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. 2025 Feb 10;27(3):411–422. doi: 10.1177/10998004251318397

Gut Microbiome and Symptom Burden in Obese and Non-Obese Women Receiving Chemotherapy for Breast Cancer

Catherine H Cherwin 1,✉, Jemmie Hoang 1, Emily K Roberts 2, Ashutosh Mangalam 3
PMCID: PMC12633718  NIHMSID: NIHMS2121234  PMID: 39928757

Abstract

Purpose: Obese women with breast cancer experience high symptom burden, poor quality of life, and increased mortality compared to non-obese women with breast cancer. Obesity-related changes to the bacteria of the gut, the GI microbiome, may be one such mechanism for these differences in outcomes. The purpose of this work is to report symptom burden and GI microbiome composition between obese and non-obese women with breast cancer to identify potential microbial influences for symptom severity. Methods: 59 women with breast cancer (26 obese, 33 non-obese) provided symptom reports using the Memorial Symptom Assessment Scale and stool samples for 16S analysis one week after receiving chemotherapy. Symptom reports were summarized and examined for differences based on obesity. Fecal microbiome analysis was compared between groups using alpha-diversity (Shannon index), beta-diversity (Principal Coordinate Analysis with weighted UniFrac distances), and LASSO analysis of abundance of bacterial species. Results: While symptom burden was high, it did not differ based on obesity status. Alpha- and beta-diversity did not find significant differences based on obesity, but LASSO analysis identified eight bacteria to be significantly enriched in obese participants: Collinsella aerofacien, Prevotella 7, Coprobacillus cateniformis, Ruminococcus torques group, Agathobacter, Frisingicoccus, Roseburia inulinivorans, and Monoglobus pectinilyticus. Conclusions: Identifying biologic mechanisms driving symptoms is necessary for the development of therapies to reduce cancer-related symptom burden. While obesity may alter the GI microbiome and influence symptom burden in women with breast cancer, these effects may be outweighed by the effects of chemotherapy on the gut.

Keywords: gastrointestinal, microbiome, symptom burden, breast cancer, obesity

Introduction

Obese women with breast cancer are at risk for severe fatigue (Álvarez-Bustos et al., 2021), pain (Dorfman et al., 2023) sleep disturbance (Aronsen et al., 2022), lower quality of life (QoL) (Leach et al., 2023), reduced efficacy of cancer treatment, and increased mortality (Lee et al., 2019) compared to their non-obese counterparts. The exact mechanism for poor outcomes in obese breast cancer patients is not fully understood, but literature suggests that the gastrointestinal (GI) microbiome may play a role. The GI microbiome is associated with important aspects of gut health including GI epithelial integrity, GI mucus degradation, and GI inflammation (Ruan et al., 2020) as well as effects outside the GI tract such as systemic inflammation and maintenance of hormones and neural transmitters (Luqman et al., 2024). While GI microbiome research is a quickly growing field, much of the work within cancer concerns risk for disease or response to treatment. There is limited research describing how individual gut microbes may be influenced by chemotherapy and if changes to the GI microbiome will contribute to symptom burden.

Montassier and his team (2015) explored chemotherapy-associated microbiome changes in people receiving a hematopoietic stem cell transplant for leukemia or lymphoma. They found that after high dose chemotherapy, overall diversity of the GI microbiome decreased and microbes responsible for mucus generation and intestinal permeability were depleted. They also found a depletion among microbes with anti-inflammatory properties such as Bifidobacterium, Clostridium, Coprococcus, Dorea, Faecalibacterium, Lachnospira, Roseburia, Ruminococcus, and an enrichment of Citrobacter a microbe with pro-inflammatory properties. Hong and colleagues (2019) investigated chemotherapy-associated oral microbiome changes and oral mucositis in people with cancer. They found that over a cycle of chemotherapy, patients who received 5-fluorouracil or doxorubicin had a significant change in the oral microbiome that corresponded with increased severity in oral mucositis. Most notably, enrichment of Fusobacterium nucleatum subsp. vincentii, a bacterium with pro-inflammatory properties, positively correlated with higher oral mucositis severity.

