently followed up at 2.6, 5.1 and 10.7 years. Fasting blood samples collected at the 2.6-year follow-up were used for the lipidomics assay due to depletion of baseline blood samples. A total of 536 participants with MSMP data at each follow-up time point and blood samples available were included in the study. The TASOAC study was approved by the Southern Tasmanian Health and Medical Human Research Ethics Committee (Ref. no: H0006488) and written informed consent was obtained from all participants.

4.2. Demographic and Medical Information Collection

Date of birth and sex were self-reported and age at the recruitment was calculated. Height and weight were measured at 2.6-year follow-up and BMI (kg/m2) was calculated. Physical activity at 2.6-year follow-up was measured by steps per day for 7 consecutive days using a pedometer (Omron HJ-003 and HJ-102; Omron Healthcare, Kyoto, Japan). Comorbidities including diabetes, heart attack, hypertension, thrombosis, asthma, bronchitis/emphysema, hyperthyroidism, hypothyroidism and RA at 2.6-year follow-up were self-reported. OA in the neck, back, hands, shoulders, hips, knees and feet at 2.6-year follow-up was diagnosed by physicians.

4.3. MSMP Assessment

Pain at any of the seven anatomical sites including neck, shoulder, hand, back, hip, knee and foot was self-reported using a pain questionnaire, and total number of painful sites was calculated. In accordance with the 2016 widespread pain definition (WP2016) [46], MSMP was defined as having ≥4 painful sites. Persistent MSMP was then defined as having MSMP at 2.6-, 5.1- and 10.7-year follow-up, and non-persistent MSMP was defined as not having MSMP during at least one of the follow-ups [47].

4.4. Lipidomic Profiling

Blood samples were collected after at least 8 h fasting using the white top Greiner tubes with gel and left at room temperature for 10 min. The tubes were inverted a few times by hand to help activate the clotting process. Then, blood samples were centrifuged at 2500 rpm for 5 min, and the serum was then transferred into microcentrifuge tubes and stored at −80 °C until analysis. Serum samples were randomized, and lipids were extracted using the butanol/methanol method as described previously [48]. Targeted lipidomic analysis was performed on an Agilent 6495C QQQ mass spectrometer coupled with an Agilent 1290 series HPLC system and a ZORBAX eclipse plus C18 column (Agilent, Santa Clara, CA, USA) in positive and negative ion mode as described previously [48]. Technical quality controls (TQCs) and pooled serum QCs (PQCs) were used to monitor the overall performance of the liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and experimental issues during lipid extraction. NIST1950 samples representing the “population” lipidomic profile were used to facilitate future alignment with other studies. Chromatographic peaks for each lipid were integrated based on dynamic multiple reaction monitoring (dMRM) ion pairs and retention time using the MassHunter software (Agilent). Lipid concentrations were calculated by relating the area under the chromatogram for each lipid species to the corresponding internal standard. Correction factors were applied to adjust for different response factors where these were known. Mean and median coefficients of variation (CVs) of all QCs were ≤11.7%, and those of PQCs were 9.4% and 7.6%, respectively.

4.5. Statistical Analysis

Normality of distribution was tested with the Shapiro–Wilk test. Age, BMI, steps per day were compared with the Mann–Whitney U test. All lipid and lipid class concentrations were natural log transformed for normalization and compared with independent sample Student’s t-test. The ratio of LPC to PC was calculated using original concentrations of LPC and PC classes and then natural log transformed. Sex distribution and prevalence of comorbidities were compared with Chi-squared test or Fisher’s exact test wherever appropriate. The covariates included in multivariable analyses were age, sex, BMI, physical activity and presence of any comorbidities (OA, RA, emphysema, and diabetes). Age, sex, BMI and physical activity were correlated with MSMP or lipidomic metabolites, or both, based on pervious literature [48,49,50]. OA is the most common cause of musculoskeletal pain [51]. We also compared the difference in all 10 comorbidities between the MSMP and non-MSMP group, and found the prevalence of OA, RA, emphysema and diabetes was higher in the MSMP group. These four comorbidities have also been reported to be associated with lipids in the existing literature [52,53,54,55]. Therefore, the presence of any of these four comorbidities was included as a covariate in the multivariable analyses. Logistic regression was utilized to identify lipid species and classes associated with MSMP at 2.6-year follow-up and those associated with persistent MSMP, with adjustment for potential confounders including age, sex, BMI, physical activity and presence of any comorbidities. The Benjamini–Hochberg method [56] was used to control multiple testing for lipid and lipid class concentrations. For all other variables, the significance level was defined at α ≤ 0.05. All analyses were performed in R Studio with R version 3.6.3. Visualizations of the results were done with ggplot2 R package.

