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. Author manuscript; available in PMC: 2017 Oct 1.
Published in final edited form as: Breast. 2016 Jul 22;29:231–240. doi: 10.1016/j.breast.2016.06.023

PRECISION ASSESSMENT OF HETEROGENEITY OF LYMPHEDEMA PHENOTYPE, GENOTYPES AND RISK PREDICTION

Mei R Fu 1, Yvette P Conley 2, Deborah Axelrod 2,3, Amber A Guth 2,3, Gary Yu 1, Jason Fletcher 1, David Zagzag 5
PMCID: PMC5014618  NIHMSID: NIHMS804309  PMID: 27460425

Abstract

Lymphedema following breast cancer surgery is considered to be mainly due to the mechanical injury from surgery. Recent research identified that inflammation-infection and obesity may be the important predictors for lymphedema. The purpose of this exploratory research was to prospectively examine phenotype of arm lymphedema defined by limb volume and lymphedema symptoms in relation to inflammatory genes in women treated for breast cancer. A prospective, descriptive and repeated-measure design using candidate gene association method was used to enroll 140 women at pre-surgery and followed at 4–8 weeks and 12 months post-surgery. Arm lymphedema was determined by a perometer measurement of ≥5% limb volume increase from baseline of pre-surgery. Lymphedema symptom phenotype was evaluated using a reliable and valid instrument. Saliva samples were collected for DNA extraction. Genes known for inflammation were evaluated, including lymphatic specific growth factors (VEGF-C & VEGF-D), cytokines (IL1-a, IL-4, IL6, IL8, IL10, & IL13), and tumor necrosis factor-a (TNF-a). No significant associations were found between arm lymphedema phenotype and any inflammatory genetic variations. IL1-a rs17561 was marginally associated with symptom count phenotype of ≥8 symptoms. IL-4 rs2070874 was significantly associated with phenotype of impaired limb mobility and fluid accumulation. Phenotype of fluid accumulation was significantly associated with IL6 rs1800795, IL4 rs2243250 and IL4 rs2070874. Phenotype of discomfort was significantly associated with VEGF-C rs3775203 and IL13 rs1800925. Precision assessment of heterogeneity of lymphedema phenotype and understanding the biological mechanism of each phenotype through the exploration of inherited genetic susceptibility is essential for finding a cure. Further exploration of investigative intervention in the context of genotype and gene expressions would advance our understanding of heterogeneity of lymphedema phenotype.

Keywords: breast cancer, lymphedema, symptoms, symptom clusters, limb volume, arm lymphedema, heterogeneity of phenotype, genotype, risk prediction, lymphedema symptoms

INTRODUCTION

Lymphedema, an abnormal accumulation of lymph fluid in the ipsilateral body area or upper limb, remains an ongoing major health problem affecting more than 40% of 3.1 million breast cancer survivors in the United States (1–3). The experience of lymphedema has been linked to clinically relevant and detrimental outcomes, such as disability and psychological distress, both of which are known risk factors for breast cancer survivors’ poor quality of life (QOL) and survivorship (4–7). While mechanical injury from cancer treatment (e.g. surgery, lymph node procedure, radiation) is considered the main contributor to the risk of lymphedema, research has also found that inflammation-infection and higher body mass index (BMI>30) are the main predictors of lymphedema (8–10). It remains puzzling that up to 23% of survivors who only had lumpectomy with sentinel lymph node biopsy (SLNB) of 1 or 2 lymph nodes removed have developed lymphedema, while some survivors who had mastectomy with more than 10 lymph nodes removed have not (11–14). It is possible that genetic variations may be one of the important factors that influence breast cancer survivors’ responses to the inflammatory processes and vulnerability to lymphedema, including responses to trauma (surgery and radiation) and triggering factors (infection, burns, minor injuries, higher BMI or obesity). Single nucleic polymorphisms (SNPs) known to influence gene expression and therefore protein levels of inflammatory cytokines and lymphatic specific growth factors have been identified from the non-lymphedema and lymphedema literature (15–24).

Precision assessment of lymphedema phenotype among breast cancer survivors remains a huge challenge in research and clinical practice. Traditionally, lymphedema has been diagnosed by healthcare providers’ observations of swelling. Research focus has been on measuring limb girth, limb volume or limb size to evaluate phenotype of arm swelling (hereafter arm lymphedema) with arbitrarily defined criteria of >2-cm increase in limb girth, >200-mL limb volume, or >5% limb volume and bioimpedance ratio (2,6,25–26). Yet, lymphedema phenotypes may include symptoms related to lymph fluid accumulation (hereafter, lymphedema symptoms), including arm swelling, breast swelling, chest wall swelling, heaviness, firmness, tightness, stiffness, pain, aching, soreness, tenderness, numbness, burning, stabbing, tingling, arm fatigue, arm weakness, and limited movement in shoulder, arm, elbow, wrist and fingers (25–26). More importantly, lymphedema symptom phenotype may indicate an early stage of lymphedema in which changes cannot be detected by current objective measures of limb volume (25–29). There is a critical need to understand heterogeneity of lymphedema phenotype in relation to inflammatory genetic variations to advance precision assessment of lymphedema phenotype and related biological mechanism. The purpose of this exploratory research was to prospectively examine phenotype of arm lymphedema and lymphedema symptoms in relation to inflammatory genes in breast cancer survivors.

MATERIAL AND METHODS

Ethical Consideration

This study was approved by the Institutional Review Board of the study institute in the metropolitan area of New York.

Research Design

We employed a prospective, descriptive and repeated-measure design using candidate gene association method that enabled phase-specific monitoring of lymphedema phenotypes prior to surgery (baseline), at 4–8 weeks and 12 months post-surgery.

Procedures

Researchers were trained for obtaining informed consent and collecting data. Protection of human subjects was ensured by following the guidelines set forth by the Institutional Review Board and successful recruitment procedures used in our prior studies (25,28,30). Written consent to the study was obtained. Procedures for using the perometer and bioimpedance device (25,28,30) as well as collecting saliva samples were followed as recommended by the manufacturers.

Study Participants

Between December 2011 and April 2014, we enrolled 140 women at pre-surgery baseline and followed the participants at 4–8 weeks and 12 months post-surgery. Study participants were over 21 years or older, had a first time diagnosis of breast cancer (Stage I-III), and were scheduled for surgical treatment of lumpectomy or mastectomy, including SLNB, lymph node dissection or axillary lymph node dissection and neoadjuvant or adjuvant therapy (8–10); Women were excluded if they had (1) prior history of lymphedema and breast cancer; (2) renal or heart failure, cardiac pacemaker or defibrillator, artificial limbs or pregnancy.

