40 research outputs found
μνΈλ₯΄μ° νλ‘ λ¨μΌ μΌκΈ° λ€νμ± λ° νκ²½μμΈ κ° μνΈμμ©κ³Ό λμ₯μ λ°μ μν νμ
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : μκ³Όλν μνκ³Ό, 2021. 2. μ μ μ .Colorectal cancer is one of the most common malignancies worldwide. Risk factors for the development of colorectal cancer include major contributors to energy balance, such as obesity and reduced physical activity. Based on these findings, physical activity, weight loss, and a healthy diet are recommended for the prevention of colorectal cancer. Even though there are individual differences in preventive effects, changes in lifestyle can affect cancer development with respect to metabolism in both the human body and cells. This study aimed to evaluate the association between genetic variants in the mitochondrial citric acid cycle and colorectal cancer to augment the explanation regarding individual differences in energy metabolism as genetic polymorphisms of mitochondria, which has a central role in the energy metabolism at the cellular level. Interactions of single nucleotide polymorphisms (SNPs) in genes of the citric acid cycle with obesity, physical activity, and energy intake on colorectal cancer were also assessed. Furthermore, pairwise SNP-SNP interactions were examined to account for some missing heritability.
Data from the UK Biobank study were used. The study participants comprised of 3,523 colorectal cancer cases and matched 10,522 controls. Obesity was defined using body mass index (BMI) and waist-to-hip ratio (WHR). The participants were classified as obese if BMI is greater than or equal to 30 and severely obese if BMI is greater than or equal to 40. Participants with abdominal obesity were defined as men with a WHR > 0.9 and women with a WHR > 0.85. Participants who had excess energy intake were classified as having an estimated daily energy consumption of more than 2,000 kcal per day for women and 2,500 for men. Participants who performed over 150 minutes of moderate physical activity or 75 minutes of vigorous physical activity throughout the week were classified as those who achieved physical activity for general health benefits. The main effects of the citric acid cycle SNPs were evaluated in the codominant, dominant, and additive models. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for colon and rectal cancer were estimated using a conditional logistic regression model. The false discovery rate was used to correct multiple comparisons.
SUCLG2-rs35494829 was associated with a decreased risk of colon cancer in the dominant model (OR [95% CI]: 0.82 [0.74β0.92]) and additive model (0.82 [0.74β0.92]). The association between SUCLG2-rs35494829 and colon cancer was statistically significant after correcting for multiple comparisons (p=0.0206). The interaction between SDHC-rs17395595 and obesity for colon cancer was found (pinteraction=0.0023), and the significance of this interaction remained after correcting multiple comparisons (corrected pinteraction=0.047). Pairwise SNP-SNP interactions were also evaluated using the attributable proportion (AP) owing to interaction. Negative AP between the citric acid cycle SNPs for colon and rectal cancer with statistical significance is shown as follows. However, the P values did not reach statistical significance.
This study found a significant association between SUCLG2-rs35494829 and colon cancer. A significant interaction between SDHC-rs17395595 and obesity in colon cancer was also shown. This study evaluated the citric acid cycle SNPs, which were nonsynonymous SNPs or SNPs at a splicing site, as a functional candidate locus of the citric acid cycle in colorectal cancer. The findings in this study suggest that obesity could alter the association between variants in the citric acid cycle and colorectal cancer and may provide new insights into the genetic susceptibility and molecular mechanisms of obesity and the citric acid cycle on colorectal cancer.λμ₯μμ μΈκ³μ μΌλ‘ νν μμ’
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μ κ°ννκΈ° μν΄ λ―Έν μ½λ리μ μνΈλ₯΄μ° μ¬μ΄ν΄μ μ μ μ λ³μ΄μ λμ₯μ μ¬μ΄μ μ°κ΄μ±μ νκ°νλ κ²μ λͺ©νλ‘ νλ€. λμ₯μ λ°μ μνμ λν μνΈλ₯΄μ° μ¬μ΄ν΄μ μ μ μμ μλ λ¨μΌ μΌκΈ° λ€νμ±(single nucleotide polymorphism, SNP)μ λΉλ§, μ 체 νλ, μλμ§ μμ·¨ κ° μνΈμμ©λ νκ°νμλ€. λν, μνΈλ₯΄μ° μ¬μ΄ν΄μ SNP-SNP κ° μνΈμμ©λ νκ°νμλ€.