5. Conclusions

This study identified three novel lipid signatures of persistent MSMP, suggesting that lipid metabolism is involved in the pathogenesis of persistent pain.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/metabo12030206/s1, Table S1: Serum lipid species associated with MSMP at 2.6-year follow-up, Table S2: Serum lipid classes associated with MSMP at 2.6-year follow-up, Table S3: Serum lipid species associated with persistent MSMP, Table S4: Serum lipid classes associated with persistent MSMP.

Author Contributions

Conceptualization, F.P.; methodology, Y.L.S., P.J.M.; software, M.L.; validation, C.M., M.L. and J.T.; formal analysis, M.L.; investigation, J.T., G.J. and F.P.; resources, F.C., G.J. and F.P.; data curation, F.P.; writing—original draft preparation, C.M., M.L. and F.P.; writing—review and editing, C.M., M.L., J.T., G.Z., F.C., Y.L.S., P.J.M., G.J. and F.P.; visualization, M.L.; supervision, F.P.; project administration, F.C., G.J. and F.P.; funding acquisition, F.C., G.J. and F.P. All authors have read and agreed to the published version of the manuscript.

Funding

(NHMRC) project grant (302204) and Arthritis Australia (P0027184). J.T. is funded by the National Heart Foundation Fellowship; F.C. is funded by the NHMRC Leadership Fellowship; G.J. is funded by the NHMRC Practitioner Fellowship; F.P. is funded by the NHMRC Early Career Fellowship.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Southern Tasmanian Health and Medical Human Research Ethics Committee (reference number H0006488 and date of approval 24 August 2001).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Blyth, F.M.; Noguchi, N. Chronic musculoskeletal pain and its impact on older people. Best Pract. Res. Clin. Rheumatol. 2017, 31, 160–168. [Google Scholar] [CrossRef] [PubMed]
  2. Cohen, S.P.; Vase, L.; Hooten, W.M. Chronic pain: An update on burden, best practices, and new advances. Lancet 2021, 397, 2082–2097. [Google Scholar] [CrossRef]
  3. Blyth, F.M.; Briggs, A.M.; Schneider, C.H.; Hoy, D.G.; March, L.M. The Global Burden of Musculoskeletal Pain-Where to From Here? Am. J. Public Health 2019, 109, 35–40. [Google Scholar] [CrossRef] [PubMed]
  4. McMahon, S.B.; Dargan, P.; Lanas, A.; Wiffen, P. The burden of musculoskeletal pain and the role of topical non-steroidal anti-inflammatory drugs (NSAIDs) in its treatment. Ten underpinning statements from a global pain faculty. Curr. Med Res. Opin. 2021, 37, 287–292. [Google Scholar] [CrossRef] [PubMed]
  5. Carnes, D.; Parsons, S.; Ashby, D.; Breen, A.; Foster, N.E.; Pincus, T.; Vogel, S.; Underwood, M. Chronic musculoskeletal pain rarely presents in a single body site: Results from a UK population study. Rheumatology 2007, 46, 1168–1170. [Google Scholar] [CrossRef] [Green Version]
  6. Lacey, R.J.; Belcher, J.; Rathod, T.; Wilkie, R.; Thomas, E.; McBeth, J. Pain at multiple body sites and health-related quality of life in older adults: Results from the North Staffordshire Osteoarthritis Project. Rheumatology 2014, 53, 2071–2079. [Google Scholar] [CrossRef] [Green Version]
