臺灣博碩士論文加值系統

Objectives: Gestational anemia, also known as GA, has emerged as a significant public health concern among women of childbearing age. Concurrently, vitamin D, recognized as a prohormone, has garnered scientific interest due to its regulatory role in hepcidin. However, the precise relationship between vitamin D and the modulation of anemia-related blood biomarkers remains controversial. Therefore, each current investigation aimed to elucidate the following objectives-study 1: aimed to investigate the associations between dietary patterns derived from principal component analysis (PCA) and anemia-related blood biomarkers alongside vitamin D. Study 2: undertook to examine the dietary associations using reduced rank regression (RRR) analysis-derived dietary patterns and serum anemia-related blood parameters in conjunction with vitamin D. Study 3: was employed to reveal the associations between dietary patterns derived from k-means cluster analysis, vitamin D, and gestational anemia, incorporating risk predictions utilizing machine learning algorithms (MLA) support vector machine (SVM), k-nearest neighbor (KNN), naïve bayes (NB), random forest (RF), decision tree (DT)]. Study 4: conducted a comparative analysis between two statistical methods aiming to identify the optimal predictive method for determining the association between dietary patterns, vitamin D insufficiency, and anemia risk.
Methods: A total of 1502 adult pregnant women (> 15 years old) were selected from the National Nutrition Health Survey in Taiwan conducted from 2017 to 2019 (NNHSIT -2017 to 2019). Data collection occurred during the initial trimester, encompassing anthropometric, socioeconomic, and dietary data, including food frequency questionnaires and 24-hour dietary recalls. Dietary patterns were derived using principal component analysis (PCA), reduced rank regression (RRR), and k-means cluster analysis. Linear regression (β coefficient, 95% confidence interval), binomial logistic regression (odds ratio [OR], confidence interval [CI]), relative risk analysis, and machine learning algorithms were employed to explore the associations between serum anemia-related blood parameters, vitamin D, and GA. Additionally, covariate adjustments were made using sociodemographic, anthropometric, and dietary components to ascertain the most accurate predictive associations.
Results: Study 1: revealed significant associations between expectant mothers (EMs) adhering to plant-based dietary patterns (PbDP) and carnivore dietary patterns (CDP) with serum 25-hydroxy cholecalciferol levels. Covariate adjustments indicated that EMs with moderate PbDP consumption exhibited reduced risks for serum folate (OR = 0.60, 95% CI: 0.41, 0.87) and 25-hydroxy cholecalciferol (OR = 0.69, 95% CI: 0.52, 0.93) deficiencies. EMs with the highest CDP consumption demonstrated decreased serum iron (OR = 1.33, 94% CI: 1.02, 1.75), vitamin B12 (OR = 0.25, 95% CI: 0.17, 0.37) and 25-hydroxy cholecalciferol (OR = 0.59, 95% CI: 0.44, 0.80) levels. Similarly, high consumption of dairy and non-dairy alternative dietary pattern (DnDADP) correlated with decreased tendencies for serum folate (OR = 0.67, 95% CI: 0.46, 0.98) and vitamin B12 (OR = 0.66, 95% CI: 0.48, 0.90) concentrations. In Study 2: after accounting for all pertinent factors, linear regression analysis demonstrated a positive correlation between the ferritin related dietary pattern (FrDP) and serum iron levels, along with a tendency towards a negative correlation with serum 25(OH) vitamin D. Pregnant women in the highest FrDP tertile exhibited reduced odds of low serum iron (OR = 0.65, 95% CI: 0.50, 0.85) but increased odds of low 25(OH) vitamin D (OR = 1.79, 95% CI: 1.32, 2.43) levels. Study 3: findings from binomial analysis indicated that individuals following the moderate plant + low animal (MP+LA) dietary pattern exhibited decreased probabilities of low serum iron (OR = 0.45, 95% CI: 0.34, 0.60) and ferritin (OR = 0.27, 95% CI: 0.21, 0.36) but an elevated probability of low serum 25-(OH) vitamin D (OR = 1.47, 95% CI: 1.14, 1.88) levels. The MLA model's accuracy in identifying dietary patterns ranged from 70% to 76%, with sociodemographic and dietary variables being the most influential predictors. In study 4: the final model of logistic regression analysis showed a positive correlation between serum iron levels and the convenience food dietary patter (CFDP), while a negative correlation was noted with total iron binding capacity. Risk assessment indicated a 0.41% reduction in the odds of vitamin D insufficiency among pregnant women with high intake of plant and marine-based dietary pattern (PMDP). Conversely, moderate consumption of RRR-derived DP (CFDP) was associated with a 0.95% increased risk of vitamin D insufficiency.
Conclusions: Overall, the findings of this study elucidate the intricate interplay between dietary patterns, vitamin D status, and gestational anemia risk among pregnant women. Among all the models, RRR stands out as a promising approach for measuring vitamin D deficiency and insufficiency-related anemia risk associations. Serum vitamin D exhibits positive associations with anemia-related blood biomarkers among pregnant women, highlighting the importance of maintaining appropriate serum vitamin D and iron status at the onset of pregnancy.