λ³Έ μ°κ΅¬λ UK Biobank μ°κ΅¬μ λ°μ΄ν°λ₯Ό μ¬μ©νμλ€. μ°κ΅¬ μ°Έμ¬μλ€μ 3,523λͺ
μ λμ₯μ νμμ, νμκ΅°μ λν΄ λ§€μΉν 10,522λͺ
μ λμ‘°κ΅°μ ν¬ν¨νλ€. λΉλ§μ 체μ§λμ§μ(body mass index, BMI)μ ν리 λ μλ©μ΄ λλ λΉ(waist to hip ratio, WHR)λ₯Ό μ¬μ©νμ¬ μ μλμλ€. μ°Έκ°μλ€μ BMIκ° 30λ³΄λ€ ν¬κ±°λ κ°μΌλ©΄ λΉλ§μΌλ‘, BMIκ° 40λ³΄λ€ ν¬λ©΄ μ€μ¦ λΉλ§μΌλ‘ λΆλ₯λλ€. 볡λΆλΉλ§μ WHRμ΄ λ¨μ±μμ 0.9 μ΄μ, μ¬μ±μμ 0.85 μ΄μμΌλ‘ μ μνμλ€. μλμ§ μμ·¨λμ΄ κΆκ³ λ μλ³΄λ€ μ΄κ³Όλ μ°Έκ°μλ μ¬μ±μ κ²½μ° ν루 μλμ§ μλΉλμ΄ 2,000 kcal μ΄μ, λ¨μ±μ 2,500 kcal μ΄μμΈ κ²μΌλ‘ μ μνλ€. 150λΆ μ΄μμ μ λΉν μ 체νλ λλ 75λΆ μ΄μμ νλ°ν μ 체νλμ μΌμ£ΌμΌ λ΄λ΄ μνν μ°Έκ°μλ μΌλ°μ μΈ κ±΄κ°μ μ΄μ΅μ μν΄ μ 체νλμ λ¬μ±ν μ°Έκ°μλ‘ λΆλ₯λμλ€. λμ₯μμ λν μνΈλ₯΄μ° μ¬μ΄ν΄ SNPμ effect sizeλ codominant, dominant λ° additive modelμ κ°μ νμ¬ νκ°νμλ€. λμ₯μκ³Ό μ§μ₯μμ λν μ€μ¦λΉ(odds ratio, OR)μ 95% μ λ’° ꡬκ°(95% confidence intervals, 95% CIs)μ μ‘°κ±΄λΆ λ‘μ§μ€ν± νκ· λͺ¨νμ μ¬μ©νμ¬ μΆμ νμλ€. λ€μ€ λΉκ΅λ₯Ό 보μ νκΈ° μν΄ false discovery rateλ₯Ό μ¬μ©νλ€.
SUCLG2-rs35494829λ dominant model (OR [95% CI]: 0.82 [0.74β0.92]) λ° additive model(0.82 [0.74β0.92)μμ λμ₯μμ μν κ°μμ κ΄λ ¨μ΄ μμλ€. λ€μ€ λΉκ΅μ λν 보μ μ ν νμλ SUCLG2-rs35494829μ λμ₯μ μ¬μ΄μ μ°κ΄μ±μ ν΅κ³μ μΌλ‘ μ μνλ€(p=0.0206). SDHC-rs17395595μ λμ₯μμ λν λΉλ§ μ¬μ΄μ κ΅νΈμμ©μ΄ λ°κ²¬λμμΌλ©°(p for interaction =0.0023), λ€μ€ λΉκ΅λ₯Ό κ΅μ ν νμλ μ΄ κ΅νΈμμ©μ μ μμ±μ λ¨μ μμλ€(p for interaction=0.047). pairwise SNP-SNP κ΅νΈμμ©μ κ΅νΈμμ©μΌλ‘ μΈν κΈ°μ¬ λΆμ¨ (attributable proportion of disease due to interaction with both exposures, AP)μ μ¬μ©νμ¬ νκ°νλ€. ν΅κ³μ μΌλ‘ μ μν λμ₯μκ³Ό μ§μ₯μμ λν μνΈλ₯΄μ° μ£ΌκΈ° SNP μ¬μ΄μ μμ APλ₯Ό κ΄μ°°ν μ μμμ§λ§, ν΅κ³μ μΌλ‘ μ μνμ§ μμλ€.