Obesity has also been shown to alter the GI microbiome. A recent meta-analysis found an overall decrease in microbial diversity and significantly different abundance of 29 bacterial species among people who were obese compared to those who were not obese. Within those who were obese, Ruminococcus gnavus and Megasphaera elsdenii were enriched and Akkermansia muciniphila, Eubacterium eligens, and Coprococcus eutactus were depleted (Hu et al., 2024). Analyses of these individual bacteria have found Ruminococcus gnavus to have pro-inflammatory properties (Kandasamy et al., 2023) and E. eligens, A. muciniphila, and C. eutactus have anti-inflammatory properties (Chung et al., 2017; Rodrigues et al., 2022; Tian et al., 2022), further supporting inflammation as a possible mechanism driving symptom burden among obese patients with cancer. Furthermore, M. elsdenii was found to be associated with gas production during fermentation of flatulogenic foods (Mutuyemungu et al., 2024), providing evidence that enrichment may contribute to GI symptoms experienced during chemotherapy, including bloating, diarrhea, and flatulence.

Clinicians use knowledge of chemotherapy-associated toxicities to predict onset and severity of patient symptoms. Clinicians can then plan an intervention schedule to manage these symptoms as they arise. However, some patients have more severe symptoms than the average patient and either need to have their chemotherapy dose reduced or even discontinued altogether. While some influences for GI symptom burden are known, such as emetogenicity rating for chemotherapy, several factors remain unknown. There is still a gap in knowledge concerning how the combination of obesity and cancer will influence the GI microbiome and if these changes result in higher symptom burden.

This work is guided by the framework of human GI microbiome variability and influence on symptoms and symptom outcomes. This framework describes how the human GI microbiome changes in response to cancer treatments when the barrier system separating the microbiome from the tissues of gut becomes inflamed and breaks down, which in turn produces symptoms (Chase et al., 2015). Applying this framework in the context of this study, obesity and chemotherapy each contribute to alterations in the gut and the GI microbiome that promote the enrichment of harmful bacteria and a depletion of helpful bacteria, a state known as dysbiosis (Faul et al., 2007). These two sources of dysbiosis combine to then produce higher symptom among the obese patients with cancer (Figure 1).

Figure 1.

Figure 1.

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Framework for obesity, cancer, and microbial alterations influencing symptom burden in women with breast cancer. Figure created with BioRender.com

The aims of this pilot study are to (1) describe the symptom burden (presence, frequency, severity, and distress) and GI microbiome composition (operational taxonomic units) among a sample of obese and non-obese women with breast cancer, and (2) examine for differences in symptoms and GI microbiome that may be attributed to obesity. Understanding these associations and ultimately the biologic mechanisms driving symptoms is necessary for the development of customized therapies to prevent or correct harmful imbalances in the GI microbiota. Eventually this can lead to tailored treatments to alleviate symptoms in obese women with breast cancer undergoing chemotherapy.

Methods

Design & Sample

This pilot study uses a descriptive, cross-sectional design from data collected from women with breast cancer receiving chemotherapy at a Midwestern Comprehensive Cancer Center. The Institutional Review Board (IRB) approved all study procedures. Eligibility criteria were (1) an adult (≥18 years) woman, (2) with a primary diagnosis of any stage breast cancer, (3) receiving a taxane- or platinum-based chemotherapy in 21- or 28-day cycles, and (4) able to read and write in English. Exclusion criteria were (1) surgery or radiation therapy in the last three months, (2) an ostomy that precluded stool collection from the large intestine, or (3) cognitive impairment that would limit their ability to collect a stool sample or questionnaire data.