  7. Pan, F.; Tian, J.; Cicuttini, F.; Jones, G. Sleep Disturbance and Its Association with Pain Severity and Multisite Pain: A Prospective 10.7-Year Study. Pain Ther. 2020, 9, 751–763. [Google Scholar] [CrossRef]
  8. Pan, F.; Tian, J.; Aitken, D.; Cicuttini, F.; Jones, G. Pain at multiple sites is associated with prevalent and incident fractures in older adults. J. Bone Miner. Res. 2019, 34, 2012–2018. [Google Scholar] [CrossRef]
  9. Welsh, V.K.; Clarson, L.E.; Mallen, C.D.; McBeth, J. Multisite pain and self-reported falls in older people: Systematic review and meta-analysis. Arthritis Res. Ther. 2019, 21, 1–8. [Google Scholar] [CrossRef] [Green Version]
  10. Generaal, E.; Vogelzangs, N.; Penninx, B.W.J.H.; Dekker, J. Insomnia, Sleep Duration, Depressive Symptoms, and the Onset of Chronic Multisite Musculoskeletal Pain. Sleep 2016, 40, 1–10. [Google Scholar]
  11. Aroke, E.N.; Powell-Roach, K.L. The Metabolomics of Chronic Pain Conditions: A Systematic Review. Biol. Res. Nurs. 2020, 22, 458–471. [Google Scholar] [CrossRef] [PubMed]
  12. Hackshaw, K.V.; Aykas, D.P.; Sigurdson, G.T.; Plans, M.; Madiai, F.; Yu, L.; Buffington, C.A.T.; Giusti, M.M.; Rodriguez-Saona, L. Metabolic fingerprinting for diagnosis of fibromyalgia and other rheumatologic disorders. J. Biol. Chem. 2019, 294, 2555–2568. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Stockstill, K.; Doyle, T.M.; Yan, X.; Chen, Z.; Janes, K.; Little, J.W.; Braden, K.; Lauro, F.; Giancotti, L.A.; Harada, C.M.; et al. Dysregulation of sphingolipid metabolism contributes to bortezomib-induced neuropathic pain. J. Exp. Med. 2018, 215, 1301–1313. [Google Scholar] [CrossRef]
  14. Kaluarachchi, M.; Lewis, M.R.; Lindon, J.C. Standardized Protocols for MS-Based Metabolic Phenotyping. In Encyclopedia of Spectroscopy and Spectrometry, 3rd ed.; Lindon, J.C., Tranter, G.E., Koppenaal, D.W., Eds.; Academic Press: Oxford, UK, 2017; pp. 224–231. [Google Scholar]
  15. Teckchandani, S.; Nagana Gowda, G.A.; Raftery, D.; Curatolo, M. Metabolomics in chronic pain research. Eur. J. Pain 2021, 25, 313–326. [Google Scholar] [CrossRef] [PubMed]
  16. Piomelli, D.; Sasso, O. Peripheral gating of pain signals by endogenous lipid mediators. Nat. Neurosci. 2014, 17, 164–174. [Google Scholar] [CrossRef] [Green Version]
  17. Ueda, H. Pathogenic mechanisms of lipid mediator lysophosphatidic acid in chronic pain. Prog. Lipid Res. 2021, 81, 101079. [Google Scholar] [CrossRef] [PubMed]
  18. Osthues, T.; Sisignano, M. Oxidized Lipids in Persistent Pain States. Front. Pharmacol. 2019, 10. [Google Scholar] [CrossRef] [Green Version]
  19. Piomelli, D.; Hohmann, A.G.; Seybold, V.; Hammock, B.D. A Lipid Gate for the Peripheral Control of Pain. J. Neurosci. 2014, 34, 15184–15191. [Google Scholar] [CrossRef]
  20. Pousinis, P.; Gowler, P.R.W.; Burston, J.J.; Ortori, C.A.; Chapman, V.; Barrett, D.A. Lipidomic identification of plasma lipids associated with pain behaviour and pathology in a mouse model of osteoarthritis. Metabolomics 2020, 16, 32. [Google Scholar] [CrossRef] [Green Version]
