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. 2022 Oct 28;25(2):326–335. doi: 10.1177/10998004221136567

Feasibility of Acupuncture and Exploration of Metabolomic Alterations for Psychoneurological Symptoms Among Breast Cancer Survivors

Hongjin Li 1,2,✉, Judith M Schlaeger 1, Crystal L Patil 1, Oana C Danciu 2,3, Yinglin Xia 3, Jun Sun 2,3, Ardith Z Doorenbos 2,4
PMCID: PMC10236441  PMID: 36306737

Abstract

Objective

Approximately 24–68% of breast cancer survivors report co-occurring psychoneurological symptoms of pain, fatigue, sleep disturbance, depression, and anxiety during and after cancer treatment. This study aimed to assess the feasibility and acceptability of acupuncture for the treatment of multiple psychoneurological symptoms among breast cancer survivors and explore metabolomic changes before and after acupuncture.

Methods

We conducted a single-arm, prospective pilot study of breast cancer survivors with at least two moderate to severe psychoneurological symptoms (>3 on a 0–10 scale). Acupuncture was administered twice weekly for 5 weeks, for 30 minutes per session. Along with Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaires, a fasting serum comprehensive hydrophilic metabolites panel was analyzed at baseline and after acupuncture.

Results

Eight participants (mean age 52.5 ± 10.9 years; 62.5% Black) were enrolled. Feasibility was supported, with 67% recruitment, 87.5% retention, and 98% acceptability. Post intervention, PROMIS T-scores were reduced for all psychoneurological symptoms. Significant differences in serum metabolites before and after acupuncture were F-1,6/2,6-DP, glutathione disulfide, phosphorylcholine, 6-methylnicotinamide, glutathione, and putrescine (variable importance of projection values larger than 1.5 and p values <0.05). Pathway analysis indicated that glutathione metabolism (p = 0.002, q = 0.071), and arginine and proline metabolisms (p = 0.009, q = 0.166) were potentially involved in mechanisms of acupuncture.

Conclusions

Acupuncture to reduce multiple psychoneurological symptoms among breast cancer survivors was feasible and acceptable. Study findings also shed light on the metabolic pathways involved in the acupuncture response and will be tested in future studies.

Keywords: acupuncture, psychoneurological symptoms, metabolites, breast cancer

Introduction

Approximately 24–68% of breast cancer survivors report multiple co-occurring psychoneurological symptoms of pain, fatigue, sleep disturbance, depression, and anxiety during and after cancer treatment (Bjerkeset et al., 2020). Some psychoneurological symptoms can persist 5–10 years after completion of cancer treatment and have a detrimental impact on quality of life, functional status, and work functioning (Al-Bashaireh et al., 2021; Dorland et al., 2018). The typical symptom management approach is to provide intervention for each symptom; this approach does not address the burden and cost of managing multiple treatments or the risks of polypharmacy (Kwekkeboom et al., 2020). There is urgent need to efficiently coordinate symptom management strategies for co-occurring psychoneurological symptoms among breast cancer survivors.

Effective management of psychoneurological symptoms is challenging. Pharmacotherapy is the primary treatment modality for symptom management; however, opioids are often associated with significant adverse effects. Nonpharmacological interventions can be used to manage multiple psychoneurological symptoms, leading to decreased prescription opioids (Mannes et al., 2022). Clinical practice guidelines for the management of chronic pain in adult cancer survivors support nonpharmacological approaches (Paice et al., 2016).

Acupuncture has been used as a nonpharmacological, complementary and integrative health (CIH) treatment to manage cancer symptoms in more than 60% of National Cancer Institute–designated comprehensive cancer centers in the United States (Brauer et al., 2010). Emerging evidence shows that acupuncture is effective at reducing cancer-related pain (Hershman et al., 2018), fatigue (Smith et al., 2013), depression (Xiao & Liu, 2014), and sleep disturbance among cancer survivors (Garland et al., 2017). A major tenet of Traditional Chinese Medicine (TCM) is holism, which asserts that the body itself is a unified whole (Giovanni, 1989). The viscera and tissues of the body each comprise an entire coordinated system, which together communicate with and influence one another (Zhang & Chor, 2021). This interrelationship is fostered by the connecting and communicating actions of the meridians. Because of the holistic interrelationships of the viscera, tissue, and meridians, each acupuncture point can treat multiple symptoms. However, whether acupuncture can mitigate multiple psychoneurological symptoms simultaneously remains unclear.

According to TCM theory, qi is the vital energy that flows throughout the body along invisible pathways called meridians (Zhou & Benharash, 2014). Pain, fatigue, and sleep disturbance result from an excess or deficiency in qi and blood in the meridians (Lozano, 2014). The insertion of needles at specific acupoints is thought to improve the flow of qi and blood in the meridians, thereby producing therapeutic effects. Animal studies have provided evidence that the sympathetic and parasympathetic nervous systems are activated after acupuncture needle stimulation, resulting in a series of neural, endocrine, and immune responses (Wu et al., 2017). Significant gaps still remain in our understanding of the mechanisms underlying acupuncture for treating cancer-related symptoms in humans.

