Associations Between Neurotransmitter Genes and Fatigue and Energy Levels in Women Following Breast Cancer Surgery

Patients and Settings

The study methods are described in detail elsewhere.^25–27 In brief, patients were recruited from 2004 to 2008, from Breast Care Centers located in a Comprehensive Cancer Center, two public hospitals, and four community practices. Patients were eligible to participate if they: were adult women (≥18 years) who were scheduled to undergo unilateral breast cancer surgery; were able to read, write, and understand English; agreed to participate; and gave written informed consent. Patients were excluded if they were having breast cancer surgery on both breasts and/or had distant metastasis at the time of diagnosis.

Instruments

The demographic questionnaire obtained information on age, marital status, education, ethnicity, employment status, and living situation. Functional status was evaluated using the Karnofsky Performance Status (KPS) scale.²⁸ The number and impact of comorbid conditions was evaluated using the Self-Administered Comorbidity Questionnaire (SCQ).²⁹

The LFS consists of 18 items designed to assess physical fatigue and energy.²⁴ Each item was rated on a 0 to 10 numeric rating scale (NRS). Total fatigue and energy scores were calculated as the mean of the 13 fatigue items and the 5 energy items, with higher scores indicating greater fatigue severity and higher levels of energy. Patients were asked to rate each item based on how they felt “right now”. Cutoff scores of ≥4.4 and ≤4.8 indicate clinically meaningful levels of fatigue severity and low levels of energy.³ The LFS has well established validity and reliability.^24,30 Cronbach’s alphas for fatigue and energy scales were .96 and .93, respectively.

Study Procedures

The study was approved by the Committee on Human Research at the University of California, San Francisco and by the Institutional Review Boards at each of the study sites. During the patients’ preoperative visit, a clinician explained the study and determined patients’ willingness to participate. The research nurse met with interested women, determined eligibility, and obtained written informed consent prior to surgery. After obtaining consent, patients completed the enrollment questionnaires an average of 4 days prior to surgery. Patients completed the LFS at enrollment and monthly for 6 months (i.e., 7 assessments). Medical records were reviewed for disease and treatment information.

Genomic analyses

Statistical Analyses for the Phenotypic Data

Data were analyzed using SPSS version 22³⁷ and STATA Version 13.³⁸ As described previously,¹⁶ unconditional GMM with robust maximum likelihood estimation was carried out to identify latent classes with distinct fatigue and energy trajectories using Mplus Version 5.21.^39–41 Descriptive statistics and frequency distributions were generated for sample characteristics. Independent sample t-tests, Mann-Whitney U tests, and Chi square analyses were used to evaluate for differences in demographic and clinical characteristics between the two latent classes for fatigue and energy. A p-value of <0.05 was considered statistically significant.

Statistical Analyses for the Genetic Data

The genomic analyses are described in detail elsewhere.¹⁶ In brief, allele and genotype frequencies were determined by gene counting. Hardy-Weinberg equilibrium was assessed by the Chi-square or Fisher Exact tests. Measures of linkage disequilibrium ((LD), i.e., D′ and r²) were computed using Haploview 4.2. Haplotypes were constructed using PHASE version 2.1.⁴² Only inferred haplotypes that occurred with a frequency estimate of ≥15% were included in the association analyses, assuming a dosage model.

Ancestry informative markers (AIMs) were used to minimize confounding due to population stratification.^43–45 Homogeneity in ancestry among patients was verified by principal component analysis,⁴⁶ using HelixTree (GoldenHelix, Bozeman, MT). The first three PCs were used in all of the logistic regression models.

For association tests, three genetic models were assessed for each SNP: additive, dominant, and recessive. The genetic model that best fit the data, by maximizing the significance of the p-value was selected for each SNP. Logistic regression analysis, that controlled for significant covariates, as well as genomic estimates of (i.e., AIMs) and self-reported race/ethnicity, was used to evaluate the associations between genotype and Higher Fatigue and Lower Energy class memberships. Only those genetic associations identified as significant from the bivariate analyses were evaluated in the multivariate analyses. A backwards stepwise approach was used to create a parsimonious model. Except for race/ethnicity, only predictors with a p-value of <.05 were retained in the final model.