  21. Pan, F.; Liu, M.; Randell, E.W.; Rahman, P.; Jones, G.; Zhai, G. Sphingomyelin is involved in multisite musculoskeletal pain: Evidence from metabolomic analysis in 2 independent cohorts. Pain 2021, 162, 1876–1881. [Google Scholar] [CrossRef]
  22. Liu, M.; Xie, Z.; Costello, C.A.; Zhang, W.; Chen, L.; Qi, D.; Furey, A.; Randell, E.W.; Rahman, P.; Zhai, G. Metabolomic analysis coupled with extreme phenotype sampling identified that lysophosphatidylcholines are associated with multisite musculoskeletal pain. Pain 2021, 162, 600–608. [Google Scholar] [CrossRef] [PubMed]
  23. Livshits, G.; Macgregor, A.J.; Gieger, C.; Malkin, I.; Moayyeri, A.; Grallert, H.; Emeny, R.T.; Spector, T.; Kastenmüller, G.; Williams, F.M.K. An omics investigation into chronic widespread musculoskeletal pain reveals epiandrosterone sulfate as a potential biomarker. Pain 2015, 156, 1845–1851. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Caboni, P.; Liori, B.; Kumar, A.; Santoru, M.L.; Asthana, S.; Pieroni, E.; Fais, A.; Era, B.; Cacace, E.; Ruggiero, V.; et al. Metabolomics Analysis and Modeling Suggest a Lysophosphocholines-PAF Receptor Interaction in Fibromyalgia. PLoS ONE 2014, 9, e107626. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Grenald, S.A.; Doyle, T.M.; Zhang, H.; Slosky, L.M.; Chen, Z.; Largent-Milnes, T.M.; Spiegel, S.; Vanderah, T.W.; Salvemini, D. Targeting the S1P/S1PR1 axis mitigates cancer-induced bone pain and neuroinflammation. Pain 2017, 158, 1733–1742. [Google Scholar] [CrossRef] [PubMed]
  26. Morita, M.; Watanabe, S.; Oyama, M.; Iwai, T.; Tanabe, M. Glycosphingolipid Biosynthesis Pathway in the Spinal Cord and Dorsal Root Ganglia During Inflammatory Pain: Early and Late Changes in Expression Patterns of Glycosyltransferase Genes. Neuroscience 2020, 428, 217–227. [Google Scholar] [CrossRef]
  27. Farwanah, H.; Kolter, T. Lipidomics of Glycosphingolipids. Metabolites 2012, 2, 134–164. [Google Scholar] [CrossRef] [Green Version]
  28. Jatooratthawichot, P.; Talabnin, C.; Ngiwsara, L.; Rustam, Y.H.; Svasti, J.; Reid, G.E.; Ketudat Cairns, J.R. Effect of Expression of Human Glucosylceramidase 2 Isoforms on Lipid Profiles in COS-7 Cells. Metabolites 2020, 10, 488. [Google Scholar] [CrossRef]
  29. Sántha, P.; Dobos, I.; Kis, G.; Jancsó, G. Role of Gangliosides in Peripheral Pain Mechanisms. Int. J. Mol. Sci. 2020, 21, 1005. [Google Scholar] [CrossRef] [Green Version]
  30. Salvemini, D.; Doyle, T.; Kress, M.; Nicol, G. Therapeutic targeting of the ceramide-to-sphingosine 1-phosphate pathway in pain. Trends Pharmacol. Sci. 2013, 34, 110–118. [Google Scholar] [CrossRef]
  31. Fahy, E.; Subramaniam, S.; Brown, H.A.; Glass, C.K.; Merrill, A.H., Jr.; Murphy, R.C.; Raetz, C.R.H.; Russell, D.W.; Seyama, Y.; Shaw, W.; et al. A comprehensive classification system for lipids1 J. Lipid Res. 2005, 46, 839–861. [Google Scholar] [CrossRef] [Green Version]