Metabolomic profiling can capture hundreds of key molecules simultaneously and may identify diverse metabolic pathways (Beger et al., 2016), reveal metabolic changes consequent to genetic variation, and illuminate the time-effect relationship between acupuncture treatment and symptoms. For example, glutathione metabolism may be one of the targeted therapeutic mechanisms underlying acupuncture. Glutathione is a tripeptide and plays an important role of antioxidant defense (Morris et al., 2014). Studies show evidence of the effects of acupuncture on the glutathione system and its antioxidant effect among obese individuals and people with depression (Ghaemi et al., 2021; Li et al., 2020). Acupuncture can significantly increase glutamine and glutathione concentrations (Li et al., 2020). Several human studies have used metabolomics profiling to understand the molecular mechanisms involved in acupuncture treatment for hypertension and depression (Li et al., 2020; Zhang et al., 2016). To date, no study has (1) utilized a standardized acupuncture protocol for treating multiple psychoneurological symptoms and (2) applied a metabolomics approach prior to and following acupuncture for psychoneurological symptoms among breast cancer survivors. The purposes of this study are to assess the feasibility and acceptability of acupuncture for the treatment of multiple co-occurring psychoneurological symptoms among breast cancer survivors and to explore metabolomic changes before and after acupuncture.

Methods

Study Design

This was a single-arm, prospective pilot study conducted with breast cancer survivors. A research team member met or spoke on the phone with eligible survivors who expressed an interest in the study, explained the study, screened for eligibility, and obtained written informed consent. The university’s institutional review board approved the study (IRB 2021–0840). This study was registered at ClinicalTrials.gov (ref. NCT05417451).

Setting and Sample

Breast cancer survivors attending the breast oncology clinic at the University of Illinois Chicago Hospital and Health Sciences System (UI Health) were recruited for this study. UI Health has a diverse patient population that is 8% Asian, 48% Black, 24% Hispanic, and 20% non-Hispanic White. This study’s participant inclusion criteria were: (1) women with histologically confirmed stage 0–III breast cancer; (2) ≥ 18 years old; (3) who had completed primary cancer treatment (e.g., surgery, radiotherapy, chemotherapy); (4) was able to read and speak English; and (5) had self-reported pain, fatigue, sleep disturbance, depression, and/or anxiety in the last month with an average severity rating of three or more (on a 0–10 numeric rating scale) for at least two of the five symptoms. Exclusion criteria were: (1) having a bleeding disorder; (2) being physically or cognitively unable to complete the study procedures; or (3) being pregnant.

Sample Collection and Procedures

Each consented participant then completed a self-reported demographic, clinical, and symptom questionnaire. A fasting blood sample (10 cc) for serum metabolites was collected between 8 a.m. and 11 a.m. before the 1st acupuncture session, in a red-top vacutainer tube with no anticoagulant. All participants received a 5-week, 10-session acupuncture intervention. After collection, the blood samples sat undisturbed for 30 minutes at room temperature to allow for clotting. This was followed by centrifugation at 2000 × gram for 15 minutes to allow for serum separation. The serum was aliquoted into clean polypropylene tubes using a Pasteur pipette. Samples were then stored at −80°C until ready for processing. The symptom questionnaires and fasting blood sample (10 cc) were collected a second time after participants completed their 10th acupuncture session.

Metabolomics Processing

Frozen serum samples were processed and analyzed for targeted metabolomics profiling at the Metabolomics Core Facility of the Robert H. Lurie Comprehensive Cancer Center of Northwestern University. A standard protocol was used to process the samples. Serum samples were thawed at room temperature for ∼30 minutes, then vortexed. Next, 25 μL of each sample was transferred to a 2 mL Eppendorf tube, combined with 150 μL high-performance liquid chromatography (HPLC) grade methanol, vortexed for 2 minutes, and stored at −20°C for 20 minutes. The extraction solution was dried using a SpeedVac. The dried samples were stored at −20°C and were reconstituted in 500 μL of 5 mM ammonium acetate in 40% water/60% acetonitrile, followed by overtaxing for 30 seconds. The samples solution was then centrifuged for 30 minutes at 20,000 g, 4°C, and the supernatant was transferred to a liquid chromatography vial for liquid chromatography-mass spectrometry (LC-MS) analysis.

Comprehensive metabolomics profiling was carried out by HPLC and high-resolution liquid chromatography mass spectrometry and tandem mass spectrometry (HPLC-MS/MS) in both positive and negative ion modes. Extracts were analyzed by LC-MS using an UltiMate 3000 series HPLC (Thermo Fisher Scientific, LesUlis, France) coupled with a Thermo Scientific Q Exactive mass spectrometer in line with an electrospray source. In positive/negative polarity switching mode, an m/z scan range from 60 to 900 was chosen and MS1 data was collected at a resolution of 70,000. The automatic gain control target was set at 1 × 106 and the maximum injection time was 200 ms. The top five precursor ions were subsequently fragmented, in a data-dependent manner, using the higher energy collisional dissociation cell set to 30% normalized collision energy in MS2 at a resolution power of 17,500. Besides matching m/z, metabolites were identified by matching retention time with analytical standards and/or with MS2 fragmentation pattern. Data acquisition and analysis were carried out by Xcalibur 4.1 software and Tracefinder 4.1 software, respectively (both from Thermo Fisher Scientific).

Acupuncture Intervention

The acupuncture study intervention was developed according to the Standards for Reporting Interventions in Controlled Trials in Acupuncture (STRICTA) guidelines (MacPherson et al., 2010). The acupuncture intervention consisted of 10 acupuncture sessions: twice weekly for 5 weeks, with at least 1 day between sessions. All participants received (1) a standardized acupuncture protocol with acupuncture points for treating generalized pain, fatigue, sleep disturbance, depression, and anxiety; and (2) tailored points to treat up to three of the individual’s most painful areas (see Table 1). The needles were retained for 30 minutes and twirled with an even rotation 3 times throughout the treatment (10 minutes, 20 minutes, and 30 minutes after insertion) to move qi and blood. A single-size Korean (DBC) 0.25 × 40 stainless-steel acupuncture needle with wound stainless-steel head was used for all needle insertions. The protocol was delivered by a licensed acupuncturist who was certified by the National Certification Commission for Acupuncture and Oriental Medicine. The acupuncturist monitored each participant for signs of needle shock and other side effects, such as bruising and dizziness. If adverse events occurred, they were documented in an adverse event log.

