Exploring Associations Between Metabolites and Symptoms of Fatigue, Depression and Pain in Women With Fibromyalgia

Abstract

Fibromyalgia (FM) is a chronic noncommunicable disorder characterized by a constellation of symptoms that include fatigue, depression and chronic pain. FM affects 2%–8% of the U.S. population, 2% of the global population, with 61%–90% of FM diagnoses attributed to women. Key causal factors leading to the development and severity of FM-related symptoms have not yet been identified. The purpose of this article is to report relationships among identified metabolites and levels of fatigue, depression, pain severity, and pain interference in a sample of 20 women with FM. In this secondary analysis, we conducted global metabolomic analysis and examined the data for relationships of metabolite levels with self-reported symptoms of fatigue, depression, pain severity, and pain interference. Results revealed six metabolites (6-deoxy-hexose; pantothenic acid; ergothioneine; l-carnitine; n-acetylserotonin; butyrobetaine) and their associated metabolic pathways such as carnitine synthesis, lipid oxidation, tryptophan metabolism, beta-alanine metabolism and pantothenic and Coenzyme-A biosynthesis that were either positively or inversely related to pain severity, pain interference, or both. The preliminary data presented suggest that metabolites representing energy, amino acid, or lipid classification may be associated with pain symptom severity and interference in women with FM. Future work will confirm these findings in a large, comparative cohort, targeting metabolites and metabolite pathways to better understand the relationships of metabolites and symptomology.

There is intense interest in better understanding the pathophysiology of fatigue, depression, and pain in the chronically ill patient population because these symptoms are common across disease states and adversely affect quality of life. Among chronic conditions of interest is fibromyalgia (FM), a chronic pain condition on the extreme end of the centralized pain spectrum. Reported to affect 2%–8% of the U.S. population and 2% of the global population with an estimated 61%–90% female predominance, FM is characterized by a constellation of symptoms that include fatigue, depression, chronic pain, non-refreshing sleep, and cognitive problems (Chinn et al., 2016; Wolfe et al., 2018). Despite research focusing on multiple biomarkers and biomarker signatures that may aid in the diagnosis, prognostication, and treatment of symptoms of fatigue, depression, and pain across chronic illness domains, key causal factors/mechanisms leading to the development of FM-related symptoms have not been identified (Chinn et al., 2016; Sluka & Clauw, 2016). This fact contributes to the challenge of developing objective measures to identify and confirm an FM diagnosis, without which there is little chance for adequate and appropriately applied treatment strategies. Without understanding the underlying biological mechanisms related to the development of the symptoms associated with FM, evidence-based research to help guide personalized and effective health strategies remains elusive.

At present, much of the driving force in the precision health movement has been due to innovations in “omics” research. Among them is metabolomics, a promising approach that has become an important phenotyping technique with the potential to identify upstream biological events and epigenetic changes, to explain pathophysiological mechanisms underlying a disease or syndrome, as well as to propose new therapeutic targets for alternative treatments (Mastrangelo & Barbas, 2017; Menzies et al., 2020) The value of examining metabolic markers in body fluids is that disease-specific molecules that are leaked or secreted into body fluids can be quantified (Nordström & Lewensohn, 2010). Discriminating metabolites can then be identified and relationships between metabolomic profiles and clinical variables such as fatigue, depression, or pain can be examined (Young et al., 2013). To date, there have been relatively few metabolomic studies focused on FM and, of these, even fewer have examined potential metabolomic and symptom associations in this patient population. Thus, the purpose of this secondary data analysis was to examine relationships among metabolites in circulation and self-reported symptoms in individuals with FM. This article focuses on revealing relationships among symptoms of FM and metabolites and supports the relationship of molecular measures and symptom severity, an important aspect of symptom science in chronic illness.

