• 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br ARTICLE IN PRESS br Knisely et al br


    Knisely et al
    During each of the study visits, the women completed the study questionnaires and provided information on new and ongoing treatments. Over the course of the study, patients’ medical records were reviewed for disease and treatment information.
    Characterization of the Persistent Arm Pain Phenotype
    Characterization of the arm pain phenotype was described previously.43 In summary, GMM with robust maximum likelihood estimation was carried out to iden-tify latent LY-294002 of patients with distinct persistent arm pain trajectories. Arm/shoulder pain scores were assessed monthly for 6 months after breast cancer sur-gery. Patients who reported no pain in their affected arm/shoulder for all 6 assessments (n = 164 [41.6%]) were not included in the GMM analysis. These women comprised the no pain group for the current analyses. For the remaining 230 women, 6 ratings of worst arm/ shoulder pain were used in the GMM analysis to assign each patient into a latent class. The GMM analysis was performed using Mplus 6.1.45
    Gene and Single Nucleotide Polymorphism Selection
    Fifteen candidate genes involved in various aspects of catecholaminergic and serotonergic neurotransmission were evaluated. Genes involved in catecholaminergic neu-rotransmission included: adrenergic alpha-1D receptor (ADRA1D), adrenergic alpha-2A receptor (ADRA2A), LY-294002 adrenergic beta-2 receptor (ADRB2), adrenergic beta-3 receptor (ADRB3), adrenergic beta receptor kinase 2 (ADRBK2), COMT, solute-like carrier (SLC) family 6 (neuro-transmitter transporter, noradrenaline) member 2 (SLC6A2), SLC family 6 (neurotransmitter transporter, dopamine) member 3 (SLC6A3), and tyrosine hydroxylase (TH). Genes involved in serotonergic neurotransmission included 5-hydroxytrypatimeine receptor (HTR)1A (HTR1A), HTR1B, HTR2A, HTR3A; SLC family 6 (neurotrans-mitter transporter, serotonin) member 4 (SLC6A4); and tryptophan hydroxylase 2 (TPH2). All genes were identi-fied according to the approved symbol stored in the Human Genome Organization Gene Nomenclature Com-mittee database ( A combination of tagging single nucleotide polymorphisms (SNPs) and lit-erature driven SNPs for these candidate genes were selected for analysis (Supplementary Table 1). Tagging SNPs were required to be common (ie, defined as having a minor allele frequency of ≥.05) in public databases.
    Blood Collection and Genotype
    Genotyping was completed on 310 women. DNA was extracted from peripheral blood mononuclear cells using the PUREGene DNA Isolation System (Invitrogen, Carlsbad, California). DNA samples were quantitated with a Nanodrop Spectrophotometer (ND-1000; Nanodrop Products, Wilmington, Delaware) and normalized to a 
    The Journal of Pain 3
    concentration of 50 ng/mL (diluted in 10 mmol/L Tris/ 1 mmol/L EDTA). Samples were genotyped using the Golden Gate genotyping platform (Illumina, San Diego, California) and processed using GenomeStudio (Illumina). Two blinded reviewers visually inspected signal intensity profiles and resulting genotype calls for each SNP. SNPs with call rates of <95% or Hardy-Weinberg estimates with P values of <.001 were excluded. A total of 126 SNPs among the 15 candidate genes passed all the quality con-trol filters and were included in the genetic analyses (Sup-plementary Table 1). Localization of SNPs on the human genome was performed using the GRCh37/hg19 human reference assembly. Regional annotations were identified using the University of California Santa Cruz Human Genome Browser GRCh37/hg19 ( cgi-bin/hgTracks?db=hg19).
    Statistical Analyses
    Descriptive statistics and frequency distributions for the no arm pain, mild arm pain, and moderate arm pain clas-ses were generated for demographic and clinical charac-teristics. Using SPSS version 24 (IBM, Armonk, New York), independent sample t-tests, Mann-Whitney U tests, x2 tests, and Fisher’s exact tests were used to evaluate for dif-ferences in demographic and clinical characteristics between the no arm pain and the mild arm pain and between the no arm pain and the moderate arm pain clas-ses. StataSE version 14 (StataCorp, College Station, Texas) was used to conduct the logistic regression analyses to evaluate for associations between phenotypic characteris-tics and pain group membership. All phenotypic character-istics that were identified in the bivariate analyses as being different between the no arm pain and each of the other 2 persistent arm pain classes were evaluated for inclusion in the multivariate analysis. A backward stepwise approach was used to create a parsimonious model. Only predictors with a P value of <.05 were retained in the final model. These predictors were used in each of the logistic regression analyses to evaluate for associations between genotype and pain group membership.