Age-varying genetic and environmental impact on Breast Cancer = 유방암의 연령별 유전적 및 환경적 영향

다국어 초록 (Multilingual Abstract)

Breast cancer is a polygenic disease influenced by both genetic and environmental factors. Although numerous studies have examined the genetic information and breast cancer diagnoses across different ancestries and age groups, there is limited research addressing the varying incidence rates based on various factors. A comprehensive understanding of the relationship between breast cancer, age, and various contributing factors is crucial for developing effective strategies for early detection and prevention.
Our study statistically evaluates genetic and lifestyle factors influencing breast cancer development across ancestries and age groups of 10-year cohorts, ranging from 20 to 79. We compared European ancestry groups using White British samples from UK Biobank and Asian ancestry groups using Korean samples from Korean Genome and Epidemiology Study (KoGES).
The European ancestry group exhibited a significant risk of breast cancer on common genetic variant across the age groups from their 30s to 70s, with the highest risk in the 30s age group (OR=4.19, 95% CI 3.07-5.72, p-value = 2.95E-18) and the lowest risk observed in the 60s age group (OR=2.58, 95% CI 2.30-2.90, p-value = 6.14E-54). For the Asian ancestry group, common variant mutations posed a high risk in individuals in their 40s (OR=1.74, 95% CI 1.16-2.61, p-value = 0.0092). For rare variants, BRCA2 shows the highest risk in 30s age group (OR=12.45, 95% CI 2.49-62.29, p-value = 0.0007), BRCA1 (OR=1.82, 95% CI 1.17-2.81, p-value = 0.01) and CHEK2 (OR=2.31, 95% CI 1.24-4.28, p-value = 0.0115) in 40s age group, indicating early onset on breast cancer. Regarding obesity and lifestyle factors, in the European ancestry group, the 60s age group showed an increased breast cancer risk associated with obesity (OR = 1.29, 95% CI 1.11-1.49, p-value = 0.0008) and alcohol consumption (OR = 1.23, 95% CI 1.04-1.44, p-value = 0.0141). Conversely, physical activity was found to reduce the risk (OR = 0.73, 95% CI 0.59-0.91, p-value = 0.0042) within the same age group. In the Asian group, the highest risk of alcohol consumption was observed in their 40s (OR=2.49, 95% CI 1.28-4.84, p-value = 0.0096), and physical activity was associated with the lowest risk in their 50s (OR=0.58, 95% CI 0.38-0.88, p-value = 0.0101).
In this study, the analysis of genetic impact, including both common and rare variants, reveals that being under 50 years of age is usually a significant factor in the onset of breast cancer. Additionally, age is also identified as a critical factor in the onset of breast cancer from an environmental perspective, with varying trends observed across different factors. Future studies are warranted to explore the rare variant data in the Asian ancestry group if such data become available in the future, as this could lead to a better understanding of additional causes of breast cancer.

국문 초록 (Abstract)

유방암의 다양한 원인을 이해하는 것은 조기 발견과 적절한 치료를 통해 예방하는 데 중요합니다. 기존 연구들은 다양한 인구와 연령대를 대상으로 유전 정보와 여러 질병의 진단을 조사했지만, 각 연령대의 유방암 위험을 평가하는 연구가 필요해 보였습니다. 본 연구는 유럽계와 아시아계 인구를 대상으로 유전 및 생활 습관이 유방암 발병에 미치는 영향을 연령대별로 분석하고자 하였습니다. 20세에서 79세까지를 10세 단위로 나누어 분석하였으며, 유럽계 인구는 UK Biobank 데이터를, 아시아계 인구는 KoGES 데이터를 사용하였습니다.
유전적 요인 측면에서, 유럽계 인구는 30대부터 70대까지 흔한 변이와 관련된 높은 위험을 보였으며, 30대(OR=4.19, p-value=2.95E-18)에서 가장 높았습니다. 아시아계 인구에서는 40대(OR=1.74, p-value=0.0092)에서 흔한 변이와 관련된 높은 위험이 관찰되었습니다. 희귀 변이에 대한 분석에서, BRCA2는 유럽계 30대에서, BRCA1과 CHEK2는 40대에서 높은 위험을 나타냈습니다. 생활 습관 측면에서는, 유럽계 인구의 경우 60대에서 비만(OR = 1.29, p-value=0.0008)과 음주(OR = 1.23, p-value=0.0141)가 유방암 위험을 증가시키는 요인으로 작용했으며, 신체 활동은 위험 감소(OR = 0.73, p-value=0.0042)와 관련이 있었습니다. 아시아계 인구에서는 40대의 음주(OR=2.49, p-value=0.0096)와 50대의 신체 활동(OR=0.58, p-value=0.0101)에서 각각 높은 위험과 낮은 위험이 관찰되었습니다.

목차 (Table of Contents)

• Abstract i
• Table of Contents iii
• 1 Introduction 1
• 1.1 Background . 1
• 1.2 Genetic impact (common, rare variants) . 1
• 1.3 Environmental impact 2
• 1.4 Aim 3
• 2 Methods 4
• 2.1 Study population . 4
• 2.1.1 UK Biobank and KoGES 4
• 2.2 Outcome measures 7
• 2.3 Lifestyle measures 8
• 2.4 Genetic data . 8
• 2.5 Statistical methods 9
• 3 Result 11
• 3.1 Descriptives of participants 11
• 3.2 Genetic impact 11
• 3.2.1 Common variant 11
• 3.2.2 Rare variant 12
• 3.3 Environmental impact 15
• 3.3.1 Obesity 15
• 3.3.2 Smoking status . 18
• 3.3.3 Alcohol consumption . 19
• 3.3.4 Physical activity 19
• 4 Discussion 25
• 5 Conclusion 28
• Bibliography 29
• Appendix 35
• Abstract in Korean 41