Coffee and tea consumption, patient-reported, and clinical outcomes in a longitudinal study of patients with breast cancer

Abstract

Background: Higher consumption of coffee and tea has been associated with improved health outcomes in the general population and improved breast cancer (BC) prognosis. This study investigated patterns of coffee and tea consumption and association with patient-reported outcomes (PROs) and clinical outcomes among survivors of BC.

Methods: The authors included survivors of stage I-III BC enrolled in the CANTO cohort (NCT01993498) that provided post-treatment assessment of coffee and tea consumption from years 1 to 4 after diagnosis. Group-based trajectory modeling clustered patients according to daily consumption of coffee and tea. Multivariable mixed models and Cox models examined associations between consumption, PROs and clinical outcomes.

Results: Among 3788 patients, the authors identified four stable patterns of consumption: "Low" (25.8%), "Moderate" (37.6%), "High" (25.3%), and "Very high" (11.3%), corresponding to <1, 2, 3, and ≥ 4 cups of coffee and/or tea per day. Patients in the "Very high" group (vs. "Low"), were more likely to be younger, smokers, with higher monthly income and education. PROs and survival outcomes were similar across the four groups.

Conclusions: Over one in three survivors of BC reported high or very high consumption of coffee and/or tea. The authors found no association between higher consumption of coffee and/or tea, worse PROs and clinical outcomes.

Keywords: breast cancer; coffee; patient-reported outcome measures; survivors; tea.

Conflict of interest statement

Ines Vaz‐Luis reports honoraria from Amgen, Pfizer, Novartis, and AstraZeneca. Barbara Pistilli reports consulting/advising for Puma Biotechnology, Novartis, Myriad Genetics, and Pierre Fabre; meeting or travel support from Novartis, AstraZeneca, MSD Oncology, and Pfizer; and research funding from Daiichi, Puma Biotechnology, Novartis, Merus, AstraZeneca, Pfizer and AstraZeneca. Fabrice André reports research funding from Novartis, Pfizer, AstraZeneca, Eli Lilly, Daichii, and Roche. Paul Cottu reports funding from Pfizer and Nanostring; consulting fees from Pfizer, Roche, Lilly, Novartis, Daichi, and Seagen; payment for lecture presentations from Pfizer; and payment for manuscript writing from Novartis. Lucia Del Mastro reports grants or contracts from Eli Lilly, Novartis, Roche, Daiichi Sankyo, and Seagan; payment or honoraria from Roche, Novartis, Pfizer, Eli Lilly, AstraZeneca, MSD, Seagen, Gilead, Pierre Fabre, Eisai, Exact Sciences, and Ipsen; support for attending meetings or travel from Roche, Pfizer, and Eisai; and participation on a Data Safety Monitoring Board or Advisory Board from Novartis, Roche, Eli Lilly, Pfizer, Daiichi Sakyo, Exact Sciences, Gilead, Pierre Fabre, Eisai, and AstraZeneca. Olivier Tredan reports payment or honoraria from Roche, Pfizer, Novartis‐Sandoz, Lilly, MSD, Astra‐Zeneca, Pierre Fabre, Seagen, Daiichi‐Sankyo, Gilead, and Eisai; support for attending meetings and/or travel from Roche, Pfizer, Novartis‐Sandoz, Lilly, MSD, Astra‐Zeneca, Pierre Fabre, Seagen, Daiichi‐Sankyo, Gilead, and Eisai; and support for Participation on a Data Safety Monitoring Board or Advisory Board from Roche, Pfizer, Novartis‐Sandoz, Lilly, MSD, Astra‐Zeneca, Pierre Fabre, Seagen, Daiichi‐Sankyo, Gilead, and Eisai.

© 2022 The Authors. Cancer published by Wiley Periodicals LLC on behalf of American Cancer Society.

Figures

FIGURE 1

Evolution of mean model‐based C30 Summary Score, Fatigue, Pain and Insomnia scores (EORTC QLQ‐C30) over time (A–D, respectively). In functional scales, higher scores indicate a better condition. For symptom scales, higher scores are indicative of a worse symptomatology. (E and F) Evolution of mean model‐based Hospital Anxiety and Depression scores (HADS) over time. For HADS scale, higher scores indicate a worse symptomatology, scoring: noncase (score, 0–7), doubtful case (8–10), and case (11–21). All values are model‐based multivariable‐adjusted average scores obtained from mixed models including time, trajectory group for coffee and tea consumption, time × group interaction, and covariates available at baseline.