Material and Methods

Study Design and Participants

This study is a secondary analysis of data from women with FM (N = 20) that have been previously reported (Menzies et al., 2013, 2014) in a comparison of metabolites to women without FM. The parent studies were approved by the University’s Institutional Review Board (IRB). The parent study participant inclusion criteria were >18 years; female with a current diagnosis of FM based on the 1990 American College of Rheumatology criteria (Wolfe et al., 1990) as confirmed by a letter from the participants’ primary care provider or rheumatologist; able to speak and read standard English; a minimum of a 6th grade education level; and, ability to understand and sign the consent form and complete study instruments. Exclusion criteria were other systemic rheumatologic conditions or immune disorders such as systemic lupus erythematosus, rheumatoid arthritis, HIV/AIDS; systemic use of corticosteroids; history of epilepsy; currently pregnant; or presence of a psychiatric disorder, such as a history of psychosis, schizophrenia, or bipolar disorder (Menzies et al., 2020). Consented and enrolled participants provided baseline information on symptoms of fatigue, depression, and pain, and provided 3 cc of blood for study-related biomarker identification. Our sample and data quality control protocols have been previously described in detail (Menzies et al., 2020). In brief, blood samples were collected from participants, plasma was isolated via centrifugation, and multiple aliquots frozen at −80 °C in order to avoid multiple freeze thaw cycles prior to metabolomics analysis. Metabolomic analysis was conducted at an affiliate institution in southeastern United States under IRB-approval from both institutions.

Procedure

Banked plasma was selected based on availability of sufficient samples and matched with de-identified baseline symptom data (fatigue, depression, pain). Inclusion and exclusion criteria and protocol methodology, including the procedure for conducting global metabolomic profiling were reported in a previous publication (Menzies et al., 2020). Demographic and symptom data have been previously described in detail and are also summarized in Tables 1 and 2, respectively.

Table 1.

Characteristics of Women (N = 20) with Fibromyalgia.

Characteristic
Age (Mean/SD)	49.1 (6.9)
Race
Black	35%
Indian	5%
Unknown	10%
White	50%
Ethnicity
Hispanic or Latino	5%
Not-Hispanic or Latino	95%
Unknown	0%
Marital Status
Divorced/Separated	30%
Married/Partner	70%
Single-never married	0%
Education
High School	20%
Secondary School	45%
Bachelors+	35%
Income
<$30,000	20%
$30-$59,999	55%
>$60,000	25%
Work Status
Disabled	15%
Full-time	60%
Other/Not Reported	5%
Part-time	5%
Unemployed	15%

Adapted from: (Menzies et al., 2020).

Table 2.

Means and Standard Deviations of Symptoms in Women with Fibromyalgia (N = 20).

Symptom	Mean (SD)
BFI (Total)	6.4 (2.0)
BPI-Severity	5.7 (2.5)
BPI- Interference	5.7 (2.2)
CES-D (total)	22.7 (14.1)

Note. BFI = Brief Fatigue Inventory; BPI-Severity = Brief Pain Inventory–Pain Severity; BPI-Interference = Brief Pain Inventory–Pain Interference; CES-D = Center for Epidemiological Studies–Depression.

Symptom Measures

Symptom measures in the parent study included well-established, valid, and reliable instruments used frequently in individuals with chronic pain. In the present study, we focused on fatigue, depression, and pain. Fatigue was measured by the Brief Fatigue Inventory (BFI; (Mendoza et al., 1999), a 9-item self-report measure that assesses the severity of fatigue and interference in daily functioning over the past week. Severe fatigue can be defined as a score of 7 or higher. Depression was measured using the Center for Epidemiological Studies-Depression (CES-D) scale (Radloff, 1977), a widely used, psychometrically sound instrument designed to detect depressive symptoms in the general population. The CES-D is comprised of 20 items reflecting the domains of depressive affect, somatic symptoms, positive effect, and interpersonal relations. A score of 16 or greater indicates a higher level of depressive symptoms. Pain was measured using the Brief Pain Inventory (BPI) Short form (Cleeland, 1989). The BPI assesses pain severity (BPI-S) and pain interference (BPI-I) using 0–10 numeric scales for item rating; higher scores indicate increased pain/interference. A complete description of symptom measures used in each parent study appeared in prior publications (Menzies et al., 2013, 2014).