  32. Squillace, S.; Spiegel, S.; Salvemini, D. Targeting the sphingosine-1-phosphate axis for developing non-narcotic pain therapeutics. Trends Pharmacol. Sci. 2020, 41, 851–867. [Google Scholar] [CrossRef] [PubMed]
  33. Hung, C.-H.; Lee, C.-H.; Tsai, M.-H.; Chen, C.-H.; Lin, H.-F.; Hsu, C.-Y.; Lai, C.-L.; Chen, C.-C. Activation of acid-sensing ion channel 3 by lysophosphatidylcholine 16:0 mediates psychological stress-induced fibromyalgia-like pain. Ann. Rheum. Dis. 2020, 79, 1644–1656. [Google Scholar] [CrossRef] [PubMed]
  34. Rimola, V.; Hahnefeld, L.; Zhao, J.; Jiang, C.; Angioni, C.; Schreiber, Y.; Osthues, T.; Pierre, S.; Geisslinger, G.; Ji, R.R. Lysophospholipids Contribute to Oxaliplatin-Induced Acute Peripheral Pain. J. Neurosci. 2020, 40, 9519–9532. [Google Scholar] [CrossRef] [PubMed]
  35. Tigyi, G. Lipids: LPA activates TRPV1--and it hurts. Nat. Chem. Biol. 2011, 8, 22–23. [Google Scholar] [CrossRef] [Green Version]
  36. Liu, P.; Zhu, W.; Chen, C.; Yan, B.; Zhu, L.; Chen, X.; Peng, C. The mechanisms of lysophosphatidylcholine in the development of diseases. Life Sci. 2020, 247, 117443. [Google Scholar] [CrossRef]
  37. Law, S.-H.; Chan, M.-L.; Marathe, G.K.; Parveen, F.; Chen, C.-H.; Ke, L.-Y. An Updated Review of Lysophosphatidylcholine Metabolism in Human Diseases. Int. J. Mol. Sci. 2019, 20, 1149. [Google Scholar] [CrossRef] [Green Version]
  38. Wang, H.-Y.; Tsai, Y.-J.; Chen, S.-H.; Lin, C.-T.; Lue, J.-H. Lysophosphatidylcholine causes neuropathic pain via the increase of neuronal nitric oxide synthase in the dorsal root ganglion and cuneate nucleus. Pharmacol. Biochem. Behav. 2013, 106, 47–56. [Google Scholar] [CrossRef]
  39. Kuwajima, K.; Sumitani, M.; Kurano, M.; Kano, K.; Nishikawa, M.; Uranbileg, B.; Tsuchida, R.; Ogata, T.; Aoki, J.; Yatomi, Y. Lysophosphatidic acid is associated with neuropathic pain intensity in humans: An exploratory study. PLoS ONE 2018, 13, e0207310. [Google Scholar] [CrossRef]
  40. Langeslag, M.; Kress, M. The ceramide-S1P pathway as a druggable target to alleviate peripheral neuropathic pain. Expert Opin. Ther. Targets 2020, 24, 869–884. [Google Scholar] [CrossRef]
  41. Oude Elferink, R.P.J.; Bolier, R.; Beuers, U.H. Lysophosphatidic acid and signaling in sensory neurons. Biochim. Et Biophys. Acta (BBA)—Mol. Cell Biol. Lipids 2015, 1851, 61–65. [Google Scholar] [CrossRef]
  42. O’Brien, M.S.; Philpott, H.T.A.; McDougall, J.J. Targeting the Nav1.8 ion channel engenders sex-specific responses in lysophosphatidic acid–induced joint neuropathy. Pain 2019, 160, 269–278. [Google Scholar] [CrossRef] [PubMed]
  43. Brouwers, H.; von Hegedus, J.; Toes, R.; Kloppenburg, M.; Ioan-Facsinay, A. Lipid mediators of inflammation in rheumatoid arthritis and osteoarthritis. Best Pract. Res. Clin. Rheumatol. 2015, 29, 741–755. [Google Scholar] [CrossRef]
  44. Tootsi, K.; Vilba, K.; Märtson, A.; Kals, J.; Paapstel, K.; Zilmer, M. Metabolomic Signature of Amino Acids, Biogenic Amines and Lipids in Blood Serum of Patients with Severe Osteoarthritis. Metabolites 2020, 10, 323. [Google Scholar] [CrossRef] [PubMed]