Table 1.

Standardized Acupuncture Point Protocol for Breast Cancer Survivors With Psychoneurological Symptoms.

Indications Points
Full body points for multiple symptoms during endocrine therapy (all participants, all visits)
Bilateral LI4, LV3, GB34, KD3, SP6CV4 unilateral, right SJ5, left GB41, right PC6, right HT7, right LU9
Additional points for specific pain locations (participants choose up to 3 of their most painful areas)
Breast/chest LV14, GB41
Fingers Baxie, SI3, LI3 (index), LU10 (thumb)
Shoulders ST14, LI15 (top and side of shoulder), jianqian (anterior shoulder), SI10 (posterior shoulder)
Lower back BL60, KD3 yaotongxue
Knees Heding, ST35, medial xiyan (overall knee pain), SP9 (medial knee), GB34 (lateral knee)
Feet/toes SP3 (big toe pain), LV3, GB41
Hips GB29, GB39, GB34
Wrists SJ4, SI5
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Acupuncture Intervention Fidelity

To ensure the scientific rigor of this study, we followed the established methods outlined in the NIH Behavior Change Consortium treatment fidelity guidelines (Bellg et al., 2004; Eaton et al., 2011) using the treatment fidelity checklist to assess research team training, treatment delivery, receipt of treatment, and enactment of the treatment skills (Borrelli et al., 2005). We trained acupuncturist in the acupuncture protocol with written instructions and diagrams, until fidelity of the intervention was achieved. The acupuncturists documented the points needled for the standardized acupuncture protocol as well as the extra points needled per individual participants’ pain symptoms. For the random unscheduled fidelity checks, we monitored the needle insertions for appropriate placement during training and fidelity checks.

Measures

Demographics and Clinical Characteristics

Self-reported sociodemographic characteristics (age, race, education, income, marital status) were collected via a questionnaire. Clinical status (disease stage, types of treatments) was extracted from the electronic health record.

PROMIS Measures

Self-reported symptom experience was collected through Patient-Reported Outcomes Measurement Information System (PROMIS) short-form questionnaires, which are reliable measures of symptom experience among cancer patients, with good internal consistency and convergent validity (Cronbach alphas: 0.86–0.96) (Cessna et al., 2016; Quach et al., 2016). PROMIS Pain Interference (8 items) specifically focuses on pain interference, defined as the interference of pain in daily activities involving physical, psychological, and social functioning (Amtmann et al., 2010). PROMIS Fatigue (8 items) is used to assess fatigue from mild subjective feelings of tiredness to an overwhelming and sustained sense of exhaustion (Cella et al., 2010). PROMIS Sleep Disturbance (8 items) is used to assess perceived sleep quality, sleep depth, and restoration associated with sleep; perceived difficulties and concerns with getting to sleep or staying asleep; and perceived adequacy of and satisfaction with sleep (Yu et al., 2011). PROMIS Depressive Symptoms (8 items) addresses sadness, loss of interest, worthlessness, low self-esteem, loneliness, and interpersonal alienation over the past 7 days (Pilkonis et al., 2011). PROMIS Anxiety (8 items) is used to assess fearfulness, worry, and nervousness (Pilkonis et al., 2011).

Feasibility and Acceptability Outcomes

Recruitment feasibility (set as >60%) is the process for determining the time frame and parameters necessary for successful recruitment. We measured this by number of participants enrolled divided by number of participants invited to participate. Retention (set as >80%) was measured at post-intervention by dividing the number of participants present at these time points by the number of individuals who were enrolled and began the acupuncture protocol.

The Protocol Acceptability Scale for Treating Psychoneurological Symptoms with Acupuncture (Wilkie et al., 2001, 2003), completed after the last acupuncture session, was used to assess the acceptability of all study measures. This acceptability scale is a 9-item self-report measure, with scores ranging from 0 to 18, to assess acceptability to the study participants. The scale is reliable and valid and has been used in other studies with stable test-retest reliability (Wilkie et al., 2001, 2003). This study’s protocol would be deemed acceptable if the total mean acceptability scale score is higher than 80% of the maximum score.

Statistical Analyses

Descriptive statistics such as mean and standard deviation (for continuous variables) and frequency and proportion (for categorical variables) were used to depict participants’ sociodemographic and clinical characteristics and to calculate feasibility and acceptability. Metabolomics datasets were loaded into MetaboAnalyst 5.0 (https://www.metaboanalyst.ca) for analysis. Missing values were replaced with the minimum values registered in the samples. All metabolites missing in more than 60% of samples overall were filtered and then were log2 transformed and normalized. Univariate analysis (paired t-test and fold change analysis), multivariate analysis (principal component analysis [PCA]), and orthogonal partial least squares discriminant analysis [OPLS-DA]) were used to compare metabolic changes before and after the acupuncture intervention as in Statistical Data Analysis of Microbiomes and Metabolomics (Xia & Sun, 2022). We used the Benjamini-Hochberg false discovery rate (FDR) method, with a threshold q value of 0.05, to control the number of false positives (false discoveries) for multiple comparisons (Benjamini & Hochberg, 1995). Fold change above two was considered statistically significant. We selected differential metabolites as significant if their VIP values were larger than 1.5 from the OPLS-DA and their p values were greater than 0.05 from the 2-tailed Student’s t-test (Weljie et al., 2011). Pathway analysis was conducted on the MetaboAnalyst website. The differential metabolites of serum samples were imported to match the Human Metabolome Database, PubChem database, and KEGG (Kyoto Encyclopedia of Genes and Genomes) database (the mass error tolerance was <15 ppm).