Measurement of Plasma Metabolites

Global metabolomic profiling was completed at the Southeast Center for Integrated Metabolomics (SECIM). Briefly, plasma samples (100 µL) were mixed with an internal standard mixture containing stably-labeled standards (20 µL). The protein was precipitated out with 8:1:1 acetonitrile: methanol: acetone (800 µL). The supernatant was collected after centrifugation, transferred to a new tube and dried under nitrogen. The dried sample was reconstituted in 0.1% formic acid in water (100 µL). Samples were analyzed in positive and negative electrospray ionization on a ThermoScientific Q-Exactive with a Dionex Ultimate 3000 UHPLC under reverse phase separation on a C18-pfp column (Ace, 100 × 2.1 mm, 2 µM). Metabolite identification was performed with MZmine and matching metabolite retention time and m/z value to an internal library of 1000 metabolites. Metabolic pathway analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database which contains metabolite sets based on human metabolic pathways (https://www.genome.jp/kegg/pathway.html). Metabolite set enrichment (fold enrichment) were calculated using MetaboAnalyst (a free R package).

Power Analysis and Statistical Analysis

We examined the association between symptoms and known global metabolites we identified. Benjamini-Hochberg procedure was used to adjust for multiple testing (Benjamini & Hochberg, 1995). If we assume that 95% of the metabolites are not associated with a symptom while the correlation between the symptom and the remaining 5% is at a level of 0.6, a sample of 20 is underpowered (57% power) even at a cutoff threshold of 0.2.

Statistical analysis was performed using software R version 3.5.1 (R Core Team, 2014). Descriptive statistics for the patient symptom scores, including means and standard deviations were obtained. Bivariate associations between metabolites and symptom severity scores were evaluated using Pearson’s correlation. Benjamini-Hochberg procedure was used to adjust for multiple testing (Benjamini & Hochberg, 1995).

Results

Complete demographics for this study sample are reported in Table 1. Women with FM (N = 20) had moderately high levels of fatigue (BFI score >7 indicates severe fatigue) and moderate levels of pain severity and pain interference (moderate pain is defined as a worst pain score of 5–6) as shown in Table 2. CES-D scores revealed high levels of depression.

Metabolites and Symptoms

We examined all known metabolites identified in our sample. In this report, we focused on those correlated with at least one symptom at an estimated false discovery rate (FDR) below 0.2 (Table 3). Empty cells correspond to metabolites that were not detected in a given mode (negative or positive). The values given are correlations and those with an FDR < 0.2 are labeled with an asterisk. Further, we conducted metabolic pathway analysis using the KEGG pathway database. Metabolic pathway analysis using symptom-associated metabolites was found to be associated with carnitine synthesis, lipid oxidation, tryptophan metabolism, beta-alanine metabolism, pantothenate and Coenzyme A (CoA) biosynthesis (Figure 1).

Table 3.

Correlations Between Metabolites (Positive/Negative Mode) and Symptoms in Women With FM (N = 20).

Metabolite	BFI	PainS	PainI	CES-D	BFI	PainS	PainI	CES-D
(classification)	(POS)	(POS)	(POS)	(POS)	(NEG)	(NEG)	(NEG)	(NEG)
ENERGY
6-deoxy-hexose	0.21	0.63*	0.47	0.39
pantothenic acid	−0.67	−0.63*	−0.70*	−0.64	−0.67	−0.65*	−0.74*	−0.65
AMINO ACID
Ergothioneine	0.62	0.58	0.66*	0.60
l-carnitine	0.11	0.67*	0.32	0.29
n-acetylserotonin	−0.23	−0.70*	−0.41	−0.25
LIPID
Butyrobetaine	0.43	0.66*	0.61	0.46

Note. BFI = Brief Fatigue Inventory; PainS = Brief Pain Inventory-Severity; PainI = Brief Pain Inventory-Interference; CES-D = Center for Epidemiological Studies-Depression.

*p-value < 0.2.

Figure 1.

Metabolic Pathways of Differential Metabolites Association with Pain Severity, Pain Interference, or both in Women with FM (N = 20). KEGG compound ID for corresponding metabolite were used for MetaboNetwork analysis. Fold enrichment were automatically calculated based on the possible presence of each metabolite in metabolic networks. Negative fold enrichment indicates metabolites present as lower in respective pathways and positive fold enrichment indicates metabolites present as elevated in respective metabolic pathways.