  45. Zhang, W.; Sun, G.; Aitken, D.; Likhodii, S.; Liu, M.; Martin, G.; Furey, A.; Randell, E.; Rahman, P.; Jones, G.; et al. Lysophosphatidylcholines to phosphatidylcholines ratio predicts advanced knee osteoarthritis. Rheumatology 2016, 55, 1566–1574. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Wolfe, F.; Clauw, D.J.; Fitzcharles, M.A.; Goldenberg, D.L.; Hauser, W.; Katz, R.L.; Mease, P.J.; Russell, A.S.; Russell, I.J.; Walitt, B. 2016 Revisions to the 2010/2011 fibromyalgia diagnostic criteria. Semin. Arthritis Rheum 2016, 46, 319–329. [Google Scholar] [CrossRef]
  47. Gureje, O.; Von Korff, M.; Simon, G.E.; Gater, R. Persistent pain and well-being: A World Health Organization Study in Primary Care. Jama 1998, 280, 147–151. [Google Scholar] [CrossRef]
  48. Beyene, H.B.; Olshansky, G.; Smith, A.A.T.; Giles, C.; Huynh, K.; Cinel, M.; Mellett, N.A.; Cadby, G.; Hung, J.; Hui, J.; et al. High-coverage plasma lipidomics reveals novel sex-specific lipidomic fingerprints of age and BMI: Evidence from two large population cohort studies. PLoS Biol. 2020, 18, e3000870. [Google Scholar]
  49. Pan, F.; Byrne, K.S.; Ramakrishnan, R.; Ferreira, M.; Dwyer, T.; Jones, G. Association between musculoskeletal pain at multiple sites and objectively measured physical activity and work capacity: Results from UK Biobank study. J. Sci. Med. Sport 2019, 22, 444–449. [Google Scholar] [CrossRef]
  50. Pan, F.; Laslett, L.; Blizzard, L.; Cicuttini, F.; Winzenberg, T.; Ding, C.; Jones, G. Associations Between Fat Mass and Multisite Pain: A Five-Year Longitudinal Study. Arthritis Care Res. 2017, 69, 509–516. [Google Scholar] [CrossRef] [Green Version]
  51. Breivik, H.; Collett, B.; Ventafridda, V.; Cohen, R.; Gallacher, D. Survey of chronic pain in Europe: Prevalence, impact on daily life, and treatment. Eur. J. Pain 2006, 10, 287–333. [Google Scholar] [CrossRef]
  52. Zhai, G. Alteration of Metabolic Pathways in Osteoarthritis. Metabolites 2019, 9, 11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Miltenberger-Miltenyi, G.; Cruz-Machado, A.R.; Saville, J.; Conceição, V.A.; Calado, Â.; Lopes, I.; Fuller, M.; Fonseca, J.E. Increased monohexosylceramide levels in the serum of established rheumatoid arthritis patients. Rheumatology 2019, 59, 2085–2089. [Google Scholar] [CrossRef] [PubMed]
  54. Xuan, L.; Han, F.; Gong, L.; Lv, Y.; Wan, Z.; Liu, H.; Zhang, D.; Jia, Y.; Yang, S.; Ren, L.; et al. Association between chronic obstructive pulmonary disease and serum lipid levels: A meta-analysis. Lipids Health Dis. 2018, 17, 263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Palmer, M.K.; Toth, P.P. Trends in Lipids, Obesity, Metabolic Syndrome, and Diabetes Mellitus in the United States: An NHANES Analysis (2003–2004 to 2013–2014). Obesity 2019, 27, 309–314. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 1995, 57, 289–300. [Google Scholar] [CrossRef]