Results

Sociodemographic and Clinical Characteristics

Participants ranged in age from 43 to 66 years (mean age 52.5 ± 10.9 years). Five of eight total participants self-identified as Black, and seven identified as non-Hispanic. Four participants had stage II breast cancer; all eight were taking endocrine therapy (see Table 2). Seven reported experiencing psychoneurological symptoms at a severity of three or more on a 0–10 scale. Figure 1 shows the distribution of sociodemographic and clinical characteristics.

Table 2.

Characteristics of the Sample (N = 8).

Sociodemographic Characteristics Mean ± Sd or n (%)
Gender
 Female 8 (100%)
Age (years) 53.1 ± 8.4
Race
 Black 5 (62.5%)
 White 2 (25%)
 Asian 1 (12.5%)
Ethnicity
 Hispanic 1 (12.5%)
 Non-hispanic 7 (87.5%)
Highest education
 Less than high school 1 (12.5%)
 High school graduate 4 (50.0%)
 Bachelor’s degree 1 (12.5%)
 Graduate degree 2 (25.0%)
Income
 < $35,000 5 (62.5%)
 $35,000–$100,000 2 (25.0%)
 > $100,000 1 (12.5%)
Married/partnered
 Yes 3 (37.5%)
 No 5 (62.5%)
Clinical characteristics
Cancer stage
 0 1 (12.5%)
 I 3 (37.5%)
 II 4 (50.0%)
Cancer treatment
 Chemotherapy 5 (62.5%)
 Endocrine therapy 8 (100%)
Number of comorbidities
 1 6 (75.0%)
 2 1 (12.5%)
 3 1 (12.5%)
Number of psychoneurological symptoms
 2 1 (12.5%)
 3 3 (37.5%)
 4 2 (25.0%)
 5 2 (25.0%)
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Figure 1.

Figure 1.

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(a) Volcano plot showing metabolites that were upregulated (red dots) or downregulated (blue dots) before versus after the acupuncture intervention. (b) Principal component analysis for same samples; red dots represent before-acupuncture cases and green dots represent after-acupuncture cases. (c) Heat map showing the top 25 metabolite changes before (red) and after (green) the acupuncture intervention.

Feasibility and Acceptability

We screened 12 potential participants. Eight met eligibility requirements and agreed to participate in the study. All eight completed the pre-intervention procedures, and seven completed all 10 acupuncture sessions for an attrition rate of 12.5%, which is less than our target of 20%. Among those who completed the Protocol Acceptability Scale for Treating Psychoneurological Symptoms with Acupuncture, the mean acceptability score was 97.6%, well over the criterion of 80% or more.

Symptom Changes After Acupuncture

Pain, fatigue, sleep disturbance, depression, and anxiety scores at baseline and post-intervention are displayed in Table 3. Overall, participants reported reduced T scores for pain inference (−7.3), fatigue (−6.9), sleep disturbance (−8.8), anxiety (−6.0), and depression (−3.3) post-intervention. None of the participants reported adverse events during their treatments.

Table 3.

Reported PROMIS Outcomes Before and After the Acupuncture Intervention (N = 8).

Outcomes Before (T Score ± SD) After (T Score ± SD) Change (T Score ± SD)
Pain interference 60.3 ± 9.2 53.0 ± 7.1 7.3 ± 5.0
Fatigue 56.9 ± 11.2 50.1 ± 6.5 6.9 ± 8.2
Sleep disturbance 70.6 ± 7.3 62.0 ± 8.3 8.8 ± 12.1
Anxiety 60.9 ± 8.4 54.9 ± 9.1 6.0 ± 8.1
Depression 50.9 ± 12.1 45.9 ± 9.8 3.3 ± 17.9
Psychoneurological symptom cluster 69.9 ± 6.1 52.9 ± 4.6 7.0 ± 7.7
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Abbreviation: PROMIS, Patient-Reported Outcomes Information System.

Potential Biomarkers

The comprehensive metabolite panel covered most common 261 metabolites in total. Based on metabolites identified in both univariate and multivariate analysis, a total of six metabolites had significant changes after the acupuncture intervention (VIP values  >  1.5 and p values  < 0 .05). These metabolites were F-1,6/2,6-DP, glutathione disulfide, phosphorylcholine, 6-methylnicotinamide, glutathione, and putrescine (Figure 1(a), Table 4).

Table 4.

Differential Metabolites Before and After the Acupuncture Intervention.

Metabolite Before Acupuncture (Mean of Peak area) After Acupuncture (Mean of Peak area) VIP p Value Log2 (FC)
F-1,6/2,6-DP 1.38 × 105 3.69 × 106 2.291784 0.012 4.302
Glutathione disulfide 1.92 × 106 3.60 × 107 2.135785 0.025 3.804
Phosphorylcholine 3.99 × 107 7.16 × 108 2.104418 0.027 3.764
6-Methylnicotinamide 2.69 × 108 1.11 × 108 2.089280 0.031 −1.556
Glutathione 2.44 × 107 2.21 × 108 2.011526 0.041 2.770
Putrescine 1.17 × 106 1.75 × 108 2.009914 0.048 6.733
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Abbreviation: VIP, Variable importance in projection.