As was done in our previous studies,^9,47–51 based on the recommendations in the literature,^52,53 as well as the implementation of rigorous quality controls for genomic data, the non-independence of SNPs/haplotypes in LD, and the exploratory nature of the analyses, adjustments were not made for multiple testing. In addition, significant SNPs identified in the bivariate analyses were evaluated further using logistic regression analyses that controlled for differences in phenotypic characteristics, potential confounding due to population stratification, and variations in other SNPs/haplotypes within the same gene. Only those SNPs that remained significant were included in the final presentation of the results. Therefore, the significant independent associations reported are unlikely to be due solely to chance. Unadjusted associations, for all of the SNPs evaluated, are found in Supplementary Table 1, to allow for subsequent comparisons and meta-analyses.

Differences in Demographic and Clinical Characteristics between the Fatigue Classes

Differences between the two fatigue classes are listed in Table 1. Patients in the Higher Fatigue class were significantly younger, had more years of education, a lower KPS score, a higher SCQ score, a higher number of lymph nodes removed and a higher fatigue severity score at enrollment. A larger percentage of patients in the Higher Fatigue class had received neoadjuvant CTX and had received CTX during the first 6 months after breast cancer surgery.

Candidate Gene Analyses for Fatigue

Genotype distributions differed between the Lower and Higher Fatigue classes for: 2 SNPs and one haplotype in ADRB2; 3 SNPs in BDNF; 1 SNP in COMT; 1 SNP in CYP3A4; 1 SNP in GALR1; 1 SNP in GCH1; 5 SNPs and 2 haplotypes in NOS1; 1 SNP and 1 haplotype in NPY1R; 1 SNP and 1 haplotype in SLC6A1; 2 SNPs and 1 haplotype in SLC6A2; 1 SNP in SLC6A3; and 2 SNPs and 1 haplotype in TAC1, as well as in the 5HTTLPR + rs25521 haplotype in the SLC6A4 gene.

Regression Analyses for Fatigue

In these regression analyses that included genomic estimates of and self-reported race/ethnicity, the only phenotypic characteristics that remained significant in the multivariate model were: age, KPS score, SCQ score, and receipt of CTX within six months after breast cancer surgery. Eleven gene loci remained significantly associated with fatigue class membership in the regression analyses (Table 2).

For ADRB2 rs1042718, carrying two doses of the rare A allele was associated with a 87% lower odds of belonging to the Higher Fatigue class (Figure 1A). For BDNF rs6265, carrying one or two doses of the rare A allele was associated with a 50% lower odds of belonging to the Higher Fatigue class (Figure 1B). For COMT rs9332377, carrying one or two doses of the rare C allele was associated with a 52% lower odds of belonging to the Higher Fatigue class (Figure 1C). For CYP3A4 rs4646437, carrying one or two doses of the rare T allele was associated with a 52% lower odds of belonging to the Higher Fatigue class (Figure 1D). For GALR1 rs949060, carrying two doses of the rare C allele was associated with a 2.46-fold higher odds of belonging to the Higher Fatigue class (Figure 1E). For GCH1 rs3783642, carrying one or two doses of the rare C allele was associated with a 53% lower odds of belonging to the Higher Fatigue class (Figure 1F).

For NOS1, two SNPs were associated with membership in the Higher Fatigue class. In the regression analysis, including both SNPs, for NOS1 rs9658498, carrying two doses of the rare C allele was associated with a 55% lower odds of belonging to the Higher Fatigue class (Figure 2A). In the same regression analysis, for NOS1 rs2293052, carrying two doses of the rare T allele was associated with a 4.58-fold higher odds of belonging to the Higher Fatigue class (Figure 2B). For NPY1R HapA04, that is composed of alleles at two SNPs (i.e., rs9764 [common T allele], and rs7687423 [common G allele]), each additional dose of NPY1R HapA04 was associated with a 1.77-fold higher odds of belonging to the Higher Fatigue class (Figure 3). For SLC6A2 rs17841327, carrying two doses of the rare A allele was associated with a 10.31-fold higher odds of belonging to the Higher Fatigue class (Figure 2C). For the 5-HTTLPR + rs25531 polymorphism in SLC6A4, carrying one or two doses of the L_A allele was associated with a 47% lower odds of belonging to the Higher Fatigue class (Figure 2D).