Phenotype Measures

Demographic & Clinical Information

Demographic and clinical information were collected: breast cancer treatment, stage of disease, cancer location, type of adjuvant therapy and treatment complications (4,25,27).

Height & BMI

Height was measured to the nearest 0.1 cm with a portable stadiometer without shoes. An electrical bioimpedance device (InBody 520, Biospace Co., Ltd) was used to measure weight and BMI (28). The device assesses weight and automatically calculates BMI using the formula: weight (kg) / height (m2).

Infra-red Perometer Measurement

Perometry 350S was performed on each arm. A 3-dimensional limb image was generated and limb volume was calculated for each participant. This optoelectronic method has a standard deviation of 8.9 ml (arm), less than 0.5% of limb volume with repeated measuring (29–30).

Lymphedema Symptom Assessment

The Lymphedema and Breast Cancer Symptom Experience Index is a valid and reliable self-report instrument to assess symptoms related to lymph fluid accumulation (25,27,31).

DNA Data Collection

Selection of Genes and polymorphisms (SNPs)

Genes were selected for this exploratory investigation based on functional implications from previous publications. Table 1. These inflammatory genes include lymphatic specific growth factors (VEGF-C & VEGF-D), cytokines (IL1-a, IL-4, IL6, IL8, IL10, & IL13) and tumor necrosis factor-a (TNF-a). Functional SNPs known for inflammation and lymphatic specific growth factors were selected for the genes where these types of functional SNPs have been documented. VEGF-C and VEGF-D do not have any known functional SNPs. Therefore these genes were evaluated using tagging SNPs (tSNP) that cover the variability in the entire gene as well as the 5’ flanking promoter regions. The criteria used for selection of tSNPs were based on a MAF cutoff of 20%, R2 cutoff of 80% using Pairwise Tagger for a Caucasian population (http://hapmap.ncbi.nlm.nih.gov/) (32).

Table 1.

Selected SNPs: Genes, Chromosome, Function

tagSNPs Gene Chromosome Function Functional SNPs
rs1800587
rs17561		 IL1-a *Chro.2q14-21 Proinflammatory, primary
initiator of inflammation
response		 −380 A/G
−889 C/T
rs2070874
rs2243250		 IL4 Chro.5 132.01-
132.02 Mb		 Inflammation and wound repair the T allele of the
−590C/T polymorphism of
the IL4 gene
Interleukin-4 (IL-4) −589
rs1800795
ss1800796		 IL6 Chro. 7p21-24 Proinflammatory, primary
initiator of inflammation
response, growth factors,
obesity		 −174 C/G
−572 G/C
rs4073 IL8 Chro. 4 Proinflammatory chemokines,
activation of neutrophils		 −251 A/T
rs1800871
rs1800872
rs1800896		 IL10 Chro.1 Anti-inflammatory, regulates T-
cell and macrophage function		 −819 C/T
−592
−1082 G/A
rs1800925
rs20541		 IL13 Chro. 5q31 Anti-inflammatory, inhibits the
production of proinflammatory
cytokines and chemokines,
including IL1, IL6, & IL8		 Chromosome: 5;
NC_000005.9
(131993865..131996801
rs1800610
rs1800629
rs361525		 TNF-a Chro. 6 p21.3 Proinflammatory, key
immunemediator		 −IVS1+90 G/A
−308 G/A
−251 T/A
rs1485766
rs3775203
rs3775195
rs11947611
rs4604006		 VEGF C Chro. 4 Lymphatic specific growth
factor, strong regulator of
lymphangiogenesis,
hyperplastic lymphatic vessels,
lymphedema.

**VEGF C binds to and
activates VEGFR3 and
VEFGR2 receptors on
lymphatic epithelium.		 None known
rs6527518
rs6632528
rs6632474
rs4830939
rs1285844		 VEGF D Chro. X Lymphatic specific growth
factor receptor, lymphedema		 None known
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*

Chro: chromosome

Sample collection and DNA extraction

Saliva sample collections were conducted using the Oragene DNA self-collection kit from DNA Genotek Corporation. DNA were extracted using the protocol and reagents for extraction supplied with the Oragene kit.

Genotype data collection. Genotype data collection

The MassArray i-PLEX Gold SNP Assay (Sequenom, San Diego, CA, USA) was utilized for genotype data collection. Amplification and MassExtend primers were designed with MassARRAY Assay Design 3.1.

Genotyping Quality

Reliability evaluation included checks of expected homozygosity to observed homozygosity at each marker; allele frequencies; genotype call rates; and checks for Hardy-Weinberg Equilibrium consistency (33–34). To ensure robust genetic association analyses, quality control filtering of SNPs was performed. SNPs with call rates of < 95% or Hardy-Weinberg p values of < 0.001 were excluded.

Statistical Analysis

Defining Phenotypes

Because even a 5% limb volume increase enables detectable differences in QOL, arm lymphedema phenotype was defined as a perometer measurement of ≥5% limb volume increase from pre-surgery baseline in the ipsilateral arm in comparison with the changes in the contralateral arm (5). Since >8 symptoms enables detectable differences in ≥5% and 200 mL limb volume increase as well as QOL (5,25,31), phenotype of symptom count was categorized into three groups: 0 symptom, 1–7 symptoms, and ≥8 symptoms at 4–8 weeks and 12 months post-surgery. As symptoms may denote clusters as groups of more than two symptoms that occur together indicating different biological mechanism (31,35), we performed exploratory factor analysis using principal component analysis (PCA) with a varimax rotation to examine if symptoms presented as clusters. Suitability of the data for PCA was supported by the KMO test (4–8 weeks = 0.762, 12 months = 0.790), indicating an adequate sample size for factor analysis and Bartlett’s test of sphericity (Approx. Chi-Square= 4–8 weeks = 1805.124, df=253, p<0.001; 12 months = 2285.879, df = 325, p < 0.001). It was theorized there were three factors identifying symptom clusters (impaired limb mobility, fluid accumulation, and discomfort), which was supported by the rotated factor solutions (scree plot, variance explained, factors with at least 5 variables loading > 0.4, minimal cross loadings. Three-factor solutions explained 49.7% and 52.5% of the variance in reported symptoms at 4–8 weeks and 12 months post-surgery. Table 5.

Table 5.

Factor loadings with varimax rotation for symptom cluster phenotypes.