As seen in the PCA plot (Figure 1(b)), each point represents a serum metabolite. The distribution in the PCA plot revealed that metabolite profiles were separated at baseline but became clustered after the 10-session acupuncture protocol, indicating that the serum metabolic pattern changed significantly after acupuncture.

Given the small sample and preliminary nature of the study, we considered a q-value (FDR) of 20% to be significant (Benjamini & Hochberg, 1995) and found that glutathione metabolism (raw p = 0.002, q = 0.071) and arginine and proline metabolisms (raw p = 0.009, q = 0.166) were potentially involved in mechanisms of acupuncture (Figure 2).

Figure 2.

Figure 2.

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Pathway analysis of differential serum metabolites false discovery rate.

Discussion

This study demonstrates that implementation of acupuncture to manage multiple psychoneurological symptoms among breast cancer survivors is indeed feasible and acceptable. It was achievable to enroll and retain most participants to complete all study measures, and the participant acceptability criterion was met.

Pain, fatigue, sleep disturbance, depression and anxiety usually co-occur and cluster together as a psychoneurological symptom cluster among breast cancer survivors (Doong et al., 2015; Langford et al., 2016). The etiology of the psychoneurological symptom cluster is unclear. But evidence showed that these symptoms usually share common biological mechanisms and can be managed together (Kim et al., 2012; Kwekkeboom et al., 2018; Starkweather et al., 2017). In this study, most of the breast cancer survivors were experiencing three or more co-occurring psychoneurological symptoms following their major cancer treatment. This result is consistent with the finding that nearly 50% of breast cancer survivors report three or more long-term symptoms after primary cancer treatment (Schumacher, 2021). As shown in both this study and our recent meta-analysis (Li et al., 2021), acupuncture offers a holistic treatment that has the ability to alleviate multiple psychoneurological symptoms at the same time; this effect is supported by the underlying theory of TCM holism.

The absence of a single treatment to effectively address the wide variety of physical and emotional problems experienced by breast cancer survivors, and to treat the whole person focusing on the mind-body connection, remains a significant gap in the oncology field. It was to fill this gap that we developed a standardized acupuncture protocol and provide preliminary evidence for acupuncture as a single therapy with minimal side effects that can have a synergistic effect on multiple symptoms and improve whole-person health. Future studies can implement this standardized protocol in a larger trial to test the effectiveness of acupuncture for reducing multiple co-occurring psychoneurological symptoms among breast cancer survivors.

Based on our metabolomic analysis and pathway analysis, glutathione metabolism stands out as a significant pathway underlying the mechanism of acupuncture. Glutathione is a tripeptide with multiple functions: it is the key antioxidant within cells and is responsible for the detoxification of reactive oxygen species and reactive nitrogen species; it also modifies the activity of numerous proteins, including intracellular signaling molecules, and plays an important role in the regulation of pro-inflammatory cytokines (Morris et al., 2014). Decreased glutathione is associated with many neuroimmune symptoms and diseases (e.g., fatigue, depression) (Chou et al., 2021; Savushkina et al., 2022). Evidence from animal studies suggests that acupuncture can inhibit oxidative stress by increasing glutathione levels, thus enhancing the activity of antioxidative enzymes and inhibiting the excessive generation of reactive oxygen species (Su et al., 2020).

Consistent with this study’s findings, Li et al. (2020) recently found that electroacupuncture was involved in glutathione metabolism among 60 people with moderate depression. Glutamine (p = 0.02) and glutathione (p < 0.011) concentrations were significantly increased after electroacupuncture. Based on this evidence, glutathione metabolism warrants further research as a targeted therapeutic mechanism underlying acupuncture for psychoneurological symptoms. In addition, research has also found that gut microbiota have systemic effects on the host’s amino acid metabolism and regulate glutathione metabolism (Mardinoglu et al., 2015). Given the important role of the gut microbiome in regulating host metabolism (Agus et al., 2018), future studies can use multi-omics and investigate the response of the gut microbiome to metabolite changes induced by acupuncture.

Limitations

The present study has several limitations. The human metabolome is sensitive to many external factors, including diet, lifestyle habits (i.e., sleep patterns, exercise), and medications. Given the sample size of this feasibility study, we did not control and adjust them in the analysis. Future studies can measure these external factors as well as control for them in the data analysis. This study explored the possible metabolic targets of acupuncture, and future findings may eventually predict the metabolic pathways involved in acupuncture for reducing psychoneurological symptoms among breast cancer survivors. However, the small sample size and single-arm design reduced our ability to rule out the role of the placebo effect. Findings of the mechanism of acupuncture in survivors of breast cancer should thus be viewed as preliminary and interpreted with caution. However, results from this study can inform a powered, rigorous future randomized controlled trial using metabolomics to understand the effect of acupuncture in reducing co-occurring psychoneurological symptoms among breast cancer survivors.

Conclusion

In conclusion, our study suggests it is feasible and acceptable to implement our acupuncture protocol to manage multiple psychoneurological symptoms among breast cancer survivors. This study provides preliminary data to support the potential involvement of glutathione metabolism and arginine and proline metabolisms in the mechanisms of acupuncture. Leveraging metabolomics and microbiome approaches, we will further explore biomarkers associated with treatment response to acupuncture and ultimately promote precision symptom care among breast cancer survivors.

Footnotes

Author Contributions: HL contributed to conception and design contributed to acquisition, analysis, and interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy JS contributed to conception and design contributed to interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy CP contributed to conception contributed to interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy OD contributed to conception contributed to acquisition drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy YX contributed to design contributed to analysis drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy JS contributed to design contributed to interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy AD contributed to conception and design contributed to interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. Research reported in this study was supported by the National Center for Complementary and Integrative Health of the National Institutes of Health under Award Number K24AT011995-01.