λ³Έ μ°κ΅¬μμ SUCLG2-rs35494829μ λμ₯μ μ¬μ΄μ μ μλ―Έν μ°κ΄μ±μ λ°κ²¬ν μ μμλ€. λν, SDHC-rs17395595μ λμ₯μμ λΉλ§ μ¬μ΄μ μ μν μνΈμμ©λ κ΄μ°°ν μ μμλ€. μ΄ μ°κ΅¬μ κ²°κ³Όλ₯Ό ν΅ν΄, λμ₯μ λ°μμ λν λΉλ§κ³Ό μνΈλ₯΄μ° νλ‘μ λΆμ λ©μ»€λμ¦μ λν μλ‘μ΄ κ·Όκ±°λ₯Ό μ μνκ³ μ νλ€.Abstract i
Contents v
List of Tables viii
List of Figures xi
1. Introduction 1
1.1. Colorectal cancer epidemiology 1
1.2. Well-known risk factors for colorectal cancer 4
1.2.1. Obesity 4
1.2.2. Physical inactivity 4
1.2.3. Energy intake 5
1.3. Cell metabolism as a contributor to energy balance 7
1.4. The mitochondria play a major role in energy metabolism 8
1.5. Mitochondrial citric acid cycle as a biomarker for cancer 10
1.6. Previous studies on the interaction of obesity, physical activity, and energy intake with genetic factors on cancer risk and SNP-SNP interaction in colorectal cancer 11
1.6.1. Previous studies on the interaction of obesity, physical activity, and energy intake with genetic factors in cancer 12
1.6.2. Previous studies on SNP-SNP interactions in colorectal cancer 17
2. Research objectives 27
3. Materials and methods 28
3.1. Study population 28
3.2. Data collection and measurements 29
3.3. Outcome ascertainment 33
3.4. Case and control selection 34
3.5. Genotyping 36
3.6. Marker selection 37
3.7. Statistical analysis 43
4. Results 46
4.1. Characteristics of participants 46
4.2. Citric acid cycle polymorphisms involved in the risk for colon and rectal cancer development 52
4.3. Interaction of the citric acid cycle polymorphisms with obesity, physical activity, and energy intake on the risk of colorectal cancer development 61
4.4. Pairwise SNP-SNP interactions of SNPs within the Citric acid cycle on the risk of colorectal cancer 66
5. Discussion 73
5.1. Previous studies on polymorphisms of the citric acid cycle 77
5.2. Mechanisms of the citric acid cycle for colorectal cancer 84
6. Conclusions 87
7. References 89Docto
λμ¬μ μΌλ‘ 건κ°ν λΉλ§κ³Ό λ§μ± μ κΈ°λ₯ μ ν λ°μ μν κ°μ μ°κ΄μ±
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : 보건λνμ 보건νκ³Ό(보건νμ 곡), 2018. 8. μ‘°μ±μΌ.Introduction: Chronic kidney disease (CKD), known as a global public health problem, has also become important issues threatening public health in Korea. Many epidemiological studies have investigated the association between obesity and kidney disease, supporting that obesity increases the risk of kidney disease. It has been known that most of the increased risk of CKD in obese individuals is primarily due to cardiometabolic factors associated with excess adiposity. However, not all the obese people have metabolic abnormality, and obese people with no metabolic dysfunction have been existed. They have been reported as metabolically healthy obesity (MHO) phenotype. The association between MHO and kidney dysfunction is well unknown, and it is yet to be determined whether MHO is associated with kidney dysfunction. The objective of this study is to investigate the association between MHO and the risk of incident chronic kidney dysfunction for general population of Korea.