Metabolites 12 00206 g001 550
Figure 1. The original concentrations of serum lipid species in persistent MSMP and non-persistent MSMP groups. MSMP: multisite musculoskeletal pain; HexCer: monohexosylceramide; AC: acylcarnitine; LPC: lysophosphatidylcholine; MHDA: methylhexadecanoic acid. p values were obtained by independent sample Student’s t-test.
Figure 1. The original concentrations of serum lipid species in persistent MSMP and non-persistent MSMP groups. MSMP: multisite musculoskeletal pain; HexCer: monohexosylceramide; AC: acylcarnitine; LPC: lysophosphatidylcholine; MHDA: methylhexadecanoic acid. p values were obtained by independent sample Student’s t-test.
Metabolites 12 00206 g001
Metabolites 12 00206 g002 550
Figure 2. The original concentrations of lipid classes in persistent MSMP and non-MSMP groups. MSMP: multisite musculoskeletal pain; HexCer: monohexosylceramide; LPC: lysophosphatidylcholine; LPC(O): lysoalkylphosphatidylcholine. p values were obtained by independent sample Student’s t-test.
Figure 2. The original concentrations of lipid classes in persistent MSMP and non-MSMP groups. MSMP: multisite musculoskeletal pain; HexCer: monohexosylceramide; LPC: lysophosphatidylcholine; LPC(O): lysoalkylphosphatidylcholine. p values were obtained by independent sample Student’s t-test.
Metabolites 12 00206 g002
Table
Table 1. Participants’ characteristics *.
Table 1. Participants’ characteristics *.
Total
(n = 530)		Persistent MSMP
(n = 112)		Non-Persistent MSMP
(n = 418)		p Value
Age (years)61.54 ± 6.5761.71 ± 6.6561.49 ± 6.560.80
BMI (kg/m2)27.73 ± 4.5428.13 ± 4.7327.62 ± 4.480.31
Females (%)506746<0.001
Physical activity (steps per day)7865.21 ± 3253.787234.72 ± 3009.638034.60 ± 3299.350.04
Comorbidities (%)407031<0.001
OA (%)376530<0.001
RA (%)3530.23
Emphysema (%)3720.01
Diabetes (%)2410.23
MSMP: multisite musculoskeletal pain; BMI: body mass index; OA: osteoarthritis; RA: rheumatoid arthritis. * Values are the mean ± SD unless indicated otherwise. p values were obtained by Mann–Whitney U test, Chi-squared test or Fisher’s exact test wherever appropriate.
Table
Table 2. Lipid species and class associated with MSMP at 2.6-year follow-up.
Table 2. Lipid species and class associated with MSMP at 2.6-year follow-up.
UnivariableMultivariable *
p ValueOR2.5% CI97.5% CIp ValueOR2.5% CI97.5% CI
Lipid species
SM(38:3) (a)3.30 × 10−44.451.9910.196.12 × 10−22.610.967.23
HexCer(d18:1/24:0)3.60 × 10−30.380.200.737.50 × 10−20.510.251.07
SM(40:4)3.91 × 10−34.791.6714.041.59 × 10−12.550.709.44
AC(26:0)5.27 × 10−30.420.230.776.84 × 10−10.860.411.80
Lipid class
HexCer2.21 × 10−20.440.210.881.21 × 10−10.530.241.18
MSMP: multisite musculoskeletal pain; OR: odds ratio; CI: confidence interval; HexCer: monohexosylceramide; SM: sphingomyelin; AC: acylcarnitine. p values were obtained by univariable or multivariable logistic regression. * Adjusted for age, sex, body mass index, physical activity and presence of at least one of the four comorbidities.
Table