ORCID iD

Hongjin Li https://orcid.org/0000-0002-5466-1192

References

  1. Agus A., Planchais J., Sokol H. (2018). gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host & Microbe, 23(6), 716–724. 10.1016/j.chom.2018.05.003 [DOI] [PubMed] [Google Scholar]
  2. Al-Bashaireh A. M., Khraisat O., Alnazly E. K., Aldiqs M. (2021). Inflammatory markers, metabolic profile, and psychoneurological symptoms in women with breast cancer: A literature review. Cureus, 13(11), e19953. 10.7759/cureus.19953 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Amtmann D., Cook K. F., Jensen M. P., Chen W. H., Choi S., Revicki D., Cella D., Rothrock N., Keefe F., Callahan L., Lai J. S. (2010). Development of a PROMIS item bank to measure pain interference. Pain, 150(1), 173–182. 10.1016/j.pain.2010.04.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Beger R. D., Dunn W., Schmidt M. A., Gross S. S., Kirwan J. A., Cascante M., Brennan L., Wishart D. S., Oresic M., Hankemeier T., Broadhurst D. I., Lane A. N., Suhre K., Kastenmüller G., Sumner S. J., Thiele I., Fiehn O., Kaddurah-Daouk R. (2016). Metabolomics enables precision medicine: “A white paper, community perspective”. Metabolomics, 12(10), 149. 10.1007/s11306-016-1094-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bellg A. J., Borrelli B., Resnick B., Hecht J., Minicucci D. S., Ory M., Ogedegbe G., Orwig D., Ernst D., Czajkowski S., Treatment Fidelity Workgroup of the, N. I. H. B. C. C. (2004). Enhancing treatment fidelity in health behavior change studies: Best practices and recommendations from the NIH behavior change Consortium. Health Psychol, 23(5), 443–451. 10.1037/0278-6133.23.5.443 [DOI] [PubMed] [Google Scholar]
  6. Benjamini Y., Hochberg Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. 10.1111/j.2517-6161.1995.tb02031.x [DOI] [Google Scholar]
  7. Bjerkeset E., Röhrl K., Schou-Bredal I. (2020). Symptom cluster of pain, fatigue, and psychological distress in breast cancer survivors: Prevalence and characteristics. Breast Cancer Research and Treatment, 180(1), 63–71. 10.1007/s10549-020-05522-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Borrelli B., Sepinwall D., Ernst D., Bellg A. J., Czajkowski S., Breger R., DeFrancesco C., Levesque C., Sharp D. L., Ogedegbe G., Resnick B., Orwig D. (2005). A new tool to assess treatment fidelity and evaluation of treatment fidelity across 10 years of health behavior research. J Consult Clin Psychol, 73(5), 852–860. 10.1037/0022-006X.73.5.852 [DOI] [PubMed] [Google Scholar]
  9. Brauer J. A., El Sehamy A., Metz J. M., Mao J. J. (2010). Complementary and alternative medicine and supportive care at leading cancer centers: A systematic analysis of websites. The Journal of Alternative and Complementary Medicine, 16(2), 183–186. 10.1089/acm.2009.0354 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cella D., Riley W., Stone A., Rothrock N., Reeve B., Yount S., Amtmann D., Bode R., Buysse D., Choi S., Cook K., Devellis R., DeWalt D., Fries J. F., Gershon R., Hahn E. A., Lai J. S., Pilkonis P., Revicki D., Group, P. C. (2010). The patient-reported Outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J Clin Epidemiol, 63(11), 1179–1194. 10.1016/j.jclinepi.2010.04.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cessna J. M., Jim H. S., Sutton S. K., Asvat Y., Small B. J., Salsman J. M., Zachariah B., Fishman M., Field T., Fernandez H. (2016). Evaluation of the psychometric properties of the PROMIS cancer fatigue short form with cancer patients. Journal of Psychosomatic Research, 81, 9–13. 10.1016/j.jpsychores.2015.12.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chou Y.-J., Kober K. M., Kuo C.-H., Yeh K.-H., Kuo T.-C., Tseng Y. J., Miaskowski C., Liang J.-T., Shun S.-C. (2021). A pilot study of metabolomic pathways associated with fatigue in survivors of colorectal cancer. Biological Research for Nursing, 23(1), 42–49. 10.1177/1099800420942586 [DOI] [PubMed] [Google Scholar]
  13. Doong S.-H., Dhruva A., Dunn L. B., West C., Paul S. M., Cooper B. A., Elboim C., Abrams G., Merriman J. D., Langford D. J. (2015). Associations between cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression in patients prior to breast cancer surgery. Biological Research for Nursing, 17(3), 237–247. 10.1177/1099800414550394 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dorland H., Abma F., Van Zon S., Stewart R., Amick B., Ranchor A., Roelen C., Bültmann U. (2018). Fatigue and depressive symptoms improve but remain negatively related to work functioning over 18 months after return to work in cancer patients. Journal of Cancer Survivorship, 12(3), 371–378. 10.1007/s11764-018-0676-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Eaton L. H., Doorenbos A. Z., Schmitz K. L., Carpenter K. M., McGregor B. A. (2011). Establishing treatment fidelity in a web-based behavioral intervention study. Nursing Research, 60(6), 430–435. 10.1097/NNR.0b013e31823386aa [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Garland S. N., Xie S. X., Li Q., Seluzicki C., Basal C., Mao J. J. (2017). Comparative effectiveness of electro-acupuncture versus gabapentin for sleep disturbances in breast cancer survivors with hot flashes: A randomized trial. Menopause: the Journal of the North American Menopause Society, 24(5), 517–523. 10.1097/gme.0000000000000779 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Ghaemi F., Azizi H., Sahebkar M. S., Mehraban Moghadam S., Jarahi L., Safarian M., Bahrami Taghanaki H. R., Zahedi Avval F. (2021). Effects of acupuncture on the glutathione system in overweight and obese individuals. Journal of Nutrition, Fasting and Health, 9(3), 196–201. 10.22038/jnfh.2021.54314.1310 [DOI] [Google Scholar]