Discussion

This pilot study examines the relationship of select symptoms in women with FM with global metabolomics profiling. We found that pain severity was correlated with circulating 6-deoxyhexose, pantothenic acid, l-carnitine, n-acetylserotonin, and butyrobetaine; pain interference was correlated with pantothenic acid and ergothioneine in those with FM. This suggests that changes in these metabolites and their associated pathways may be a distinct feature of FM-related pain and therefore may play a role as metabolomic biomarkers. A metabolic correlation to a clinical syndrome such as FM does not explain why abnormalities are present (Vogt et al., 2016). While we do not claim diagnostic specificity in this report (Naviaux et al., 2016), our study findings provide preliminary considerations of an underlying biologic profile in FM that may, with future metabolomics research, shed light on how changes in metabolism are associated with symptom presence, severity and interference.

Associations Among Metabolites and Symptoms

Pantothenic acid (Vitamin B5) is a water-soluble B vitamin known as the “anti-stress vitamin” (Gheita et al., 2020) and is obtained from foods or dietary supplements. Pantothenic acid is a precursor for synthesizing CoA and acyl carrier protein (ACP) that are essential for fatty acid synthesis and degradation. Hypothetically, the deficiency of pantothenic acid may cause pro-inflammatory effects on the immune system by producing cytokines, and result in some symptoms, including arthritic pain/myalgia, fatigue, depression, and insomnia (Gheita et al., 2020). To date, there has been no study of a beneficial role of pantothenic acid on FM; however, previous studies of other rheumatologic diseases may implicate a connection between pantothenic acid and FM. Interestingly, high doses of pantothenic acid improved systemic lupus erythematosus in a small number of women (Leung, 2004) and administration of pantothenic acid exhibited a clinical improvement in a patient with osteoporosis, and brain and intestinal dysfunction (Subramanian et al., 2017). The plasma level of pantothenic acid in this preliminary study was inversely correlated with pain severity (r = −0.63; −0.67) and interference (r = −0.70; −0.74; Table 3), which suggests that deficiency or lower levels of this vitamin or its substrates may result in heightened pain severity and intensity in individuals with FM. KEGG pathway analysis identified that pantothenic acid was associated with both pantothenate-CoA biosynthesis and beta-alanine (β-alanine) metabolism and both pathways were downregulated. Pantoate-beta-alanine ligase protein encoded by PANC gene utilizes ATP, pantoate, and β-alanine as substrates to produce pantothenic acid (Figure 1). In our study, we did not detect a relationship between β-alanine and pain severity. While the role of β-alanine in pain severity is unknown, β-alanine supplementation has been shown to decrease muscle pain and muscle fatigue during resistance exercises intended to increase muscle performance in healthy young adults (Roveratti et al., 2019). This finding suggests that the lower level of pantothenic acid in our sample may be related to down regulation of its substrate β-alanine.

6-deoxyhexose (rhamnose) was correlated (r = 0.63) with pain severity in women with FM in this study. 6-deoxyhexose is the sugar residue component of glycosides and presents as L-form in nature (Wishart et al., 2018). In contrast to our study findings, Iorio et al. (2014) showed that lanthipeptides, which carry a 6-deoxyhexose moiety, have a beneficial effect on nociceptive pain; however, it is unknown as to how 6-deoxyhexose may be related with the pain experience. KEGG metabolic pathway analysis suggested that L-Rhamnose was involved in an elevation of the fucose and mannose degradation pathway, leading to lactate accumulation by sequential metabolic reactions (Figure 1). It has been reported that lactate accumulation is associated with muscle fatigue and pain (Immke & McCleskey, 2001; Ishii & Nishida, 2013). It would be feasible to test a relationship of this metabolite with pain perception in a larger trial.

In the current study, ergothioneine (EGT), an antioxidant obtained through dietary means, was correlated (r = 0.66) with pain interference. High levels of EGT are found in black and red beans, red meat, liver, kidney, grains, and certain species of mushrooms. Although a wide body of evidence supports a protective role of EGT, high levels found in populations suffering from chronic inflammatory conditions such as rheumatoid arthritis and Crohn’s disease, suggest that it could play a role in disease progression (Cheah & Halliwell, 2012; Halliwell et al., 2016). Further research to identify how accumulation of EGT may contribute to impairment in functioning due to pain is needed.