Table 3. Lipid species associated with persistent MSMP.
Table 3. Lipid species associated with persistent MSMP.
Univariable Multivariable *
p ValueOR2.5% CI97.5% CIp ValueOR2.5% CI97.5% CI
HexCer(d18:1/22:0)3.46 × 10−40.260.120.547.71 × 10−30.330.140.74
HexCer(d18:1/24:0)4.55 × 10−50.200.090.432.15 × 10−30.250.100.60
LPC(18:1) [sn1]6.60 × 10−40.230.090.531.46 × 10−20.310.120.78
LPC(15-MHDA) [sn1] [104_sn1]6.48 × 10−40.320.160.617.95 × 10−30.360.170.76
LPC(18:2) [sn1]6.53 × 10−40.280.130.584.70 × 10−20.410.170.98
LPC(18:2) [sn2]4.06 × 10−40.210.090.501.91 × 10−20.300.110.82
AC(26:0)2.19 × 10−40.250.120.525.01 × 10−20.410.170.99
MSMP: multisite musculoskeletal pain; OR: odds ratio; CI: confidence interval; HexCer: monohexosylceramide; LPC: lysophosphatidylcholine; MHDA: methylhexadecanoic acid; AC: acylcarnitine. p values were obtained by univariable or multivariable logistic regression. * Adjusted for age, sex, body mass index, physical activity and presence of at least one of the four comorbidities.
Table
Table 4. Lipid classes associated with persistent MSMP.
Table 4. Lipid classes associated with persistent MSMP.
UnivariableMultivariable *
p ValueOR2.5% CI97.5% CIp ValueOR2.5% CI97.5% CI
HexCer2.23 × 10−40.200.080.462.02 × 10−30.220.080.57
LPC1.02 × 10−30.180.060.492.50 × 10−20.270.080.84
LPC(O)2.35 × 10−30.220.080.586.88 × 10−20.360.121.07
MSMP: multisite musculoskeletal pain; OR: odds ratio; CI: confidence interval; HexCer: monohexosylceramide; LPC: lysophosphatidylcholine; LPC(O): lysoalkylphosphatidylcholine. p values were obtained by univariable or multivariable logistic regression. * Adjusted for age, sex, body mass index, physical activity and presence of at least one of the four comorbidities.
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Ma, C.; Liu, M.; Tian, J.; Zhai, G.; Cicuttini, F.; Schooneveldt, Y.L.; Meikle, P.J.; Jones, G.; Pan, F. Lipidomic Profiling Identifies Serum Lipids Associated with Persistent Multisite Musculoskeletal Pain. Metabolites 2022, 12, 206. https://doi.org/10.3390/metabo12030206

AMA Style

Ma C, Liu M, Tian J, Zhai G, Cicuttini F, Schooneveldt YL, Meikle PJ, Jones G, Pan F. Lipidomic Profiling Identifies Serum Lipids Associated with Persistent Multisite Musculoskeletal Pain. Metabolites. 2022; 12(3):206. https://doi.org/10.3390/metabo12030206

Chicago/Turabian Style

Ma, Canchen, Ming Liu, Jing Tian, Guangju Zhai, Flavia Cicuttini, Yvette L. Schooneveldt, Peter J. Meikle, Graeme Jones, and Feng Pan. 2022. "Lipidomic Profiling Identifies Serum Lipids Associated with Persistent Multisite Musculoskeletal Pain" Metabolites 12, no. 3: 206. https://doi.org/10.3390/metabo12030206

APA Style

Ma, C., Liu, M., Tian, J., Zhai, G., Cicuttini, F., Schooneveldt, Y. L., Meikle, P. J., Jones, G., & Pan, F. (2022). Lipidomic Profiling Identifies Serum Lipids Associated with Persistent Multisite Musculoskeletal Pain. Metabolites, 12(3), 206. https://doi.org/10.3390/metabo12030206

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