4-8 Weeks post-surgery 12-Months post-surgery
Factor 1 Factor 2 Factor 3 Factor 1 Factor 2 Factor 3
Impaired
Limb
Mobility		 Fluid
Accumulation		 Discomfort Impaired Limb
Mobility		 Discomfort Fluid
Accumulation
Limited Shoulder
Movement		 0.850 0.131 0.227 0.650 −0.091 0.031
Limited Elbow
Movement		 0.742 0.023 −0.010 0.621 −0.222 0.297
Limited Wrist
Movement		 0.300 0.331 −0.070 0.410 −0.044 0.519
Fingers Limited 0.190 −0.214 0.546 0.370 −0.149 0.741
Limited Arm
Movement		 0.852 0.158 0.236 0.783 0.189 0.104
Hand swelling - - - 0.546 0.050 0.599
Arm Swelling 0.320 0.627 −0.108 0.553 0.226 0.577
Breast Swelling −0.079 0.753 0.180 −.075 0.220 0.669
Chest Wall
Swelling		 0.178 0.688 0.170 0.091 0.328 0.487
Firmness 0.331 0.554 0.033 0.784 .009 0.187
Tightness 0.784 0.133 0.182 0.785 .270 −0.034
Heaviness 0.401 0.521 0.090 0.794 .310 0.148
Fibrosis
(Toughness or
thickness of skin)		 0.074 0.418 0.015 0.588 .101 0.476
Stiffness 0.728 0.120 0.333 0.618 .198 0.095
Tenderness 0.457 0.324 0.489 0.418 0.562 0.207
Hotness/Increased
temperature		 −0.231 0.558 0.280 0.419 0.301 0.265
Redness 0.030 0.710 0.163 −0.037 0.164 0.363
Blistering - - - −0.069 0.736 0.024
Pain 0.341 −0.065 0.529 0.306 0.602 0.289
Numbness 0.296 0.301 .321 0.299 0.145 0.520
Burning −0.215 0.328 0.556 −0.065 0.436 0.522
Stabbing −0.163 0.359 0.694 −0.111 0.733 0.211
Tingling (pins and
needles)		 0.086 0.080 0.466 0.085 0.587 0.146
Arm or hand
fatigue		 0.237 0.102 0.694 0.412 0.606 0.033
Arm or hand
weakness		 0.278 0.118 0.719 0.508 0.558 −0.053
Seroma - - - 0.199 0.688 0.150
Initial Eigenvalues 6.587 2.777 2.073 8.657 2.972 2.012
% of variance 28.641 12.076 9.012 33.296 11.429 7.739
% of cumulative
variance 		28.641 40.717 49.729 33.296 44.725 52.464
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*

Bold values denote factor loading > 0.40, -’s indicate symptoms not reported by any patients

Phenotype and Genotype Analysis

SPSS version 22 (IBM, Armonk, NY, USA) was used for statistical analysis. Descriptive statistics were calculated for demographic and clinical characteristics. All point estimates were generated with 95% confidence intervals. Means and standard deviations were used to summarize continuous variables; frequencies and percentages were used to summarize categorical variables. Distributions of the phenotype variables, including (a) limb volume by perometer ≥5%; (b) count of lymphedema symptoms (0 symptom; 1–7symptoms; ≥8 symptoms); and (c) symptom clusters (reporting 2 or more symptoms related to impaired limb mobility, fluid accumulation and discomfort), were summarized at each time point (pre-surgery, 4–8 weeks and 12 months post-surgery) and associations with demographic and clinical covariates were estimated. Chi-square analyses were performed to estimate for the associations between categorical demographic and clinical predictors, genotypes (i.e. SNPs for proinflammatory genes) and phenotype variables. Fisher’s exact tests were conducted when data did not meet the assumptions of Pearson’s chi-square (cells with expected counts < 5). Independent groups t-tests and ANOVAs were used to compare groups on continuous variables, Mann-Whitney and Kruskal-Wallis test of ranks was used for data that were not normally distributed. No adjustments were conducted due to the exploratory nature of the study that would require confirmation in future subsequent research (36–37). Co-dominant mode of genetic allelic (0, 1, 2) inheritance (i.e., rare-allele homozygosity, heterozygosity and reference category of common-allele homozygosity) was assumed in statistical analyses, using a trend test with 1 degree of freedom (36–37).

RESULTS

Phenotypic Characteristics

A total of 136 participants completed the study with only 3.8% attrition rate (n=4 patients). Data from these four patients were not included in the data analysis. The participants were women of a mean age of 52 years with more than 66% having at least a bachelor degree. More than one-half of the participants were married (58.8%) and the majority of participants was employed (83.1%). Among the participants, 60.3% were white, followed by black/African American (19.9%), Asian (9.6%) and Hispanic (8.8%). The majority of the participants had adjuvant chemotherapy (70.7%), radiation therapy (70%), lumpectomy (48.5%) and mastectomy (51.4%). Table 2. Hardy-Weinberg Equilibrium p-value > 0.01 were for all SNPs.

Table 2.

Demographic and Clinical Characteristics by Phenotype of Arm Lymphedema By ≥5% Limb Volume Increase