L-carnitine (r = 0.67), a molecule that is commonly found in tissues and plays a key role in cellular metabolism (Rebouche, 2004; Stoppoloni et al., 2013), was correlated with pain severity in the current study. Previous studies have underscored a beneficial effect of L-carnitine in the treatment and prevention of pain, which may be contradictory to our preliminary finding of a positive relationship between l-carnitine and pain severity. For example, Mahdavi and colleagues reported that L-carnitine supplementation may reduce knee pain in patients with osteoarthritis (OA) as well as decrease inflammatory mediators in serum without oxidative stress changes (Mahdavi et al., 2015, 2016). Similarly, L-acetylcarnitine (LAC) treatment produced analgesic effects against chronic inflammatory pain through up-regulation of type2 metabotropic glutamate (mGlu2) receptors in the dorsal horn in a mouse model (Notartomaso et al., 2017), and showed a neuroprotective effect in patients with carpal tunnel syndrome (CTS) according to testing results of the median nerve sensory conduction velocity and CTS questionnaires (Cruccu et al., 2017). LAC was also tested on FM patients in a pilot randomized controlled trial, in which it improved pain and depressive symptoms as well as quality of life in patients (Leombruni et al., 2015).

Butyrobetaine is a direct biosynthetic precursor of carnitine (Minkler et al., 2015), through the lysine degradation pathway which, as with l-carnitine, is an important component in the β-oxidation of long chain fatty acids involved in energy production (Ramsay et al., 2001). Lipid oxidation is an important metabolic signature associated with pain (Osthues & Sisignano, 2019). Interestingly, KEGG metabolic pathway analysis revealed that both butyrobetaine and L-carnitine lead to several lipid oxidation networks including β-oxidation of very long chain fatty acids, β-oxidation of branch chain fatty acids, mitochondrial β-oxidation of long chain fatty acids, and mitochondrial β-oxidation of short chain fatty acids (Figure 1). In this study, we found butyrobetaine to be correlated (r = 0.66) with pain severity. Evidence is lacking with regard to butyrobetaine and associated symptoms; however, studies have revealed butyrobetaine to be a significant metabolite of disease states such as cardiovascular disease, a condition known to carry a high symptom burden including fatigue and pain (Skagen et al., 2016). Recently, both carnitine and butyrobetaine have been linked to accelerated atherosclerosis due to their metabolic conversion by gut bacteria into trimethylamine-N-oxide (Minkler et al., 2015). In the original report of our study findings (Menzies et al., 2020), 5% of the study sample reported comorbid heart disease and 40% reported high blood pressure. It may be that l-carnitine and butyrobetaine play contributing roles in this regard as the positive relationship between these two metabolites and pain severity suggest the possibility of lipid oxidation. As such, further exploration in individuals with FM warrants consideration.

N-acetylserotonin (NAS), also known as normelatonin, is the immediate chemical precursor of melatonin, and in a number of disorders (e.g., systemic inflammatory, neurodegenerative, and psychological) is reported to be decreased following its release from the pineal gland. In particular, the expression of arylalkylamine-N-acetyltransferase (AANAT), acetylserotonin methyltransferase, and hydroxyindole-O-methyltransferase (HIOMT), has been shown to impact melatonin homeostasis, wherein decreased levels of these melatonin synthesizing enzymes were associated with gastric pain (Chojnacki et al., 2013). In their systematic review, Hemati et al. (2020) focused on studies that examined the effect of melatonin on symptoms of pain, fatigue, and depression. The authors reported consistent findings across studies regarding significant improvement in pain measures following administration of melatonin, but inconsistency among studies regarding impact on fatigue and depression. Other researchers have reported that NAS has an antioxidant and anti-inflammatory effect in a diverse range of disorders, such as Parkinson’s disease, multiple sclerosis, and depression (Anderson & Maes, 2014), and as such, may have some implication in the inflammatory response to other chronic illnesses, including FM. In the current study, NAS inversely correlated (r = −0.70) with pain severity, which suggests that lower levels may result in heightened pain severity in individuals with FM. Metabolic pathway analysis identified that NAS is an important metabolite in tryptophan metabolism where AANAT catalyze serotonin to NAS (Figure 1). Altered tryptophan metabolism has been noted in FM (Schwarz et al., 2002, 2003). Taken together, these findings suggest that NAS may play an important mechanistic role in regulating pain via the central nervous system or neurogenic inflammation, of which is amplified in fibromyalgia patients. More research is warranted to understand the role and process of NAS in fibromyalgia and other pain conditions.