  18. Giovanni M. (1989). The foundations of Chinese medicine: A comprehensive text for acupuncturists and herbalists. Edinburgh, UK: Churchill Livingstone, 219-268. [Google Scholar]
  19. Hershman D. L., Unger J. M., Greenlee H., Capodice J. L., Lew D. L., Darke A. K., Kengla A. T., Melnik M. K., Jorgensen C. W., Kreisle W. H., Minasian L. M., Fisch M. J., Henry N. L., Crew K. D. (2018). effect of acupuncture vs sham acupuncture or waitlist control on joint pain related to aromatase inhibitors among women with early-stage breast cancer: A randomized clinical trial. Jsls: Journal of the Society of Laparoendoscopic Surgeons, 320(2), 167–176. 10.1001/jama.2018.8907 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kim H.-J., Barsevick A. M., Fang C. Y., Miaskowski C. (2012). Common biological pathways underlying the psychoneurological symptom cluster in cancer patients. Cancer Nursing, 35(6), E1–E20. 10.1097/NCC.0b013e318233a811 [DOI] [PubMed] [Google Scholar]
  21. Kwekkeboom K. L., Tostrud L., Costanzo E., Coe C. L., Serlin R. C., Ward S. E., Zhang Y. (2018). The role of inflammation in the pain, fatigue, and sleep disturbance symptom cluster in advanced cancer. Journal of Pain and Symptom Management, 55(5), 1286–1295. 10.1016/j.jpainsymman.2018.01.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kwekkeboom K. L., Wieben A., Stevens J., Tostrud L., Montgomery K. (2020). Guideline-recommended symptom management strategies that cross over two or more cancer symptoms. Oncology Nursing Forum, 45(5), 498-511. 10.1188/20.ONF.498-511. [DOI] [PubMed] [Google Scholar]
  23. Langford D. J., Paul S. M., Cooper B., Kober K. M., Mastick J., Melisko M., Levine J. D., Wright F., Hammer M. J., Cartwright F., Lee K. A., Aouizerat B. E., Miaskowski C. (2016). Comparison of subgroups of breast cancer patients on pain and co-occurring symptoms following chemotherapy. Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer, 24(2),605–614. 10.1007/s00520-015-2819-1 [DOI] [PubMed] [Google Scholar]
  24. Li H., Schlaeger J. M., Jang M. K., Lin Y., Park C., Liu T., Sun M., Doorenbos A. Z. (2021). Acupuncture improves multiple treatment-related symptoms in breast cancer survivors: A systematic review and meta-analysis. The Journal of Alternative and Complementary Medicine, 27(12), 1084–1097. 10.1089/acm.2021.0133 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Li W., Sun M., Yin X., Lao L., Kuang Z., Xu S. (2020). The effect of acupuncture on depression and its correlation with metabolic alterations: A randomized controlled trial. Medicine, 99(43), e22752. 10.1097/MD.0000000000022752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lozano F. (2014). Basic theories of traditional Chinese medicine. In Acupuncture for pain management (pp. 13-43). New York, NY: Springer. [Google Scholar]
  27. MacPherson H., Altman D. G., Hammerschlag R., Youping L., Taixiang W., White A., Moher D. (2010). Revised standards for reporting interventions in clinical trials of acupuncture (STRICTA): Extending the CONSORT statement. The Journal of Alternative and Complementary Medicine, 16(10), ST–14. 10.1136/aim.2009.001370 [DOI] [PubMed] [Google Scholar]
  28. Mannes Z. L., Stohl M., Fink D. S., Olfson M., Keyes K. M., Martins S. S., Gradus J. L., Saxon A. J., Maynard C., Livne O. (2022). Non-pharmacological treatment for chronic pain in US veterans treated within the veterans health administration: Implications for expansion in US healthcare systems. Journal of General Internal Medicine, 1–10. 10.1007/s11606-021-07370-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Mardinoglu A., Shoaie S., Bergentall M., Ghaffari P., Zhang C., Larsson E., Bäckhed F., Nielsen J. (2015). The gut microbiota modulates host amino acid and glutathione metabolism in mice. Molecular Systems Biology, 11(10), 834. 10.15252/msb.20156487 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Morris G., Anderson G., Dean O., Berk M., Galecki P., Martin-Subero M., Maes M. (2014). The glutathione system: A new drug target in neuroimmune disorders. Molecular Neurobiology, 50(3), 1059–1084. 10.1007/s12035-014-8705-x [DOI] [PubMed] [Google Scholar]
  31. Paice J. A., Portenoy R., Lacchetti C., Campbell T., Cheville A., Citron M., Constine L. S., Cooper A., Glare P., Keefe F. (2016). Management of chronic pain in survivors of adult cancers: American society of clinical oncology clinical practice guideline. Journal of Clinical Oncology, 34(27), 3325–3345. 10.1200/JCO.2016.68.5206 [DOI] [PubMed] [Google Scholar]