Limb Volume Change
At 4-8 Weeks Post-Surgery		 Total Sample Limb Volume Change
At 12-months Post-Surgery
<5% ≥5% <5% ≥5%
n=114 n=22 N=136 n=105 n=31
Mean SD Mean SD p Mean SD Mean SD Mean SD p
Age 51.3 10.8 56.5 12 0.046a 52.1 11.1 0.695a
Weight (pounds) 157.1 35.8 171.4 33.1 0.085a 159.4 35.6 157.1 35.7 167.2 34.8 0.168a
BMI 27.3 6.3 29.9 5.7 0.070a 27.7 6.3 27.3 6.1 29.3 6.7 0.114a
n % n % p n % n % n % p
Education 0.306c 0.455b
Associates degree or less 35 30.7 10 45.5 45 33.1 32 30.5 13 41.9
Bachelor’s degree 55 48.2 7 31.8 62 45.6 49 46.7 13 41.9
Graduate degree 24 21.1 5 22.7 29 21.3 24 22.9 5 16.1
Marital status 0.203c 0.046c
Married/partnered 69 60.5 11 50 80 58.8 64 61 16 51.6
Divorced/Widowed 14 12.3 6 27.3 20 14.7 11 10.5 9 29
Single, never partnered 31 27.2 5 22.7 36 26.5 30 28.6 6 19.4
Ethnicity 0.525c 0.898c
Black/African American 23 20.2 4 18.2 27 19.9 21 20 6 19.4
White Non-Hispanic 69 60.5 13 59.1 82 60.3 64 61 18 58.1
Asian 12 10.5 1 4.5 13 9.6 10 9.5 3 9.7
Hispanic/Latino 8 7 4 18.2 12 8.8 8 7.6 4 12.9
Other 2 1.8 0 - 2 1.5 2 1.9 0 -
Employment 0.210c 0.338b
Unemployed 17 14.9 6 27.3 23 16.9 16 15.2 7 22.6
Employed 97 85.1 16 72.7 113 83.1 89 84.8 24 77.4
Surgery 0.602c 0.673c
Mastectomy 12 10.5 3 13.6 15 11 11 10.5 4 12.9
Lumpectomy 54 47.4 12 54.5 66 48.5 53 50.5 13 41.9
Mastectomy with immediate reconstruction 48 42.1 7 31.8 55 40.4 41 39 14 45.2
Nodes removed Mean
3.2		 SD
3.1		 Mean
3.8		 SD
2.3		 0.164d Mean
3.3		 SD
2.9		 Mean
3.3		 SD
3		 Mean
3.2		 SD
2.7		 0.090 d
Nodes removed Median Median
2		 Median
3.5		 Median
2		 Median
2		 Median
2
Chemotherapy n = 63 n=12 0.318c n=75 =54 n=21 0.299b
Neoadjuvant 17 27 5 41.7 22 29.3 14 25.9 8 38.1
Adjuvant 46 73 7 58.3 53 70.7 40 74.1 13 61.9
Radiation 0.186b 0.175b
No 34 32.4 4 18.2 38 29.9 32 33 6 20
Yes 71 67.6 18 81.8 89 70.1 65 67 24 80
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a

Independent groups t-test

b

Pearson Chi-square

c

Fisher’s exact test

SD: Standard Deviation

Arm Lymphedema Phenotype by Limb Volume and Genetic Variations

At 4–8 weeks post-surgery, 16.2% participants had arm lymphedema defined by ≥5% limb volume increase from baseline in the ipsilateral arm. At 12 months post-surgery, 22.8% participants exceeded ≥5% limb volume increase. Table 2. Limb volume change from pre-surgery baseline at 4–8 weeks post-surgery was significantly correlated (r = 0.503; p<0.01) with limb volume change from baseline at 12 months post-surgery. At 4–8 weeks post-surgery, participants with ≥5% limb volume increase were on average 5 years older. At 12 months post-surgery, marital status significantly related to limb volume: more participants who were married had ≥5% limb volume increase. Of those that have limb volume of <5% at 4–8 weeks post-surgery, 14.9% had ≥5% limb volume at 12 months post-surgery. Those who had ≥5% limb volume increase at 4–8 weeks post-surgery, 36.4% improved to have <5% limb volume at 12 months post-surgery. No significant associations were found between arm lymphedema phenotype defined by ≥5% limb volume increase and any inflammatory-related genetic variations.

Symptom Count Phenotype and Genetic Variations

Prior to surgery, only one participant reported more than 8 symptoms and 18 had 1–7 symptoms; all of these participants had received neoadjuvant chemotherapy or radiation prior to surgery. At 4–8 weeks post-surgery, all participants reported at least one symptom: 46% participants had ≥8 symptoms and 53.7% 1–7 symptoms. At 12 months post-surgery, 10.3% participants were symptom free, while 26.5% participants reported ≥8 symptoms and 63.2% 1–7 symptoms. There was a significant correlation between the number of symptoms reported at 4–8 weeks and 12 months post-surgery (r=0.382; p < 0.01). Type of surgery and number of nodes removed were related to symptoms reported at 4–8 weeks post-surgery: a larger proportion of those who underwent mastectomy with immediate reconstruction (p=0.024) and had more lymph nodes removed (p=0.014) reported 8 or more symptoms. At 12 months post-surgery, more participants who had a Bachelor’s degree or less reported ≥8 symptoms. Table 3.

Table 3.

Demographic And Clinical Characteristic By Symptom Count Phenotype

Symptoms reported
At 4-8 Weeks Post-surgery		 Total Sample Symptoms reported
At 12 months Post-surgery
1-7 Symptom ≥8 Symptoms 0 Symptom 1-7 Symptoms ≥8 Symptoms
n =73 n=6 3 N=136 n=14 n=86 n=36
Mean SD Mean p Mean SD Mean SD Mean SD Mean SD p
Age 52.8 10.3 51.4 11.9 0.461a 52.1 11.1 53.3 13.9 52.5 10.7 50.8 11.1 0.695a
Weight (pounds) 158.7 35.9 160.3 35.4 0.787a 159.4 35.6 144.5 21.4 158.8 35.6 166.8 38.7 0.134a
BMI 27.9 6.5 27.6 6.2 0.765a 27.7 6.3 25.4 4.1 27.6 6.5 29.0 6.5 0.173a
n % n % p n % n % n % n % p
Education 0.582b
Associates degree or
Less		 25 34.2 20 31.7 45 33.1 1 7.1 26 30.2 18 50 0.039c
Bachelor’s degree 35 47.9 27 42.9 62 45.6 10 71.4 40 46.5 12 33.3
Graduate degree 13 17.8 16 25.4 29 21.3 3 21.4 20 23.3 6 16.7
Marital status 0.808b 0.238c
Married/partnered 45 61.6 35 55.6 80 58.8 6 42.9 50 58.1 24 66.7
Divorced/Widowed 10 13.7 10 15.9 20 14.7 5 35.7 11 12.8 4 11.1
Single, never
partnered 		18 24.7 18 28.6 36 26.5 3 21.4 25 29.1 8 22.2
Ethnicity 0.400c 0.189c
Black/African
American		 11 15.1 16 25.4 27 19.9 2 14.3 17 19.8 8 22.2
White Non-Hispanic 49 67.1 33 52.4 82 60.3 8 57.1 56 65.1 18 50
Asian 7 9.6 6 9.5 13 9.6 1 7.1 8 9.3 4 11.1
Hispanic/Latino 5 6.8 7 11.1 12 8.8 3 21.4 3 3.5 6 16.7
More than One Race 1 1.4 1 1.6 2 1.5 0 - 2 2.3 0 -
Employment 0.360b 0.957b
Unemployed 10 13.7 13 20.6 23 16.9 2 14.3 15 17.4 6 16.7
Employed 63 86.3 50 79.4 113 83.1 12 85.7 71 82.6 30 83.3
Surgery 0.024b 0.158c
Mastectomy 4 5.5 11 17.5 15 11 0 - 7 8.1 8 22.2
Lumpectomy 42 57.5 24 38.1 66 48.5 8 57.1 44 51.2 14 38.9
Mastectomy with
immediate
reconstruction		 27 37 28 44.4 55 40.4 6 42.9 35 40.7 14 38.9
Nodes removed
Mean (SD)		 Mean
2.7		 SD
2.1		 Mean
4.1		 SD
3.9		 0.014d Mean
3.3		 SD
2.9		 Mean
2.6		 SD
1.7		 Mean
3.4		 SD
3.3		 Mean
3.4		 SD
2.1		 0.620d
Nodes removed
Median		 Median
2		 Median 3 Median
2		 Median
2		 Median
2		 Median
3
Chemotherapy n = 35 n = 40 0.011b n= 75 n = 7 n = 39 n = 29 0.116c
Neoadjuvant 5 14.3 17 42.5 22 29.3 0 - 11 28.2 11 37.9
Adjuvant 30 85.7 23 57.5 53 70.7 7 100 28 71.8 18 62.1
Radiation 0.712b 0.119c
No 21 31.3 17 28.3 38 29.9 7 50 24 30.8 7 20
Yes 46 68.7 43 71.7 89 70.1 7 50 54 69.2 28 80
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a

