Intake of Common Alcoholic and Non-Alcoholic Beverages and Breast Cancer Risk among Japanese Women: Findings from the Japan Collaborative Cohort Study

Association of food groups and dietary pattern with breast cancer risk: A systematic review and meta-analysis

BACKGROUND & AIMS

We conducted a systematic review and meta-analysis of current evidence for the association between food groups, dietary patterns, and breast cancer risk among the Asian population.

METHODS

This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. We performed a systematic literature search up to December 2022 in English in the PubMed, Web of Science, Embase, and Cochrane databases. Risk ratios (RRs) with 95% confidence intervals (CIs) were extracted as effect sizes. Publication bias was estimated by two different funnel plot methods.

RESULTS

We collected the data from 15 cohort studies and 34 case-control studies meeting the search criteria. The meta-analysis found that the consumption of fruits and, likewise, vegetables were associated with a 29% lower risk of breast cancer, respectively [RR = 0.71 (0.55, 0.93); RR = 0.71 (0.53, 0.95)]. By contrast, no significance was found between meat, soy foods, and green tea consumption and breast cancer risk (P > 0.05). However, soy protein and isoflavone intake could lower breast cancer risk by 35% and 32%, respectively [RR = 0.65 (0.51, 0.83); RR = 0.68 (0.55, 0.82)]. As for the dietary pattern, high adherence to a healthy dietary pattern and, similarly, to a healthy eating index was associated with a 38% and 51% reduction in breast cancer risk, respectively [RR = 0.62 (0.44, 0.88; RR = 0.49 (0.27, 0.87)], while high adherence to an unhealthy dietary pattern was associated with a 44% increased risk [RR = 1.44 (1.06, 1.96)]. Considering alcohol consumption, a 75% increased risk of breast cancer was found [RR = 1.75 (1.33, 2.30)].

CONCLUSION

The present meta-analysis found that high intakes of fruits, vegetables, soy protein, and soy isoflavone significantly reduced the risk of breast cancer, while high intake of alcohol had a significantly increased risk. Meat, soy food, and green tea consumption were not significantly associated with breast cancer risk. Considering dietary patterns, high adherence to a healthy eating index and a healthy dietary pattern may reduce breast cancer risk. Conversely, adherence to unhealthy dietary patterns may increase breast cancer risk. However, further studies are needed to confirm the associations between dietary patterns and breast cancer in the Asian population.

Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis

The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine learning can be integrated with conventional statistics in health research. To help address this knowledge gap, this paper presents a review on the conceptual integration between conventional statistics and machine learning, focusing on the health research. The similarities and differences between the two are compared using mathematical concepts and algorithms. The comparison between conventional statistics and machine learning methods indicates that conventional statistics are the fundamental basis of machine learning, where the black box algorithms are derived from basic mathematics, but are advanced in terms of automated analysis, handling big data and providing interactive visualizations. While the nature of both these methods are different, they are conceptually similar. Based on our review, we conclude that conventional statistics and machine learning are best to be integrated to develop automated data analysis tools. We also strongly believe that machine learning could be explored by health researchers to enhance conventional statistics in decision making for added reliable validation measures.

Coffee consumption and breast cancer risk: a narrative review in the general population and in different subtypes of breast cancer

PURPOSE

Most of the existing literature reports no association or a slight negative association between coffee consumption and the risk of developing breast cancer. However, the level of risk differs when considering various subgroups, such as menopausal status, hormonal status of the tumor or genetic mutations. The present review based on a literature search sets the point on the potential influence of a common daily drink, coffee, on the risk of developing breast cancer in the general population, in different subgroups of women and the consequences of drinking coffee after breast cancer has been diagnosed and treated.

RESULTS

This review confirms that in the general population, there is no association between coffee intake and breast cancer risk or a slight protective effect, even at high dosages. Coffee is inversely associated with breast cancer risk in postmenopausal women and in women carrying a BRCA1 mutation. Possible risk differences exist between slow and fast caffeine metabolizers and with weight. Coffee consumption after breast cancer diagnosis and surgery, associated with tamoxifen and/or radiotherapy, reduced the occurrence of early events. The effects of coffee intake are less clear in other subgroups, mainly premenopausal women, women carrying a BRCA2 mutation and tumors with variable hormonal status (positive or negative for ER/PR) and would need additional studies.