Differences in Demographic and Clinical Characteristics between the Energy Classes

Differences between the two energy classes are listed in Table 3. Patients in the Lower Energy class had a lower KPS score, a higher SCQ score, and a lower mean energy score at enrollment. In addition, a higher percentage of patients with more advanced disease were in the Lower Energy class.

Candidate Gene Analyses for Energy

Genotype distributions differed between the Higher Energy and Lower Energy classes for: 1 SNP in COMT; 2 SNPs in HTR2A; 1 SNP in NOS1; 1 SNP in NOS2A; 4 SNPs and 3 haplotypes in SLC6A1; 4 SNPs in SLC6A2; 1 SNP in SLC6A3; 3 SNPs and 1 haplotype in SLC6A4; 1 SNP in TAC1; and 1 SNP in TACR1.

Regression Analyses for Energy

In these regression analyses that included genomic estimates of and self-reported race/ethnicity, the phenotypic characteristics that remained significant in the multivariate model were: KPS score and receipt of CTX within six months after breast cancer surgery. Seven gene loci remained significantly associated with energy class membership in the multivariate logistic regression analyses (Table 4).

For NOS1 rs471871, carrying two doses of the rare T allele was associated with a 72% lower odds of belonging to the Lower Energy class (Figure 4A). For SLC6A1, one SNP and one haplotype were associated with membership in the Lower Energy class. For SLC6A1 rs2675163, carrying one or two doses of the rare C allele was associated with a 1.85-fold higher odds of belonging to the Lower Energy class (Figure 4B). In the same regression analysis, for SLC6A1 HapD01, that is composed of alleles at three SNPs (i.e., rs10514669 [common C allele], rs2697138 [common C allele], and rs1062246 [common A allele]), each additional dose of SLC6A1 HapD01 was associated with a 40% lower odds of belonging to the Lower Energy class (Figure 5). For SLC6A2 rs36027, each additional dose of the rare G allele was associated with a 41% lower odds of belonging to the Lower Energy class (Figure 4C). For SLC6A3 rs37022, carrying two doses of the rare A allele was associated with a 9.75-fold higher odds of belonging to the Lower Energy class (Figure 4D). For SLC6A4 rs2020942, carrying two doses of the rare A allele was associated with a 64% lower odds of belonging to the Lower Energy class (Figure 4E). For TAC1 rs2072100, carrying two doses of the rare G allele was associated with a 2.11-fold higher odds of belonging to the Lower Energy class (Figure 4F).

Fatigue Polymorphisms

The ADRB2 receptor is part of the G-protein-coupled receptor family that influences sympathetic nervous system responses, as well as plays a role in the regulation of lipid metabolism. Polymorphisms in ADRB2 are associated with bronchodilation; insulin secretion; gluconeogenesis and glycogenolysis in skeletal muscle; as well as increased cardiac output; arterial dilation; and lipolysis.⁵⁴ Sarpeshkar and Bentley hypothesized that alterations in this gene may be responsible for enhanced aerobic capacity and delayed exercise-induced fatigue.⁵⁴ In addition, ADRB2 receptor stimulation inhibits production of type 1 pro-inflammatory cytokines⁵⁵ and under-expression of ADRB2 receptors is associated with chronic fatigue syndrome.⁵⁶

In our study, patients who were homozygous for the rare A allele for ADRB2 rs1042718 had a 87% lower odds of belonging to the Higher Fatigue class. Polymorphisms in ADRB2 rs1042718 produce a silent mutation. While no studies were identified that evaluated associations between this SNP and fatigue, significant associations were found between rs1042718 and enhanced longevity⁵⁷ and negative emotions.⁵⁸ In the negative emotions study,⁵⁸ individuals who were heterozygous or homozygous for the rare allele in rs1042718 were less likely to report feelings of uselessness, loneliness, and anxiety. Given that previous studies of oncology patients found associations between higher levels of psychological distress and fatigue,^59–61 our findings are consistent with those reported by Zheng and colleagues.⁵⁸