ANOVA

b

Pearson Chi-square

c

Fisher’s exact test

d

Kruskal-Wallis test

SD: Standard Deviation

IL1-a (rs17561) was marginally associated with symptom count phenotype of ≥8 symptoms at 4–8 weeks (p = 0.045) and 12 months post-surgery (p=0.050). Participants with genotype of rs17561 (IL1-a) homozygous T/T had 3.16 odds at 4–8 weeks post-surgery and 2.51 odds at 12 months post-surgery for phenotype of ≥8 symptoms as compared with homozygous G/G as the reference group. Table 4.

Table 4.

Odds Ratio of Genotypes and Phenotypes

SNPs Genotype 0 Symptoms 1-7
Symptoms		 >7
Symptoms		 p
value		 OR (95%
CI)		 OR (95%
CI)
4-8 Weeks
Post-Surgery		 n; % n; % n; % 1-7 vs 7+
Symptoms
IL1-a
rs17561		 GG 1/2; 50.0% 40/64;
62.5%		 38/55;
69.1%		 0.045 -----a 1.00
GT 0/2; 0.0% 22/64;
34.4%		 11/55;
20.0%		 -----a 0.53 (0.20,
1.32)
TT 1/2; 50.0% 2/64;
3.1%		 6/55;
19.9%		 -----a 3.16 (0.52,
33.44)
12 Months
Post-Surgery		 0 vs 1-7
Symptoms		 1-7 vs 7+
Symptoms
IL1-a
rs17561		 GG 7/14; 50.0% 47/74;
63.5%		 25/33;
75.8%		 0.050 1.00 1.00
GT 5/14; 25.7% 24/74;
32.4%		 4/33;
12.1%		 0.71 (0.17,
3.19)		 0.31 (0.07,
1.07)
TT 2/14; 14.3% 3/74;
4.1%		 4/33;
12.1%		 0.22 (0.02,
3.23)		 2.51 (0.39,
18.25)
SNPs Genotype No Limb Mobility
(< 2 Symptoms)		 Limb Mobility
(2+ Symptoms)		 p-value OR (95% CI)
4-8 Weeks
Post-Surgery		 n; % n; %
IL4
rs2070874		 CC 21/52; 40.4% 33/68; 48.5% 0.022 1.00
TC 28/52; 53.8% 22/68; 32.4% 0.50 (0.21, 1.17)
TT 3/52; 5.8% 13/68; 19.1% 2.76 (0.64, 16.65)
SNPs Genotype No Fluid
Accumulation
(< 2 Symptoms) 		Fluid
Accumulation
(2+ Symptoms) 		p-value OR (95% CI)
4-8 Weeks
Post-Surgery		 n; % n; %
IL6
rs1800795		 GG 42/54; 77.8% 22/65; 33.8% 0.025 1.00
CG 7/54; 13.0% 40/65; 61.5% 3.30 (1.18, 10.08)
CC 5/54; 9.3% 3/65; 4.6% 0.63 (0.09, 3.50)
IL4
rs2070874		 CC 21/54; 38.9% 54/66; 50.0% 0.007 1.00
TC 30/54; 55.6% 20/66; 30.3% 0.42 (0.18, 1.00)
TT 3/54; 5.6% 13/66; 19.7% 2.76 (0.64, 16.65)
IL4
rs2243250		 CC 19/55; 34.5% 26/65; 40.0% 0.033 1.00
TC 30/55; 54.5% 22/65; 33.8% 0.54 (0.22, 1.30)
TT 6/55; 10.9% 17/65; 26.2% 2.07 (0.62, 7.59)
SNPs Genotype No Discomfort
(< 2 Symptoms) 		Discomfort
(2+ Symptoms) 		p-value OR (95% CI)
4-8 Weeks
Post-Surgery		 n; % n; %
VEGF-C
rs3775203		 CC 13/17; 76.5% 35/93; 37.6% 0.012 1.00
AC 3/17; 17.6% 38/93; 40.9% 4.70 (1.14, 27.44)
AA 1/17; 5.9% 20/93; 21.5% 7.43 (0.95, 331.15)
12 Months
Post-surgery
IL13
rs1800925		 CC 40/73; 54.8% 14/45; 31.1% 0.007 1.00
TC 28/73; 38.4% 20/45; 44.4% 2.04 (0.82, 5.15)
TT 5/73; 6.8% 11/45; 24.4% 6.29 (1.62, 26.61)
Open in a new tab
a

Due to the small sample size for 0 symptom.

Symptom Clusters and Genetic Variations

Prior to surgery, identification of symptom phenotypes was not feasible as 86% of participants were symptom free. Table 5 presents the factor loadings from PCA analysis of symptom reports. At 4–8 weeks post-surgery, 58.1% participants were classified as the phenotype of impaired limb mobility, 86.0% discomfort, and 55.9% fluid accumulation. At 12 months post-surgery, 55.2% participants were classified as the phenotype of impaired limb mobility, 38.2% discomfort, and 44.1% fluid accumulation. Weight and BMI were significantly related to fluid accumulation at 4–8 weeks (p = 0.025) and 12 months post-surgery (p=0 .010) and discomfort 4–8 weeks (p = 0.041) and 12 months post-surgery (p = 0.040). Table 6.

Table 6.

