Metabolic consequences of perioperative oral carbohydrates in breast cancer patients - an explorative study

Tone Hoel Lende, Marie Austdal, Tone Frost Bathen, Anne Elin Varhaugvik, Ivar Skaland, Einar Gudlaugsson, Nina G Egeland, Siri Lunde, Lars A Akslen, Kristin Jonsdottir, Emiel A M Janssen, Håvard Søiland, Jan P A Baak, Tone Hoel Lende, Marie Austdal, Tone Frost Bathen, Anne Elin Varhaugvik, Ivar Skaland, Einar Gudlaugsson, Nina G Egeland, Siri Lunde, Lars A Akslen, Kristin Jonsdottir, Emiel A M Janssen, Håvard Søiland, Jan P A Baak

Abstract

Background: The metabolic consequences of preoperative carbohydrate load in breast cancer patients are not known. The present explorative study investigated the systemic and tumor metabolic changes after preoperative per-oral carbohydrate load and their influence on tumor characteristics and survival.

Methods: The study setting was on university hospital level with primary and secondary care functions in south-west Norway. Serum and tumor tissue were sampled from a population-based cohort of 60 patients with operable breast cancer who were randomized to either per-oral carbohydrate load (preOp™; n = 25) or standard pre-operative fasting (n = 35) before surgery. Magnetic resonance (MR) metabolomics was performed on serum samples from all patients and high-resolution magic angle spinning (HR-MAS) MR analysis on 13 tumor samples available from the fasting group and 16 tumor samples from the carbohydrate group.

Results: Fourteen of 28 metabolites were differently expressed between fasting and carbohydrate groups. Partial least squares discriminant analysis showed a significant difference in the metabolic profile between the fasting and carbohydrate groups, compatible with the endocrine effects of insulin (i.e., increased serum-lactate and pyruvate and decreased ketone bodies and amino acids in the carbohydrate group). Among ER-positive tumors (n = 18), glutathione was significantly elevated in the carbohydrate group compared to the fasting group (p = 0.002), with a positive correlation between preoperative S-insulin levels and the glutathione content in tumors (r = 0.680; p = 0.002). In all tumors (n = 29), glutamate was increased in tumors with high proliferation (t-test; p = 0.009), independent of intervention group. Moreover, there was a positive correlation between tumor size and proliferation markers in the carbohydrate group only. Patients with ER-positive / T2 tumors and high tumor glutathione (≥1.09), high S-lactate (≥56.9), and high S-pyruvate (≥12.5) had inferior clinical outcomes regarding relapse-free survival, breast cancer-specific survival, and overall survival. Moreover, Integrated Pathway Analysis (IPA) in serum revealed activation of five major anabolic metabolic networks contributing to proliferation and growth.

Conclusions: Preoperative carbohydrate load increases systemic levels of lactate and pyruvate and tumor levels of glutathione and glutamate in ER-positive patients. These biological changes may contribute to the inferior clinical outcomes observed in luminal T2 breast cancer patients.

Trial of registration: ClinicalTrials.gov; NCT03886389. Retrospectively registered March 22, 2019.

Keywords: Breast cancer; Carbohydrate load; Clinical outcome; Fasting state; Insulin; Insulin c-peptide; Ketonic bodies; Proliferation; S-lactate; S-pyruvate; Tumor glutamate; Tumor glutathione.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of study participants
Fig. 2
Fig. 2
Partial Least Square Discriminant Analysis (PLS-DA) in serum. a Scores plot showing serum samples from the fasting group (green) and carbohydrate group (red). The carbohydrate and fasting groups have significantly different metabolic profiles as evidenced by permutation testing. b Variable Importance in Projection (VIP) scores showing the top 14 metabolites contributing to differences between the groups. The right column indicates increased (red) or decreased (green) metabolite in the indicated group
Fig. 3
Fig. 3
Correlation between serum metabolic profile and serum insulin, insulin C-peptide, and IGFBP3. Samples from carbohydrate-fed patients are shown in red, while samples from fasting patients are shown in blue. Metabolites are colored according to their variable importance in projection (VIP) score and labeled when VIP≥1. a Measured insulin vs. predicted insulin levels based on metabolic profile (cross-validated measurements). b Metabolites versus regression coefficient for insulin. Increased S-glucose, S-lactate, and decreased S-Leucine are important to prediction of serum insulin from the metabolic profile. c Measured insulin C peptide vs. predicted insulin C-peptide levels. d Regression weight plot showing metabolites versus the regression coefficient for insulin C-peptide. Increased S-Glucose, S-Lactate, and decreased S-Leucine are important to prediction of serum insulin C-peptide from the metabolic profile. e Measured Insulin Growth Factor Binding Protein 3 (IGFBP3) vs. predicted IGFBP3 based on metabolic profile. f Regression weight plot showing metabolites versus the regression coefficient for IGFBP3. Increased S-Acetone, S-Glycoproteins, and S-Leucine are important to prediction of serum IGFBP3 from the metabolic profile
Fig. 4
Fig. 4
a Principal Component Analysis (PCA) of tumor metabolites. No grouping of fasting vs carbohydrate groups observed. b Glutathione levels in ER positive tumors. c ROC curve for classification into carbohydrate or fasting group by glutathione concentration in ER-positive tumors. AUC= 0.894; 95%CI=0.0.687-1.000, P=0.002
Fig. 5
Fig. 5
Survival analyses for Tumor-Glutathione, Serum-lactate and Serum-pyruvate. a-c Relapse Free Survival (RFS); d-f Breast Cancer Survival (BCSS); g-i Overall Survival (OS)
Fig. 6
Fig. 6
Pathway analyses in serum metabolites. a Metabolite Set Enrichment Analysis of serum metabolism. Significantly enriched pathways are annotated in the pathway network. The circle size denotes significance of the pathway, and lines denote at least 25% shared metabolites in the pathways. b Ingenuity pathway analysis (IPA) bar chart showing the top 5 functions enriched in the dataset. c IPA pathway network showing the metabolites connected to four microRNAs found to be involved in tamoxifen resistance. Metabolites in green are downregulated in carbohydrate-fed patients, while metabolites in red are upregulated. MicroRNAs are colored purple. d IPA Function plot showing metabolites involved in organismal growth. Orange arrows indicate activation, while blue arrows indicate inhibition

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