Study Limitations

The cross-sectional nature of the study design from which the data for this secondary analysis was obtained does not allow causal claims to be made. More generally, results obtained at the metabolic level do not automatically mean that a disease or syndrome should be understood at that level (Vogt et al., 2016). The exploratory nature of this study simply suggests associations, but it does not prove them. Nonetheless, the study findings are sufficient to warrant further exploration in a larger sample that reflects associations with age, sex, nutritional status, and time of sampling. As some researchers have noted, it is possible that changes in normal metabolic patterns could actually mask molecular changes caused by the chronic illness itself (Nordström & Lewensohn, 2010). This was a small sample size using banked plasma from study participants recruited between 2008 and 2010. For purposes of this pilot study, samples were analyzed by symptoms, but not by race or age. Limitations related to dated banked plasma and management of metabolomic profiling are addressed in a prior publication (Menzies et al., 2020). Finally, given the small sample size, some of our findings could be false positives; however, given the moderate to high symptom correlations we found for several metabolites, we believe our findings merit validation in a larger separate study of fibromyalgia participant.

Implications for Patient Care

This secondary analysis provides preliminary data that suggests dysregulated metabolic profiles are implicated in FM symptom manifestation, and in this case, pain severity, and pain interference. Both oxidative stress and systemic inflammation have been suggested as either causative or the result of FM (Benlidayi, 2019; Sanchez-Dominguez et al., 2015); however, to address the immediacy of needed nursing care for this vulnerable patient population, we turn to the current absence of diagnostic markers to specifically identify the presence of FM and to the promise of precision medicine. In the context of diagnostic markers, metabolomics offers the possibility of quantifying and qualifying metabolite composition that can translate into a disease-specific metabolic signature, in this case, for pain associated with fibromyalgia. In the context of precision medicine, the possibility of directly evaluating the phenotype of individuals, let alone accurately diagnosing them, will contribute to determining appropriate treatments and assessing therapeutic responses (Puchades-Carrasco & Pineda-Lucena, 2017).

Implications for Nursing Research

The impact of living with chronic pain arising from a syndrome whose etiology and pathophysiology is largely unknown (Skaer & Kwong, 2017; Sluka & Clauw, 2016) has long-term deleterious effects on patients’ health-related quality of life. Health-related quality of life has been ranked to be significantly worse in FM than in patients with other forms of chronic pain or people from the general population. To date there is no acknowledged treatment algorithm for FM, with diagnosis based on clinical presentation and symptom severity (Skaer & Kwong, 2017). The metabolome arises partially from the exposomes of diet and lifestyle and environmental factors, and from the metabolic products of the gut microbes, and thus are modifiable (Beger et al., 2016). As noted by Misra (2020), nurse scientists are poised to improve outcomes and quality of life through contributions to precision medicine and the development of personalized nursing interventions. Therefore, examining metabolic profiles and differentiating metabolites in FM could: (a) improve our understanding of biological changes leading to symptom development, (b) be used to identify diagnostic biomarkers specific to the disorder, and (c) be used as biomarkers of change related to treatment efficacy (Beger et al., 2016).

Conclusion

Because metabolomics have the potential to bridge the gap between the FM phenotype and data obtained through metabolomic profiling, these preliminary study findings contribute to the development of further studies that take into consideration factors that may affect metabolite levels, including sex, age, race, ethnicity, length of time since FM diagnosis, comorbidities, and medications. The goal is to contribute to understanding the disease mechanisms operating in patients with FM that may lead to symptoms of pain, fatigue, and depression, and ultimately assist in the elucidation of novel therapeutic pathways, identify potential diagnostic or treatment effect biomarkers, and facilitate the development of personalized health strategies for symptom management (Beger et al., 2016; Naviaux et al., 2016).