Demographic and Clinical Characteristics by Symptom Clusters Phenotype

Demographic and Clinical Characteristics by Phenotype of Impaired Limb Mobility
Impaired Limb Mobility Symptoms at 4-8 Weeks Post-Surgery Total Impaired Limb mobility Symptoms at 12 Months Post-Surgery
<2 Symptoms 2+ Symptoms <2
Symptoms		 2+
Symptoms
n = 57 n = 79 N= 136 n = 61 n = 75
Mean SD Mean SD p Mean SD Mean SD Mean SD p
Age 52.7 10.31 51.8 11.7 0.640a 52.1 11.1 53.6 10.5 50.9 11.5 0.695a
Weight (pounds) 162.1 34.8 157.5 36.3 0.459a 159.4 35.6 158.9 38.9 159.8 33.0 0.881a
BMI 28.7 6.2 27.0 6.3 0.132a 27.7 6.3 27.4 6.4 28.0 6.3 0.563a
n % n % p n % n % n % p
Education 0.182b 0.030b
Associates degree or less 22 38.6 23 29.1 45 33.1 14 23.0 31 41.3
Bachelor’s degree 27 47.4 35 44.3 62 45.6 29 47.5 33 44.0
Graduate degree 8 14.0 21 26.6 29 21.3 18 29.5 11 14.7
Marital status 0.864b 0.834b
Married/partnered 32 56.1 48 60.8 80 58.8 36 59.0 44 58.7
Divorced/Widowed 9 15.8 11 13.9 20 14.7 10 16.4 10 13.3
Single, never partnered 16 28.1 20 25.3 36 26.5 15 24.6 21 28.0
Ethnicity 0.960b 0.682c
Black/African American 11 19.3 16 20.3 27 19.9 12 19.7 15 20.0
White Non-Hispanic 36 63.2 46 58.2 82 60.3 36 59.0 46 61.3
Asian 5 8.8 8 10.1 13 9.6 5 8.2 8 10.7
Hispanic/Latino 4 7.0 8 10.1 12 8.8 6 9.8 9 8.0
More than One Race 1 1.8 1 1.3 2 1.5 2 3.3 0 -
Employment 0.447b 0.168b
Unemployed 8 14.0 15 19.0 23 16.9 7 11.5 16 21.3
Employed 49 86.0 64 81.0 113 83.1 54 88.5 59 78.7
Surgery <0.001b 0.057b
Mastectomy 3 5.3 12 15.2 15 11.0 3 4.9 12 16.0
Lumpectomy 40 70.2 26 32.9 66 48.5 35 57.4 31 41.3
Mastectomy with
immediate reconstruction		 14 24.6 41 51.9 55 40.4 23 37.7 32 42.7
Nodes removed Mean SD Mean SD 0.111d Mean SD Mean SD Mean SD 0.269d
2.8 2.2 3.7 3.6 3.3 2.9 2.9 2.2 3.7 3.6
Nodes removed (Median) Median
2		 Median
3		 Median
2		 Median
2		 Median
3
Chemotherapy n = 28 n= 47 0.006b n= 75 n = 25 n = 50 0.004b
Neoadjuvant 3 10.7 19 40.4 22 29.3 2 8.0 20 40.0
Adjuvant 25 89.3 28 59.6 53 70.7 23 92.0 30 60.0
Radiation 0.313b 0.010b
No 13 25.0 25 33.3 38 29.9 23 41.8 15 20.8
Yes 39 75.0 50 66.7 89 70.1 32 58.2 57 79.2
Demographic and Clinical Characteristics by Phenotype of Fluid Accumulation
Fluid Accumulation Symptoms at 4-8 Weeks Post-Surgery Total Fluid Accumulation Symptoms at 12 Months Post-Surgery
<2 Symptoms 2+ Symptoms <2 Symptoms 2+ Symptoms
n=60 n=76 N =136 n =76 n = 60
Mean SD Mean SD p Mean SD Mean SD Mean SD p
Age 50.4 11.2 53.5 10.9 0.105a 52.1 11.1 52.1 11.5 52.2 10.7 0.955a
Weight (pounds) 153.0 37.3 164.5 33.7 0.063a 159.4 35.6 153.4 32.7 167.1 37.9 0.025a
BMI 26.4 6.2 28.8 6.2 0.032a 27.7 6.3 26.5 5.9 29.2 6.4 0.010a
n % n % p n % n % n % p
Education 0.730b 0.168b
Associates degree or less 20 33.3 25 32.9 45 33.1 21 27.6 24 40.0
Bachelor’s degree 29 48.3 33 43.4 62 45.6 35 46.1 27 45.0
Graduate degree 11 18.3 18 23.7 29 21.3 20 26.3 9 15.0
Marital status 0.124b 0.8342b
Married/partnered 40 66.7 40 52.6 80 58.8 46 60.5 34 56.7
Divorced/Widowed 5 8.3 15 19.7 20 14.7 10 13.2 10 16.7
Single, never partnered 15 25.0 21 27.6 36 26.5 20 26.3 16 26.7
Ethnicity 0.966c 0.863c
Black/African American 11 18.3 16 21.1 27 19.9 13 17.1 14 23.3
White Non-Hispanic 38 63.3 44 57.9 82 60.3 48 63.2 34 56.7
Asian 5 8.3 8 10.5 13 9.6 8 10.5 5 8.3
Hispanic/Latino 5 8.3 7 9.2 12 8.8 6 7.9 9 10.0
More than One Race 1 1.7 1 1.3 2 1.5 1 1.3 1 1.7
Employment 0.323b 0.946b
Unemployed 8 13.3 15 19.7 23 16.9 13 17.1 10 16.7
Employed 52 86.7 61 80.3 113 83.1 63 82.9 50 83.3
Surgery 0.818b 0.129b
Mastectomy 6 10.0 9 11.8 15 11.0 6 7.9 9 15.0
Lumpectomy 28 46.7 38 50.0 66 48.5 34 44.7 32 53.3
Mastectomy with
immediate reconstruction		 26 43.3 29 38.2 55 40.4 36 47.4 19 31.7
Nodes removed Mean
3		 SD
2.1		 Mean
3.5		 SD
3.5		 0.845d Mean
3.3		 SD
2.9		 Mean
3.3		 SD
3.2		 Mean
3.3		 SD
2.6		 0.077d
Nodes removed (Median) Median
2		 Median
2		 Median
2		 Median
2		 Median
2
Chemotherapy n = 35 n = 40 0.006b n= 75 n = 37 n = 38 0.051b
Neoadjuvant 10 28.6 12 30.0 22 29.3 7 18.9 15 39.5
Adjuvant 25 71.4 28 70.0 53 70.7 30 81.1 23 60.5
Radiation 0.741b 0.004b
No 17 31.5 21 28.8 38 29.9 28 40.6 10 17.2
Yes 37 68.5 52 71.2 89 70.1 41 59.4 48 82.8
Demographic and Clinical Characteristics by Phenotype of Discomfort
Discomfort Symptoms at 4-8 Weeks Post-Surgery Total Discomfort Symptoms at 12 months Post-Surgery
<2 Symptoms 2+ Symptoms <2 Symptoms 2+ Symptoms
n = 19 n = 117 N= 136 n = 84 n = 52
Mean SD Mean SD p Mean SD Mena SD Mean SD p
Age 51.7 12.7 52.2 10.9 0.865a 52.1 11.1 51.9 11.5 52.6 10.6 0.696a
Weight (pounds) 158.3 32.9 159.6 36.2 0.878a 159.4 35.6 154.5 34.6 167.3 36.2 0.041a
BMI 27.1 4.7 27.8 6.6 0.655a 27.7 6.3 26.9 5.9 29.1 6.7 0.040a
n % n % p n % n % n % p
Education 0.480c 0.039b
Associates degree or less 4 21.1 41 35.0 45 33.1 21 25.0 24 46.2
Bachelor’s degree 10 52.6 52 44.4 62 45.6 43 51.2 19 36.5
Graduate degree 5 26.3 24 20.5 29 21.3 20 23.8 9 17.3
Marital status 0.474c 0.949b
Married/partnered 9 47.4 71 60.7 80 58.8 49 58.3 31 59.6
Divorced/Widowed 3 15.8 17 14.5 20 14.7 13 15.5 7 13.5
Single, never partnered 7 36.8 29 24.8 36 26.5 22 26.2 14 26.9
Ethnicity 0.982c 0.507c
Black/African American 4 21.1 23 19.7 27 19.9 14 16.7 13 25.0
White Non-Hispanic 11 57.9 71 60.7 82 60.3 54 64.3 28 53.8
Asian 2 10.5 11 9.4 13 9.6 8 9.5 5 9.6
Hispanic/Latino 2 10.5 10 8.5 12 8.8 6 7.1 6 11.5
More than One Race 0 - 2 1.7 2 1.5 2 2.4 0 -
Employment 0.741c 0.923b
Unemployed 4 21.1 19 16.2 23 16.9 14 16.7 9 17.3
Employed 15 78.9 98 83.8 113 83.1 70 83.3 43 82.7
Surgery 0.292c 0.035b
Mastectomy 0 - 15 12.8 15 11.0 7 8.3 8 15.4
Lumpectomy 10 52.6 56 47.9 66 48.5 36 42.9 30 57.7
Mastectomy with immediate
reconstruction		 9 47.4 46 39.3 55 40.4 41 48.8 14 26.9
Nodes removed Mean
3.5		 SD
5.2		 Mean
3.2		 SD
2.3		 0.238d 3.3 2.9 3.1 2.3 3.7 4.2 0.027d
Nodes removed (Median) Median
2		 Median
2		 Median
2		 Median
2		 Median
2
Chemotherapy n =6 n = 69 0.664c n=75 n = 40 n=35 0.165b
Neoadjuvant 1 16.7 21 30.4 22 29.3 9 22.5 13 37.1
Adjuvant 5 83.3 48 69.6 53 70.7 31 77.5 22 62.9
Radiation 0.370b 0.001b
No 7 38.9 31 28.4 38 29.9 32 41.0 6 12.2
Yes 11 61.1 78 71.6 89 70.1 46 59.0 43 87.8
Open in a new tab
a