Methods: From the Ansung and Ansan community cohort of the Korean Genome and Epidemiology Study (KoGES) data, 8,608 participants were analyzed. The main exposure of this study is MHO. This concept is a combination of metabolic phenotype and the presence or absence of obesity. The participants were divided into four groups based on the body mass index (β₯28kg/γ‘ as obesity) and the metabolic healthy status by using Adult Treatment Panel-β
’ (ATP-β
’): Metabolically healthy non-obesity (MHNO), Metabolically healthy obesity (MHO), Metabolically unhealthy non-obesity (MUNO), and metabolically unhealthy obesity (MUO). The outcome of the present study is kidney dysfunction defined as eGFR <60ml/min/1.73γ‘. To control the potential confounding, socio-demographic variables, behavioral factors, and biochemical factors were adjusted. Cox proportional hazard regression was used to calculate the hazard ratio (HR) with 95% confidence interval (CI) and MHNO is used as the reference. All statistical analyzes are done by using R 3.4.3.
Results: The MHO phenotype represented 4.1% (n=351) of the total analytic sample and 29.9% of the obese population. After adjusting for all covariates, the HR of MHO individuals for incident kidney dysfunction was 1.59 (95% CI, 1.24-2.04), the HR of MUNO individuals was 1.69 (95% CI, 1.51-1.89), and the HR of MUO individuals was 2.03 (95% CI, 1.73-2.38). The HRs of all groups were statistically significant higher, compared MHNO individuals, and presented a linear trend, in order of linearity: MHO, MUNO, MUO.
Conclusion: This study indicated that metabolically healthy obesity may increase the risk of incident kidney dysfunction in Korean adults. We suggest that different obese phenotype have different effect on the risk of incident kidney dysfunction and MHO is not a benign condition. Therefore, it is crucial to identify obesity-metabolic status phenotype in predicting kidney dysfunction incidence risk. Moreover, the proper prevention and treatment of chronic disease including CKD according to the obesity subtype are needed.Chapter 1. INTRODUCTION 1
1.1 Background 1
1.2 Literature Review 4
1.3 Objective 7
Chapter 2. METHODS 8
2.1 Study population 8
2.2 Measurement 10
2.3 Study design 16
2.4 Statistical analysis 17
Chapter 3. RESULTS 18
3.1 Descriptive analysis of the study participants 18
3.2 Association between metabolically healthy obesity and kidney dysfunction 28
3.3 Association between metabolically healthy obesity and kidney dysfunction stratified by sex 32
3.4 Association between metabolically healthy obesity and kidney dysfunction stratified by age group 35
3.5 Association between metabolically healthy obesity and kidney dysfunction stratified by hypertension 39
Chapter 4. DISCUSSION AND CONCLUSION 42
4.1 Discussion 42
4.2 Conclusion 47
REFERENCES 48
ABSTRACT IN KOREAN 53Maste
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무λΆν, μμ§, μ΄μ§μλμμ κ΄κ³λ₯Ό μ°κ΅¬ν νμκ° μλ€. μ
μ§Έ, νλ ¨κ° νμ μ νμμμΈ, μ¦, νμ μ λμμ λν ꡬλΆ, λμλ³ κ΄λ ¨ λ³μΈμ μν₯κ΄κ³ λ±μ΄ λ³΄λ€ κΉμ΄ μκ² μ°κ΅¬λ νμκ° μλ€.I. μλ‘ 1
1. μ°κ΅¬μ νμμ± 1
2. μ°κ΅¬μ λͺ©μ 4
3. μ°κ΅¬μ κ°μ€ 4
4. μ©μ΄μ μ μ 6
5. μ°κ΅¬μ μ ν 7
II. μ΄λ‘ μ λ°°κ²½ 8
1. μ€μκΈ°μ
κ³Ό S-OJT(체κ³μ νμ₯μ§λ¬΄κ΅μ‘νλ ¨) 8
2. μ€μκΈ°μ
S-OJTνλ ¨κ°μ μν κ³Ό μλ 14
3. νλ ¨κ° νμ μ κ°λ
κ³Ό μΈ‘μ 18
4. νλ ¨κ°μ νμ κ³Ό μ
무λΆν, μμ¨μ μ§λ¬΄λκΈ° λ° λ³΄μμ κ΄κ³ 26
III. μ°κ΅¬ λ°©λ² 45
1. μ°κ΅¬ λͺ¨ν 45
2. μ°κ΅¬ λμ 46
3. μ‘°μ¬ λꡬ 46
4. μλ£ μμ§ 57