Measures

All participants self-reported demographics including age, race, ethnicity, education, partner status, and income. Medical records were reviewed to record diagnosis, chemotherapy regimen, chemotherapy dose (e.g., standard dose or reduced dose), emetogenicity rating of chemotherapy regimen, and chemotherapy cycle. Emetogenicity ratings were determined by the likelihood of chemotherapy agents causing nausea and vomiting as detailed by the Multinational Association of Supportive Care in Cancer (MASCC) guidelines (Dupuis et al., 2017). Medical records were reviewed to record the most recent BMI used for chemotherapy dose calculation, and participants were classified as obese (BMI ≥ 30) or non-obese (BMI < 30).

Symptoms were assessed using a modified version of the Memorial Symptom Assessment Scale (MSAS) (Portenoy et al., 1994). In the original MSAS, participants are asked to review a 32-item list of symptoms and indicate if a symptom was present in the last week. For each symptom present, frequency is rated on a 1-4 scale, from “rarely” to “almost constantly”, severity is rated on a 1-4 scale, from “slight” to “very severe”, and distress is rated on a 0-4 scale, from “not at all” to “very much”. The modified version of the MSAS includes the symptoms of heartburn, belching, feeling full early, rectal burning, rectal itching, and flatulence. These GI symptoms were included as they are documented side-effects of chemotherapy (Cherwin, 2012). The original MSAS has well documented reliability and validity among the oncology population (α = 0.83–0.88).

GI Microbiome was assessed through a stool sample collected the week following chemotherapy. Stool samples were collected using an OMNIgene-GUT stool collection kit (DNAgenotek Inc., Ottawa, Canada, Catalog#OMR-200). When the OMNI-gene GUT stool collection kit was received from the participant, it was immediately frozen in a −80°C freezer until processing for DNA extraction. GI bacteria were quantified based on alpha diversity (operational taxonomic units), beta diversity (similarity of overall bacteria taxonomic diversity), and measures of microbial community membership (individual bacteria).

Procedure

As described previously (Hoang et al., 2023), medical records of incoming cancer clinic appointments were reviewed to identify eligible patients. Patients who met eligibility criteria were offered a study brochure and were invited to speak with study staff if they indicated an interest in learning more about the study. Study procedures began the first day of a chemotherapy cycle (Day 1) when participants completed the demographic questionnaire and were trained on how to collect and ship the stool sample using a provided collection kit. Participants were instructed to collect the stool sample then complete the modified MSAS the week after chemotherapy (Day 7). Stool samples were shipped via overnight mail and immediately frozen in a −80°C freezer to await analysis.

When ready for analysis, frozen OMNI-gene GUT stool collection kits were thawed and the stool specimens were analyzed using 16S ribosomal RNA (rRNA) gene sequencing per our previous protocol (Shahi et al., 2019). Briefly, DNA was extracted using two-step amplification of the bacterial 16s rRNA gene V3-V4 region using the Illumina MiSeq platform. Reads were filtered and generated an amplicon sequence variant (ASV) table using the R based platform and Divisive Amplicon Denoising Algorithm 2 (DADA2) package (Callahan et al., 2016). The ASVs were assigned bacterial taxonomy at the kingdom to species levels using the Silva database (version 138.1) with a median number of reads of 41,339.

Analysis

Summary statistics (frequencies, means, standard deviations, and ranges) were used to describe the demographic and clinical characteristics of the sample. Symptom burden is reported as the mean of the total symptom occurrence, frequency, severity, and distress for all of the symptoms present within each participant and for each individual symptom. Symptom ratings were calculated for the entire sample and based on obesity status. Comparisons between obese and non-obese women were conducted using t-tests or Mann-Whitney U tests.