Independent groups t-test

b

Pearson Chi-square

c

Fisher’s exact test

d

Kruskal-Wallis test

SD Standard Deviation

IL-4 (rs2070874) was associated with phenotype of impaired limb mobility (p=0.022) and fluid accumulation (p=0.007). Phenotype of fluid accumulation was associated with IL6 (rs1800795) (p=0.025) and IL4 (rs2243250 & 2070874). Phenotype of discomfort was associate with rs3775203 (VEGF-C) (p=0.012) and rs1800925 (IL13) (p=0.007). Participants with genotype of rs2070874 (IL-4) homozygous T/T had 2.76 odds for phenotype of impaired limb mobility as compared with homozygous C/C as the reference group. Participants with genotype of rs1800795 (IL-6) heterozygous C/G had 3.30 odds for phenotype of fluid accumulation as compared with homozygous G/G; participants with genotype rs2070874 (IL-4) homozygous T/T had 2.76 odds for phenotype of fluid accumulation in comparison with homozygous C/C as the reference group; those with genotype rs2243250 (IL-4) homozygous T/T had 2.07 odds for phenotype of fluid accumulation in comparison with homozygous C/C. Participants with genotype rs3775203 (VEGF-C) heterozygous A/C had 4.70 odds for phenotype of discomfort as compared with homozygous C/C; those with genotype rs1800925 (IL-13) homozygous T/T had 6.29 odds and heterozygous T/C had 2.04 odds for discomfort phenotype as compared with homozygous C/C. Table 4. Additive model revealed that participants with two of the three SNPs (rs1800795, rs2070874, and rs2243250) had 5.29 odds for the phenotype of fluid accumulation. Participants with both SNPs (rs3775203 and rs1800925) had 12.86 of odds for phenotype of discomfort. Table 7.

Table 7.

Genotype Additive Models

Genotypes Phenotype of Fluid Accumulation
IL6 rs1800795
IL4 rs2070874
IL4 rs2243250		 No Fluid
Accumulation
(< 2 Symptoms)		 Fluid Accumulation
(2 + Symptoms)		 P = 0.005
OR (95% CI)
0 41/54; 75.9% 31/64; 48.4% 1.00
1 10/54; 18.5% 18/64; 28.1% 2.38 (0.89 – 6.59)
2 3/54; 5.6% 12/64; 18.8% 5.29 (1.25 – 31.13)
3 0/54; 0.0% 3/64; 4.7% -----
Genotypes Phenotype of Discomfort
VEGF-C rs3775203
IL13 rs1800925		 No Discomfort
(< 2 Symptoms)		 Discomfort
(2 + Symptoms)		 P = 0.022
OR (95% CI)
0 6/17; 35.3% 14/93; 15.0% 1.00
1 10/17; 58.8% 49/93; 52.7% 2.10 (0.53 – 7.73)
2 1/17; 5.9% 30/93; 32.3% 12.86 (1.30 – 610.42)
Open in a new tab