COMT is a key enzyme responsible for the metabolism and inactivation of dopamine, norepinephrine, and epinephrine.⁶² Alterations in the COMT gene were associated with fatigue and pain in breast cancer patients through interactions with two stress pathways (i.e., hypothalamic-pituitary-adrenal (HPA) axis, the sympathetic nervous system (SNS)).^62–64 In our study, patients who were heterozygous or homozygous for the rare C allele for COMT rs9332377 had a 52% lower odds of belonging to the Higher Fatigue class. This intronic SNP is located near the 3′ UTR of the COMT gene. While this location suggests that this polymorphism has a regulatory function and may affect COMT expression,⁶⁵ we found no support for nearby regulatory regions when the ENCODE data were reviewed. Only three studies have reported significant associations between COMT rs9332377 and clinical phenotypes (i.e., hearing loss,⁶⁶ suicidal ideation,⁶⁷ nicotine dependence⁶⁵). No studies have evaluated for associations between COMT rs9332377 and fatigue. However,, in the study of suicidal ideation,⁶⁷ individuals who were homozygous for the rare C allele of COMT rs9332377 reported lower irritability scores on the Questionnaire for Measuring Factors of Aggression. This finding supports our association between rs9332377 and decreased fatigue when one considers COMT’s role in the manifestation of emotions, a possible marker for chronic fatigue syndrome.⁶⁸

BDNF is a neural growth factor found throughout the central nervous system (CNS). BDNF is associated with overall brain health because it plays a role in the promotion of neurogenesis, neuroprotection, mental performance, and cognitive function.⁶⁹ Altered BDNF levels are associated with fibromyalgia syndrome,⁷⁰ chronic fatigue syndrome,⁷¹ and depression.⁷²

BDNF rs6265 is a missense mutation that results in a non-synonymous conservative change in the amino acid sequence from valine (Val) to methionine (Met). In two studies,^72,73 decreases in serum BDNF levels were associated with the Met allele. In our study, being heterozygous or homozygous for the rare allele was associated with a reduction in the odds of belonging to the Higher Fatigue class. One might hypothesize that lower levels of BDNF would be associated with membership in the Higher Fatigue class given that lower levels of BDNF were associated with depression⁷² and chronic fatigue syndrome.⁷¹ However, findings regarding changes in serum levels of BDNF associated with the Met allele are inconsistent.⁷⁴ In addition, the effect of the Met allele on BDNF levels in the brain, where it may play a greater role in the perception of fatigue, remains unknown.

The CYP3A4 gene is a part of the cytochrome P450 superfamily. Cytochrome P450 enzymes are responsible for catalyzing multiple reactions involved in lipid synthesis and drug metabolism. These enzymes are responsible for the metabolism of approximately one-third of anticancer drugs.⁷⁵ The rs4646437 SNP is located in intron 7 of CYP3A4. While no studies evaluated for associations between CYP3A4 rs4646437 and fatigue, in one study,⁷⁶ an association was found between CYP3A4 rs4646437 and in vitro CYP3A expression and activity. In this study, women who carried the rare T allele of rs4646437 had higher expression and activity of the CYP3A4 enzyme. Considering CYP3A4’s role in metabolizing anti-cancer drugs, one can hypothesize that women who are able to more effectively metabolize CTX would be less likely to experience higher levels of fatigue. This hypothesis is supported by our findings that being heterozygous or homozygous for rare T allele for rs4646437 was associated with a 52% lower odds of belonging to the Higher Fatigue class.