5. μλ£ λΆμ 58
IV. μ°κ΅¬ κ²°κ³Ό λ° λ
Όμ 61
1. μλ΅μμ μΌλ°μ νΉμ± 61
2. μλ΅μμ μΌλ°μ νΉμ±μ λ°λ₯Έ νμ μ μ°¨μ΄ λΆμ 63
3. μ€μκΈ°μ
S-OJTνλ ¨κ°μ νμ μ λν μ
무λΆν, 보μ λ° μμ¨μ μ§λ¬΄λκΈ°μ μν₯κ΄κ³ 65
4. μ€μκΈ°μ
S-OJTνλ ¨κ°μ νμ κ³Ό μ
무λΆνμ κ΄κ³μμ 보μμ μ‘°μ ν¨κ³Ό 76
5. μ€μκΈ°μ
S-OJTνλ ¨κ°μ νμ κ³Ό 보μμ κ΄κ³μμ μμ¨μ μ§λ¬΄λκΈ°μ μ‘°μ ν¨κ³Ό 79
6. λ
Όμ 83
V. μμ½, κ²°λ‘ λ° μ μΈ 92
1. μμ½ 92
2. κ²°λ‘ 94
3. μ μΈ 97
μ°Έκ³ λ¬Έν 100
λΆλ‘ 107
[λΆλ‘ 1] μλΉμ‘°μ¬μ© μ§λ¬Έμ§ 107
[λΆλ‘ 2] μλΉμ‘°μ¬ κ²°κ³Ό 112
[λΆλ‘ 3] λ³Έμ‘°μ¬μ© μ§λ¬Έμ§ 114
Abstract 119Maste
νκ΅μμ λμ₯μ μ£Όμ μνμμΈμ λν λμ₯μ κΈ°μ¬μνλ μΆμ
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μνκ³Ό μλ°©μνμ 곡, 2016. 8. μ μ μ .Background: Colorectal cancer incidence has increased in Korea in accordance with westernization. We estimated the population attributable fraction (PAF) of well-established risk factors for colorectal cancer, using both nationwide and worldwide risk estimates.
Materials and methods: We estimated the PAFs attributable to tobacco smoking, alcohol consumption, obesity, physical inactivity, and meat intake. Relative risks (RRs) were estimated from the meta-analyses of the studies conducted in both Korean and worldwide populations. Worldwide RRs were obtained from previous studies that reported summary effect sizes of associations between colorectal cancer and each risk factor and included the largest number of studies or colorectal cancer cases. The prevalence of each exposure was calculated by using data from the 2001 Korean National Health Examination Survey. National cancer incidence data from the Korea Central Cancer Registry were used to estimate the number of colorectal cancer cases attributable to each risk factor.
Results: When using RRs estimated in the Korean population, the PAFs of all selected risk factors considered in this study were 44.5% for men and 22.7% for women. The most important risk factor for colorectal cancer among men was alcohol consumption (24.3%) and among women was meat intake (14.2%). When using RRs estimated in worldwide populations, the PAFs were 54.7% for men and 37.3% for women. The most important risk factor among both men and women was red meat intake (men, 23.1%women, 23.0%). When global estimated RRs were applied to the risk factors from the limited numbers of Korean studies (n > 3), the PAFs for all selected risk factors were 55.8% for men and 38.3% for women.
Conclusions: Appropriate lifestyle modifications could decrease risk for colorectal cancer in the Korean population by 55.8% for men and 38.3% for women.INTRODUCTION 1
Epidemiology of colorectal cancer 1
Established risk factors for colorectal cancer 1
Previous studies on attributable fraction of risk for colorectal cancer risk factors 4
Burden of colorectal cancer in Korea 8
Population attributable fraction of risk 8
Objectives 8
MATERIALS AND METHODS 9
Data selection 9
Literature search 9
Inclusion criteria 14
Relative risks for colorectal cancer used to calculate population attributable fractions of risk 14
Estimation of exposure prevalence 15
Cancer incidence in 2013 16
Statistical analysis 16
Calculation of population attributable fractions of risk 17
RESULTS 18
Identification of Korean studies 18
Relative risk estimates in the Korean population 52
Global relative risk estimates population 78
Estimated prevalence of exposure 81
Estimated population attributable fractions (PAFs) of risk 86
DISCUSSION 98
REFERENCES 100
μμ½ 111Maste