To describe GI microbiome differences between obese and non-obese participants, we used R software (v.4.4.0) with the following packages: phyloseq, microbiome, microbiomeMarker, microbiomeutilities, vegan, ggpubr, pairwiseAdonis, and Ape (Arbizu, 2017; Cao et al., 2022; Kassambara, 2020; Lahti & Shetty, 2017; McMurdie & Holmes, 2012; Oksanen et al., 2012; Paradis & Schliep, 2019; Shetty, 2024). The average read number was 39583 and the median number of reads was 41339. Two samples were removed due to read numbers below 10,000, therefore, a total of 56 samples were normalized to a minimum median sequencing depth of 10x in at least 10% of the samples. Alpha-diversity analysis utilized the Shannon diversity index and Wilcoxon rank-sum test. The Spearman’s rank correlation was used to compare the Shannon diversity index between the obese and non-obese participants. Beta-diversity analysis was conducted using Principal Coordinate Analysis with weighted UniFrac metrics, and statistical significance was assessed through the Permutational ANOVA test (PERMANOVA). The p-values were adjusted for multiple testing using the Benjamini-Hochberg correction.

To predict obesity-related differences in symptom burden and GI microbial community, we employed the least absolute shrinkage and selection operator (LASSO) regression analysis which compared obesity status and operational taxonomic units (OTUs). This method allows for multiple variable selection (e.g., OTUs significantly associated with obesity) jointly within one model (Tibshirani, 1996). The method makes it possible to fit such a model where the number of predictors is greater than the sample size using penalization.

Results

Sample Characteristics

We screened 796 cancer clinic patients, 633 of which were excluded from participation due to not having an eligible diagnosis or chemotherapy regimen, or due to having a pre-existing GI comorbidity. We approached 163 patients for enrollment, of which 68 enrolled (42%), and 59 (87%) were included in the analyses (Figure 2). Participants were predominantly White, non-Hispanic and partnered, and had an average age of 52 years (Table 1). Most had stage I-II breast cancer, were receiving their second or third cycle of chemotherapy, and had chemotherapy regimens rated as “low emetogenicity”. Regarding obesity status, 26 participants were classified as obese (44%) with an average BMI of 35, and 33 (56%) were classified as non-obese with an average BMI of 25 (p < .001). Women who were obese were more likely to be older (p = .002), non-white (p = .01), and not partnered (p = .02) compared to the participants who were non-obese.

Figure 2.

Figure 2.

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Consolidated Standards of Reporting Trials (CONSORT) diagram of recruitment and analysis of participants with breast cancer.

Table 1.

Sample Characteristics.

All (n = 59) Obese (n = 26) Non-obese (n = 33) p-value
Age (years)
 M (SD) 52.10 (13.29) 57.96 (11.34) 47.48 (13.03) p = .002
 Range 27–76 40–76 27–71
BMI
 M (SD) 29.64 (6.79) 35.44 (5.71) 25.07 (3.03) p < .001
 Range 18.10–52.06 30.03–52.06 18.10–29.62
Race
 White n = 53 (88%) n = 21 (81%) n = 32 (97%) p = .007
 Black/African American n = 5 (8%) n = 5 (19%) n = 0 (0%)
 Other/Missing n = 1 (2%) n = 0 n = 1 (3%)
Ethnicity
 Not Hispanic or Latina n = 56 (93%) n = 25 (96%) n = 31 (94%) p = .88
Partnered n = 44 (73%) n = 18 (69%) n = 26 (79%) p = .02
Stage
 I n = 18 (30%) n = 7 (27%) n = 11 (33%) p = .17
 II n = 25 (42%) n = 9 (35%) n = 16 (49%)
 III n = 9 (15%) n = 5 (19%) n = 4 (12%)
 IV n = 7 (12%) n = 5 (19%) n = 2 (6%)
Cycle number
 1 n = 1 (2%) n = 0 n = 1 (3%) p = .88
 2 n = 24 (40%) n = 12 (46%) n = 12 (36%)
 3 n = 16 (27%) n = 5 (19%) n = 11 (33%)
 >3 n = 18 (31%) n = 9 (35%) n = 9 (27%)
Emetogenicity rating
 Low n = 38 (64%) n = 16 (62%) n = 22 (67%) p = .69
 High n = 21 (36%) n = 10 (39%) n = 11 (33%)
Symptom occurrence
 M (SD) 13.40 (6.44) 13.76 (7.88) 13.12 (5.21) p = .71
 Range (1–38) 4–38 5–38 4–25
Symptom frequency
 M (SD) 1.98 (.40) 1.93 (.36) 2.02 (.43) p = .40
 Range (1–4) 1.33–3.25 1.40–2.63 1.33–3.25
Symptom severity
 M (SD) 1.92 (.39) 1.93 (.38) 1.91 (.40) p = .89
 Range (1–4) 1.09–3.00 1.09–2.78 1.17–3.00
Symptom distress
 M (SD) 1.70 (.60) 1.65 (.63) 1.74 (.59) p = .56
 Range (0–4) 0.20–3.04 0.20–2.83 0.86–3.04
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Note. Total percents may not equal 100% due to rounding. Parametric comparisons used t-tests and non-parametric comparisons used Mann-Whitney U tests.