Galanin, a neuropeptide found throughout the CNS, has an inhibitory effect on multiple neurotransmitters.⁷⁷ Polymorphisms in the galanin gene are associated with eating disorders,⁷⁸ cancer,⁷⁹ Alzheimer’s disease,^80,81 depression, and anxiety.⁷⁷ Within the CNS, the functional effects of galanin are mediated by three G-protein-coupled receptor subtypes, including GALR1. The GAL1 receptor has an inhibitory effect on adenylate cyclase through coupling with the G proteins Gi/Go. This inhibition affects ATP metabolism and plays an important role in cellular energy pathways.⁸¹ Of note, Staines⁸² hypothesized that dysfunctions in G protein-coupled receptors (e.g., GALR1) contribute to the development of fatigue. In our study, patients who homozygous for the rare C allele for GALR1 rs949060 had a 2.46-fold higher odds of belonging to the Higher Fatigue class. GALR1 rs949060 is located on chromosome 18 approximately 3 kilobases upstream of the GALR1 gene in the promoter region. However, no nearby regulatory element was identified in the ENCODE data.

GCH1 is the rate-limiting enzyme involved in the synthesis of tetrahydrobiopterin (BH4). BH4 plays a role in nitric oxide (NO) production and hydroxylation of aromatic amino acids. Polymorphisms in GCH1 are associated with pain,⁸³ altered cognitive performance,⁸⁴ and dopa-responsive dystonia.⁸⁵ In our study, being heterozygous or homozygous for the rare C allele for GCH1 rs3783642 was associated with a 53% lower odds of belonging to the Higher Fatigue class. While no studies reported on GCH1 rs3783642, in one study,⁸⁶ a protective association between other polymorphisms in GCH1 and fibromyalgia syndrome. This locus resides in putative CCCTC-binding factor (CTCF) and RAD22 transcription factor binding sites which suggests a possible role in the regulation of GCH1.

NPY1R is part of a family of G protein-coupled receptors that binds NPY. NPY acts in both the CNS and peripheral nervous system (PNS). Peripherally, NPY is a neurotransmitter that is released from sympathetic nerve endings. Centrally, NPY acts on receptors present in those areas of the brain that are involved with emotion.⁸⁷ NPY is involved in sleep regulation, anxiety, memory, pain, and energy homeostasis.^88,89 Alterations in NPY are implicated in chronic fatigue syndrome⁸⁷ and depression.⁹⁰ Alterations in NPY signaling through variations in NPY1R may have an effect on any of the aforementioned processes, including fatigue.

In our study, each additional dose of NPY1R HapA04 was associated with a 1.77-fold higher odds of belonging to the Higher Fatigue class. HapA04 is comprised of a 3-prime UTR SNP (rs9764) and one intronic SNP (rs7687423). Although no studies were identified that reported on NPYR1 HapA04, polymorphisms in rs9764 and rs7687423 were associated with nicotine⁹¹ and methamphetamine⁹² dependence, respectively. No studies were identified that reported on associations with either SNP and fatigue.

Energy Polymorphisms

The SLC6A1 gene encodes for one of the four GABA transporters found in the brain. The role of this transporter is to remove GABA from the synaptic cleft which decreases extracellular levels of GABA. The inhibitory neurotransmitter GABA is important for normal brain function. Based on studies of knockout mice,⁹³ deficiencies in SLC6A1 were associated with depression, reduced aggression, and reduced anxiety. Furthermore, an association was found between polymorphisms in SLC6A1 and anxiety disorders.⁹⁴ In a genome-wide association study,⁹⁴ a SNP in SLC6A1 was associated with symptoms of inattention and hyperactivity in attention-deficit/hyperactivity disorder (ADHD).

In our study, one SNP and one haplotype in the SLC6A1 gene were associated with membership in the Lower Energy class. Being heterozygous or homozygous for the rare C allele of SLC6A1 rs2675163 was associated with a 1.85-fold higher odds of belonging to the Lower Energy class, while each additional dose of SLC6A1 HapD01, that is composed of alleles at three SNPs (i.e., rs10514669, rs2697138, and rs1062246), was associated with a 40% lower odds of belonging to the Lower Energy class. No studies were identified that reported on polymorphisms in any of the SLC6A1 SNPs and energy.