DISCUSSION

Prior studies on lymphedema following breast cancer surgery used different criteria for the phenotype of arm lymphedema either by >5cm of limb girth comparison or bioimpedance ratio and explored genes known for primary lymphedema and cytokine genes (22–24). Our study used well-defined criteria for heterogeneous phenotypes lymphedema: arm lymphedema by 𢙞5% limb volume increase, phenotype of symptom count, and symptom clusters. Specific phenotypic factors were identified for each phenotype: age and marital status for arm lymphedema; mastectomy with immediate reconstruction and numbers of lymph node removed for phenotype of ≥8 symptoms; weight, BMI, neoadjuvant chemotherapy, radiation, mastectomy, and lower level of education for phenotype of symptom clusters: impaired limb mobility, fluid accumulation and discomfort. This supports heterogeneity of lymphedema phenotype beyond arm lymphedema.

Our study is the first to explore the associations between genetic susceptibility targeting identified phenotypic risk factors of inflammation and heterogeneous phenotypes of lymphedema. No significant associations were found between conventional defined arm lymphedema defined by ≥5% limb volume increase from baseline and inflammatory genes. Yet, genes related to inflammation, IL1-a rs17561, IL4 rs2070874 and rs2243250, IL6 rs1800795, IL-13 rs1800925, and gene strong for regulating lymphangiogenesis, VEGF-C rs3775203, are found associated with symptom count phenotype of ≥8 symptoms and symptom cluster phenotype of impaired limb mobility, fluid accumulation, and discomfort. Only one study evaluated genes related to inflammation with lymphedema defined by bioimpedance ratio and found that IL4 rs2227284 homozygous (C/C + C/A vs A/A) had a 69% decrease in the odds of developing lymphedema (23–24). Our study found genotype IL4 rs2070874 homozygous T/T increased in 2.76 odds for phenotype of fluid accumulation and IL4 rs2243250 (IL-4) homozygous T/T 2.07 odds for phenotype of fluid accumulation in comparison with homozygous C/C. IL4 has the ability to activate macrophages into M2 macrophages which function in tissue repair, fibrosis and the regulation of inflammation (23). Further research is needed to evaluate the role of IL4 in the development of lymphedema, especially phenotype of fluid accumulation and impaired limb mobility.

Our study found that genotypes are significantly associated with symptom cluster phenotype: IL4 rs2070874 for impaired limb mobility; IL6 rs1800795, IL4 rs2070874, IL4 rs2243250 for fluid accumulation; VEGF-C rs3775203 and IL-13 rs1800925 for discomfort. More importantly, individuals who had at least two of the three risk genotypes (IL6 rs1800795, IL4 rs2070874, and IL4 rs2243250) increased in 5.29 odds for the fluid accumulation. Individuals who had both VEGF-C rs3775203 and IL6 rs1800925 genotypes increased in 12.86 of odds for discomfort. This provides support for heterogeneity of lymphedema phenotypes, especially phenotype of symptom clusters based on biological mechanisms. It is important to include symptom phenotypes in future research and clinical practice. Patients who report symptoms with the identified genotypes should be considered to have higher risk for early and precision intervention.

We are aware of the limitations of the sample size and 12-month follow-up. Yet, the study provided an opportunity to assess the feasibility of this approach and necessary information for subsequent confirmation research. The strengths of our study included well-defined phenotypes, adequate sample size for an exploratory study, well-designed prospective and consecutive repeated measurement of heterogeneous phenotypes at meaningful time points, and selection of genes related to well-established phenotypic risk factors of inflammation.

CONCLUSIONS

Precision assessment of heterogeneity of lymphedema phenotype and understanding the biological mechanism of each phenotype through the exploration of inherited genetic susceptibility is a logical step for finding a cure for this chronic condition (38). For example, phenotype of fluid accumulation is identified to be associated with inflammatory mechanism as evidenced by not only significant associations with IL6 rs1800795, IL4 rs2243250 and IL4 rs2070874 but also higher BMI (>30), which is associated with polymorphisms of IL6 and elevated IL6 level (16, 17,19). Further evidence includes that interventions to promote lymph fluid flow and optimize BMI have demonstrated positive effects for phenotype of fluid accumulation (30). Further exploration of the intervention to promote lymph flow and optimize BMI and other existing treatments in the context of genotype and gene expressions could further advance our understanding of biological mechanism of each phenotype. Future prospective research should focus on a priori recognition of inherited genetic susceptibility to facilitate risk prediction, more precisely assess lymphedema phenotypes and provide increased precision in targeted intervention. Such research could assist in further discerning biological mechanisms of heterogeneity of lymphedema phenotype.

Highlights for Review.

  • ➢

    Lymphedema, an abnormal accumulation of lymph fluid in the ipsilateral body area or upper limb, remains an ongoing major health problem affecting more than 40% of 3.1 million breast cancer survivors in the United States (1-3).

  • ➢

    Lymphedema following breast cancer surgery is considered to be mainly due to the mechanical injury from surgery. Recent research identified that inflammation-infection and obesity may be the important predictors for lymphedema.

  • ➢

    Genetic variations may be one of the important factors that influence breast cancer survivors’ responses to the inflammatory processes and vulnerability to lymphedema, including responses to trauma (surgery and radiation) and triggering factors (infection, burns, minor injuries, higher BMI or obesity).

  • ➢

    No significant associations were found between arm lymphedema phenotype and any inflammatory genetic variations.

  • ➢

    IL1-a rs17561 was marginally associated with symptom count phenotype of ≥8 symptoms. IL-4 rs2070874 was significantly associated with phenotype of impaired limb mobility and fluid accumulation.

  • ➢

    Phenotype of fluid accumulation was significantly associated with IL6 rs1 800795, IL4 rs2243250 and IL4 rs2070874. Phenotype of discomfort was significantly associated with VEGF-C rs3775203 and IL13 rs1800925.

  • ➢

    Precision assessment of heterogeneity of lymphedema phenotype and understanding the biological mechanism of each phenotype through the exploration of inherited genetic susceptibility is essential for finding a cure.

Acknowledgments

This study was supported by the National Institute of Health (NINR Project # 1R21NR012288-01A and NIMHD Project # P60 MD000538-03) and 2011 ONS Foundation Breast Cancer Research Grant from Oncology Nursing Society. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH and other funders. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflicts of Interest

The authors declare no conflict of interest.

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