Symptom Burden and Obesity

Symptom reports from the MSAS showed that participants experienced an average of 13 concurrent symptoms with mild to moderate frequency, severity, and distress but symptom reports did not significantly differ based on obesity status (Table1).

Microbial Community and Obesity

When corrected for multiple testing, alpha- (Shannon Diversity) and beta-diversity (Principal Coordinate Analysis with weighted UniFrac metrics) analyses did not show a significant difference in overall microbial community between obese and non-obese women with breast cancer (Figures 3 and 4). Additional sensitivity analysis to correct for the potential confounding effect of antibiotics and probiotics found no significant differences in microbial community between participants who received antibiotics (n = 5 ) or reported taking probiotics (n = 11) during the data collection period.

Figure 3.

Figure 3.

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Alpha diversity (Shannon Diversity Index; Wilcoxon, p = .94) and Spearman correlations (p = .40) found no significant difference based on obesity status among participants with breast cancer.

Figure 4.

Figure 4.

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Beta-diversity (weighted UniFrac) found no significant difference between obese and non-obese participants with breast cancer (PERMANOVA, FDR-adjusted p = .139). Axes represent proportion of variance attributed to microbial composition between obese and non-obese participants.

When examining for differences in individual bacteria species one at a time, none of the species met the threshold for significance after correction for multiple testing. In a joint model, LASSO analyses found the following species to be significantly enriched in the obese participants as compared to the non-obese participants: Collinsella aerofaciens, Prevotella 7, Coprobacillus cateniformis, Ruminococcus torques group, Agathobacter, Frisingicoccus, Roseburia inulinivorans, and Monoglobus pectinilyticus (Table 2, Figures 5 and 6).

Table 2.

LASSO Analysis of OTUs With Significant Difference Between Obese and Non-obese Women With Breast Cancer.

Bacteria Enriched in Obese BRCA Enriched in Nob-Obese BRCA
Actinobacteriota;Coriobacteriia;Coriobacteriales;Coriobacteriaceae;Collinsella;aerofaciens X
Bacteroidota;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella 7 X
Firmicutes;Bacilli;Erysipelotrichales;Erysipelatoclostridiaceae;Coprobacillus;cateniformis X
Firmicutes;Clostridia;Lachnospirales;Lachnospiraceae;[Ruminococcus] torques group X
Firmicutes;Clostridia;Lachnospirales;Lachnospiraceae;Agathobacter X
Firmicutes;Clostridia;Lachnospirales;Lachnospiraceae;Frisingicoccus X
Firmicutes;Clostridia;Lachnospirales;Lachnospiraceae;Roseburia;inulinivorans X
Firmicutes;Clostridia;Monoglobales;Monoglobaceae;Monoglobus;pectinilyticus X
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Note. LASSO (least absolute shrinkage and selection operator analyses) is a method of regression analysis that may be utilized to fit a prediction model where the number of predictors (e.g., microbial OTUs) is greater than the sample size.

Figure 5.