The SLC6A3 gene encodes for a dopamine transporter. The dopamine transporter protein is responsible for re-uptake of dopamine from the synaptic cleft which results in decreased extracellular levels of dopamine.⁹⁶ Decreased levels of dopamine are hypothesized to play a role in the development of central fatigue because of dopamine’s known effects on initiation of movement.⁹⁷ Therefore, alterations in dopaminergic circuits, including its transport receptors, may affect an individual’s energy level and fatigue.

While the majority of the literature on polymorphisms in the SLC6A3 gene has focused on ADHD,^98,99 associations were found between dopaminergic polymorphisms and fatigue,²² as well as decreases in mental energy and sustained attention.¹⁰⁰ In our study, being homozygous for the rare A allele of SLC6A3 rs37022 was associated with a 9.75-fold higher odds of belonging to the Lower Energy class. No studies were identified that reported on polymorphisms in this SNP.

The TAC1 gene encodes for a group of tachykinin peptide hormones (e.g., Substance P) that function as neurotransmitters. Substance P is implicated in fibromyalgia syndrome¹⁰¹ and with fatigue and other negative mood states.¹⁰² Therefore, polymorphisms in tachykinin pathway genes may have an effect on fatigue and energy levels. In our study, being homozygous for the rare G allele of TAC1 rs2072100 was associated with a 2.11-fold higher odds of belonging to the Lower Energy class. While the rs2072100 polymorphism was linked with increased risk for colorectal cancer¹⁰³ and susceptibility to multiple sclerosis,¹⁰⁴ no studies were identified that reported on associations with energy.

Fatigue and Energy Polymorphisms

Three genes (i.e., NOS1, SLC6A2, SL6A4) were associated with latent class membership for both fatigue and energy. NOS1 is part of a group of nitric acid synthases (NOS) responsible for the synthesis of NO. NO mediates vasodilation, neural regulation of skeletal muscle, and neurotransmission.¹⁰⁵ Elevated NO levels are implicated in chronic fatigue syndrome,¹⁰⁶ fatigue in muscular dystrophies,^107,108 and fatigue in post-radiation syndrome.¹⁰⁹ While no studies were identified on associations between NOS polymorphisms and fatigue, other studies found associations between polymorphisms in the NOS1 gene and depression¹¹⁰ and anxiety.¹¹¹

In our study, two SNPs in the NOS1 gene were associated with membership in the Higher Fatigue class. Being homozygous for the rare C allele of rs9658498 was associated with a 55% lower odds of belonging to the Higher Fatigue class, while carrying two doses of the rare T allele of rs2293052 was associated with a 4.58-fold higher odds of belonging to the Higher Fatigue class. No studies were identified that reported on NOS1 rs9658498. However, an association was found between rs2293052 and Parkinson’s disease (PD).¹¹² These results support our findings of an association between this SNP and increased fatigue because similar to the aforementioned fatigue-syndromes, PD is associated with increased NO levels.¹¹³ In addition, fatigue is a common symptom associated with PD¹¹⁴ and may share similar susceptibility gene polymorphisms. A different SNP in the NOS1 gene was associated with energy levels. Being homozygous for the rare T allele of rs471871 was associated with a 72% lower odds of belonging to the Lower Energy class. No studies were identified that reported on NOS1 rs471871.

The SLCA2 gene encodes for the norepinephrine transporter (NET) protein. The NET found at noradrenergic synapses, is responsible for the removal of NE from the synaptic cleft and plays a major role in NE homeostasis.¹¹⁵ Impairments in the NET protein may contribute to the development of fatigue.¹¹⁶ Mutations in the SLCA2 gene are associated with orthostatic intolerance, a syndrome that includes fatigue as a significant symptom.^115,117 In addition, polymorphisms in the SLCA2 gene were associated with major depression, a condition that includes fatigue as a major symptom.¹¹⁸ In our study, being homozygous for the rare A allele of SLC6A2 rs17841327 was associated with a 10.31-fold higher odds of belonging to the Higher Fatigue group. In addition, a different SNP in the SLC6A2 gene was associated with energy levels. Each additional dose of the rare G allele of SLC6A2 rs36027 was associated with a 41% higher odds of belonging to the Lower Energy class. No studies were identified that reported on either SLC6A2 SNP.