  32. Pilkonis P. A., Choi S. W., Reise S. P., Stover A. M., Riley W. T., Cella D., Group P. C. (2011). Item banks for measuring emotional distress from the patient-reported Outcomes measurement information system (PROMIS(R)): Depression, anxiety, and anger. Assessment, 18(3), 263–283. 10.1177/1073191111411667 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Quach C. W., Langer M. M., Chen R. C., Thissen D., Usinger D. S., Emerson M. A., Reeve B. B. (2016). Reliability and validity of PROMIS measures administered by telephone interview in a longitudinal localized prostate cancer study. Quality of Life Research: an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 25(11), 2811–2823. 10.1007/s11136-016-1325-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Savushkina O., Boksha I., Prokhorova T., Tereshkina E., Safarova T., Yakovleva O., Kornilov V., Shipilova E., Vorobyeva E., Burbaeva G. (2022). Activity of energy, glutamate, and glutathione metabolism enzymes in blood cells of elderly patients with depression. The European Journal of Psychiatry, 36(4), 288-293. 10.17116/jnevro2022122081136. [DOI] [Google Scholar]
  35. Schumacher. (2021). Ongoing symptoms experienced by low-risk breast cancer survivors following active treatment. In: American Society of Breast Surgeons Annual MeetingAbstract 238. [Google Scholar]
  36. Smith C., Carmady B., Thornton C., Perz J., Ussher J. M. (2013). The effect of acupuncture on post-cancer fatigue and well-being for women recovering from breast cancer: A pilot randomised controlled trial. Acupuncture in Medicine: Journal of the British Medical Acupuncture Society, 31(1), 9–15. 10.1136/acupmed-2012-010228 [DOI] [PubMed] [Google Scholar]
  37. Starkweather A., Kelly D. L., Thacker L., Wright M. L., Jackson-Cook C. K., Lyon D. E. (2017). Relationships among psychoneurological symptoms and levels of C-reactive protein over 2 years in women with early-stage breast cancer. Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer, 25(1), 167–176. 10.1007/s00520-016-3400-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Su X.-T., Wang L., Ma S.-M., Cao Y., Yang N.-N., Lin L.-L., Fisher M., Yang J.-W., Liu C.-Z. (2020). Mechanisms of acupuncture in the regulation of oxidative stress in treating ischemic stroke. Oxidative medicine and cellular longevity. 10.1155/2020/7875396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Weljie A. M., Bondareva A., Zang P., Jirik F. R. (2011). 1 H NMR metabolomics identification of markers of hypoxia-induced metabolic shifts in a breast cancer model system. Journal of Biomolecular NMR, 49(3), 185–193. 10.1007/s10858-011-9486-4 [DOI] [PubMed] [Google Scholar]
  40. Wilkie D. J., Huang H.-Y., Berry D. L., Schwartz A., Lin Y.-C., Ko N.-Y., Chen A., Gralow J., Lindsley S. K., Fitzgibbon D. (2001). Cancer symptom control: Feasibility of a tailored, interactive computerized program for patients. Family & Community Health, 24(3), 48–62. 10.1097/00003727-200110000-00007 [DOI] [PubMed] [Google Scholar]
  41. Wilkie D. J., Judge M. K. M., Berry D. L., Dell J., Zong S., Gilespie R. (2003). Usability of a computerized PAINReportIt in the general public with pain and people with cancer pain. Journal of Pain and Symptom Management, 25(3), 213–224. 10.1016/s0885-3924(02)00638-3 [DOI] [PubMed] [Google Scholar]
  42. Wu Y. Y., Jiang Y. L., He X. F., Zhao X. Y., Shao X. M., Sun J., Shen Z., Shou S. Y., Wei J. J., Ye J. Y. (2017). 5-HT in the dorsal raphe nucleus is involved in the effects of 100-Hz electro-acupuncture on the pain-depression dyad in rats. Experimental and Therapeutic Medicine, 14(1), 107–114. 10.3892/etm.2017.4479 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Xia Y., Sun J. (2022). Statistical data analysis of microbiomes and metabolomics. American Chemical Society. 10.1021/acsinfocus.7e5035 [DOI] [Google Scholar]
  44. Xiao B., Liu Z. H. (2014). Efficacy on depression in breast cancer treated with acupuncture and auricular acupressure. Zhongguo Zhen Jiu, 34(10), 956–960. [PubMed] [Google Scholar]
  45. Yu L., Buysse D. J., Germain A., Moul D. E., Stover A., Dodds N. E., Johnston K. L., Pilkonis P. A. (2011). Development of short forms from the PROMIS sleep disturbance and Sleep-Related Impairment item banks. Behavioral Sleep Medicine, 10(1), 6–24. 10.1080/15402002.2012.636266 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zhang A., Yan G., Sun H., Cheng W., Meng X., Liu L., Xie N., Wang X. (2016). Deciphering the biological effects of acupuncture treatment modulating multiple metabolism pathways. Scientific Reports, 6(1), 1–7. 10.1038/srep19942 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Zhang C., Chor W. (2021). Realizing holism in traditional Chinese medicine (TCM) consultations through the voice of TCM (VOTCM): An interactional analytical approach. Health Communication, 1–10. 10.1080/10410236.2021.1950292 [DOI] [PubMed] [Google Scholar]
  48. Zhou W., Benharash P. (2014). Effects and mechanisms of acupuncture based on the principle of meridians. The Journal of the Arkansas Medical Society, 7(4), 190–193. 10.1016/j.jams.2014.02.007 [DOI] [PubMed] [Google Scholar]

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