Figure 5.

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LASSO analysis, which assesses different abundance of bacteria OTUs based on obesity status. Bacteria presented here were found to be significantly enriched in the obese participants breast cancer.

Figure 6.

Figure 6.

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Effect sizes for Spearman’s correlations. Bacteria presented here have significantly different relative abundance of OTUs based on obesity status. Significance was adjusted for multiple testing using the Benjamini-Hochberg correction. An effect size of 0.3 represents a moderately strong relationship between bacterial OTU abundance and obesity status.

Discussion

The purpose of this pilot work was to use stool samples and symptom data from obese and non-obese women with breast cancer to describe differences in their microbial communities as a possible contributing factor for symptom burden. We hypothesized that obesity-associated dysbiosis might exacerbate the dysbiosis associated with chemotherapy. This obesity-enhanced dysbiosis would result in an enrichment of bacteria shown to have harmful effects on gut health (i.e., pro-inflammatory functions) as well as a depletion of bacteria shown to have beneficial effects on gut health (i.e., anti-inflammatory functions). Through identification of these potentially pathogenic and beneficial bacteria, we can advance our understanding of the biologic mechanisms driving symptom burden in people with cancer.

Analysis of symptom occurrence, frequency, severity, and distress revealed that while overall symptom burden was high among this sample of women with breast cancer, there were no significant differences in symptom reports based on obesity status. The lack of statistical significance between obese and non-obese participants could be due to a lack of statistical power or the complexity of how obesity interacts with cancer and cancer-treatment. Obesity is associated with a number of poor health outcomes including higher risk for developing breast cancer (Fakhri et al., 2022) as well as reduced efficacy of cancer treatments (Vaysse et al., 2019). However, a phenomena known as the obesity paradox has found higher BMI leads to longer survival time among different types of cancer including colorectal, lung, and renal cell cancer (Lam et al., 2017; Li et al., 2022; Preza-Fernandes et al., 2022). Notably, a recent study by Xie and colleagues (2024) also found higher BMI to be associated with increased survival for many cancer diagnoses though not among breast cancer patients. They discovered that the connection between higher BMI and increased survival was mediated by low systemic inflammation, with obese breast cancer patients and high levels of systemic inflammation having the poorest prognosis compared to other cancer diagnoses. This research demonstrates that not all obesity is alike, and studies have begun classifying the different subtypes of obesity, including “medically healthy obese” (Mayoral et al., 2020; Tsatsoulis & Paschou, 2020). Results from previous research and the results presented here indicate that an obesity label alone does not predict poor health outcomes, rather it is a combination of obesity, inflammation, and other metabolic processes.

Results from our microbiome analyses also did not find a significant difference in overall microbial composition based on obesity. These results differ from a recent meta-analysis of fecal metagenomic sequencing from obese- and non-obese individuals. This work found obese individuals to have a significant decrease in richness and diversity of gut bacteria compared to non-obese individuals (Hu et al., 2024). Analyses from women with breast cancer also found obese patients to have reduced richness and diversity compared with non-obese patients (Wu et al., 2020). However, to our knowledge there are no studies exploring microbial changes in obese and non-obese cancer patients undergoing chemotherapy. Chemotherapy can elicit a change in the intestinal mucosal barrier resulting in reduced cellular turnover, damage to the GI mucosal barrier, and increased GI inflammation, as well as alterations in the GI microbial community (Dahlgren et al., 2021; Walker et al., 2023). So, while there may have been obesity-associated alterations to overall GI microbial community, these may have been overshadowed by the more influential alterations associated with chemotherapy.