The SLC6A4 gene encodes for a membrane protein that is responsible for re-uptake of serotonin from the synaptic cleft. The serotonergic neurotransmitter system is hypothesized to play a role in cancer-related fatigue.^119,120 Serotonin is involved in various human behaviors including sleep, mood, appetite, memory, and learning. Increased levels of serotonin in the brain are hypothesized to contribute to fatigue through its interaction with the HPA axis leading to a sensation of reduce potential to perform physical activity.¹¹⁹ Yamamoto et al.¹²¹ demonstrated a reduced density of serotonin transporters in the rostral subdivision of the anterior cingulate of patients with chronic fatigue syndrome. In addition, an association was found between polymorphisms in the promoter of the SLC6A4 gene and chronic fatigue syndrome.¹²²

In our study, being homozygous for the rare A allele of SLC6A4 rs2020942 was associated with a 64% lower odds of belonging to the Lower Energy class. In addition, carrying one or two doses of the L_A allele for the 5-HTTLPR + rs25531 polymorphism in SLC6A4 was associated with a 47% decrease in the odds of belonging to the Higher Fatigue class. While rs2020942 was linked with obsessive-compulsive symptoms¹²³ and risk for nonsyndromic cleft lip with or without cleft palate,¹²⁴ no studies have reported on associations between SLC6A4 rs2020942 and energy level.

While the functional consequences of SLC6A4 rs2020942 are not known, a considerable amount of research has evaluated the functional consequences of the A>G polymorphism (rs25531) within the L allele of 5-HTTLPR. In this triallelic polymorphism (i.e., S, L_A, and L_G), nearly equivalent expression of the serotonin transporter is observed with the S and L_G alleles and increased transcriptional activity is observed with the L_A allele.^33,35 In addition, other studies found that being homozygous for the L_A allele was associated with increased serotonin transporter binding.^125,126 Since the serotonin transporter modulates the concentration of serotonin at the synaptic cleft, an increase in the transcription of the transporter would decrease the amount of serotonin at the synaptic cleft. In a number of studies, individuals who possessed one or two doses of the low activity alleles (S or L_G) were more likely to develop behavioral disorders (e.g., depression) when they experienced stressful life events.^127,128 Our findings are consistent with these observations in that patients who carried one or two doses of the L_A allele were less likely to be in the Higher Fatigue class. One can hypothesize that patients with the L_A allele have increased transcription of the serotonin transporter which would decrease the concentration of serotonin at the synaptic cleft. The decreased concentration of serotonin would result in decreased levels of fatigue.

Conclusions

Several limitations must be acknowledged. While our sample size was sufficient, additional studies with independent samples are needed to confirm the latent classes as well as the genetic associations. In order to increase the generalizability of these results, women were recruited from 7 different centers and approximately 30% of the patients were ethnic minorities. However, the single diagnosis of breast cancer limits the generalizability of the findings to other cancer diagnoses. Finally, recent studies suggest that diurnal variations in fatigue and energy levels constitute distinct symptom phenotypes.^9,129,130 Future studies need to evaluate for differences in the levels of morning and evening fatigue and decrements in energy levels in women following breast cancer surgery.

The molecular findings from this study support the hypothesis that fatigue and energy are distinct, yet related symptoms. Only three of the 13 genes identified in this study were associated with membership in both the fatigue and energy latent classes. Additional support for this hypothesis comes from recent studies that explored the concepts of fatigue and energy in patients with cancer and their family caregivers^9,47 and in women with HIV.¹³¹ Lerdal et al.¹³¹ concluded that fatigue and energy are distinct constructs and should not be used interchangeably, neither clinically nor in research. Additional studies are needed to determine which common and distinct phenotypic and molecular characteristics are associated with increased fatigue severity and decrements in energy. Findings from these types of studies will provide insights into the mechanisms that underlie one or both of these symptoms and facilitate the development and testing of interventions to decrease fatigue and/or increase energy levels of patients undergoing cancer treatment.