While the richness and diversity of the GI microbial community did not differ between obese and non-obese participants, LASSO analysis was able to identify eight bacteria that were significantly enriched in the obese participants. When examining the function of these bacteria, research shows an association between the abundance of these bacteria and outcomes like symptom severity and GI inflammation. For example, enrichment of C. aerofaciens has been shown to be associated with severe neurologic symptoms among women with breast cancer receiving chemotherapy (Terrisse et al., 2021), and enrichment of Prevotella 7, C. cateniformis, Ruminococcus torques group, and Frisingicoccus were found to have pro-inflammatory effects in the bowel of people with colorectal polyps, colorectal adenomas, and irritable bowel disease (El-Salhy et al., 2022; Ruiz-Limón et al., 2022; Senthakumaran et al., 2023).

This work offers a preliminary analysis of obesity-related changes to the GI microbiome in women with breast cancer. Future research could validate these findings or further identify bacteria that are significantly different between groups of patients. By isolating these bacteria and identifying their function, we hope to develop improved microbiome-based assessments and targeted interventions to improve patient outcomes.

Limitations

This work is among the first to explore the combined effect of breast cancer, chemotherapy, and obesity on the GI microbial community and symptom burden. Data used in this study was collected from 59 women during chemotherapy treatment. A majority of microbiome research uses sample sizes ranging between 10–50 (Kers & Saccenti, 2021). Studies reporting differences in symptom burden between obese and non-obese women with breast cancer use larger sample sizes ranging from 101–327 (Cantarero-Villanueva et al., 2015; Dorfman et al., 2023; Schmidt et al., 2018; Wu et al., 2019). While our sample size may have been sufficient for microbiome analyses, sample size calculations indicate that a minimum of 64 participants per group would be needed to detect a statistically significant, moderate (0.5) effect size for difference in symptom burden with 80% power (Faul et al., 2007). Another limitation of this work concerns using BMI to classify obesity status rather than more precise measurements such as waist to hip ratio, body shape index, or body fat percentage. BMI is frequently used in research as it is among the more cost effective and less invasive methods of obesity assessments. However, BMI has been shown to have bias based on race and ethnicity, age, sex, and level of muscle mass. Next, our microbiome analyses used 16s sequencing of V3-V4 region to the species level. This method is not as sensitive as full-length 16S sequencing or shotgun metagenomic sequencing (Johnson et al., 2019). Finally, we included participants with all stages of breast cancer and different chemotherapy regimens. Studies have shown that cancer type and treatment type can impact symptom burden and GI microbial composition. Given these limitations, our future work will utilize a larger sample size, narrow the inclusion criteria to focus on similarly staged cancer and similar chemotherapy regimens, and will use improved methodology to assess obesity and analyze stool samples.

Conclusion

Results from our analyses found that symptom burden among our participants was high regardless of obesity status. We also found that while overall microbial richness and diversity did not differ between obese and non-obese women, eight individual bacteria associated important functions like inflammation, gut health, and survival were significantly enriched in the obese breast cancer participants.

When examining the nursing implications of this work, our results suggest that clinicians should assess symptoms throughout treatment to identify those patients experiencing higher than expected symptom burden. Furthermore, clinicians should also consider how obesity is viewed within cancer care. Obesity is linked to many negative health outcomes, but it is often assumed that crossing a BMI-based threshold automatically equates to high morbidity and mortality. Our research adds to the evidence showing that this relationship is associated with a number of different factors and poor patient outcomes cannot be attributed to obesity alone. Understanding how different biologic mechanisms interact to drive symptom occurrence and severity is necessary for the development of customized therapies to improve symptom burden in women with breast cancer.

Footnotes

Author Contributions: Cherwin, C 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 Hoang, J 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 Roberts, E contributed to conception and design contributed to analysis and interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy Mangalam, A contributed to conception and design contributed to analysis and 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 the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: AKM is one of the inventors of a technology claiming the use of Prevotella histicola to treat autoimmune diseases. AKM received royalties from Mayo Clinic (paid by Evelo Biosciences).

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by 1P20NR018081-01 (PI: Rakel & Gardner, Pilot PI: Cherwin) from the National Institute of Nursing Research.

ORCID iD

Catherine H. Cherwin https://orcid.org/0000-0002-5731-3476

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