Objectives: Gestational anemia, also known as GA, has emerged as a significant public health concern among women of childbearing age. Concurrently, vitamin D, recognized as a prohormone, has garnered scientific interest due to its regulatory role in hepcidin. However, the precise relationship between vitamin D and the modulation of anemia-related blood biomarkers remains controversial. Therefore, each current investigation aimed to elucidate the following objectives-study 1: aimed to investigate the associations between dietary patterns derived from principal component analysis (PCA) and anemia-related blood biomarkers alongside vitamin D. Study 2: undertook to examine the dietary associations using reduced rank regression (RRR) analysis-derived dietary patterns and serum anemia-related blood parameters in conjunction with vitamin D. Study 3: was employed to reveal the associations between dietary patterns derived from k-means cluster analysis, vitamin D, and gestational anemia, incorporating risk predictions utilizing machine learning algorithms (MLA) support vector machine (SVM), k-nearest neighbor (KNN), naïve bayes (NB), random forest (RF), decision tree (DT)]. Study 4: conducted a comparative analysis between two statistical methods aiming to identify the optimal predictive method for determining the association between dietary patterns, vitamin D insufficiency, and anemia risk.
Methods: A total of 1502 adult pregnant women (> 15 years old) were selected from the National Nutrition Health Survey in Taiwan conducted from 2017 to 2019 (NNHSIT -2017 to 2019). Data collection occurred during the initial trimester, encompassing anthropometric, socioeconomic, and dietary data, including food frequency questionnaires and 24-hour dietary recalls. Dietary patterns were derived using principal component analysis (PCA), reduced rank regression (RRR), and k-means cluster analysis. Linear regression (β coefficient, 95% confidence interval), binomial logistic regression (odds ratio [OR], confidence interval [CI]), relative risk analysis, and machine learning algorithms were employed to explore the associations between serum anemia-related blood parameters, vitamin D, and GA. Additionally, covariate adjustments were made using sociodemographic, anthropometric, and dietary components to ascertain the most accurate predictive associations.
Results: Study 1: revealed significant associations between expectant mothers (EMs) adhering to plant-based dietary patterns (PbDP) and carnivore dietary patterns (CDP) with serum 25-hydroxy cholecalciferol levels. Covariate adjustments indicated that EMs with moderate PbDP consumption exhibited reduced risks for serum folate (OR = 0.60, 95% CI: 0.41, 0.87) and 25-hydroxy cholecalciferol (OR = 0.69, 95% CI: 0.52, 0.93) deficiencies. EMs with the highest CDP consumption demonstrated decreased serum iron (OR = 1.33, 94% CI: 1.02, 1.75), vitamin B12 (OR = 0.25, 95% CI: 0.17, 0.37) and 25-hydroxy cholecalciferol (OR = 0.59, 95% CI: 0.44, 0.80) levels. Similarly, high consumption of dairy and non-dairy alternative dietary pattern (DnDADP) correlated with decreased tendencies for serum folate (OR = 0.67, 95% CI: 0.46, 0.98) and vitamin B12 (OR = 0.66, 95% CI: 0.48, 0.90) concentrations. In Study 2: after accounting for all pertinent factors, linear regression analysis demonstrated a positive correlation between the ferritin related dietary pattern (FrDP) and serum iron levels, along with a tendency towards a negative correlation with serum 25(OH) vitamin D. Pregnant women in the highest FrDP tertile exhibited reduced odds of low serum iron (OR = 0.65, 95% CI: 0.50, 0.85) but increased odds of low 25(OH) vitamin D (OR = 1.79, 95% CI: 1.32, 2.43) levels. Study 3: findings from binomial analysis indicated that individuals following the moderate plant + low animal (MP+LA) dietary pattern exhibited decreased probabilities of low serum iron (OR = 0.45, 95% CI: 0.34, 0.60) and ferritin (OR = 0.27, 95% CI: 0.21, 0.36) but an elevated probability of low serum 25-(OH) vitamin D (OR = 1.47, 95% CI: 1.14, 1.88) levels. The MLA model's accuracy in identifying dietary patterns ranged from 70% to 76%, with sociodemographic and dietary variables being the most influential predictors. In study 4: the final model of logistic regression analysis showed a positive correlation between serum iron levels and the convenience food dietary patter (CFDP), while a negative correlation was noted with total iron binding capacity. Risk assessment indicated a 0.41% reduction in the odds of vitamin D insufficiency among pregnant women with high intake of plant and marine-based dietary pattern (PMDP). Conversely, moderate consumption of RRR-derived DP (CFDP) was associated with a 0.95% increased risk of vitamin D insufficiency.
Conclusions: Overall, the findings of this study elucidate the intricate interplay between dietary patterns, vitamin D status, and gestational anemia risk among pregnant women. Among all the models, RRR stands out as a promising approach for measuring vitamin D deficiency and insufficiency-related anemia risk associations. Serum vitamin D exhibits positive associations with anemia-related blood biomarkers among pregnant women, highlighting the importance of maintaining appropriate serum vitamin D and iron status at the onset of pregnancy.

GRAPHICAL ABSTRACT II
ABSTRACT III
ACKNOWLEDGMENTS V
CONTENTS VI
LIST OF FIGURES XIII
LIST OF TABLES XV
LIST OF APPENDICES XIX
ABBREVIATIONS XX
CHAPTER 1. INTRODUCTION AND PURPOSES 1
CHAPTER 2. LITERATURE REVIEW 4
2.1. OVERVIEW OF GESTATIONAL ANEMIA 4
2.1.1. Epidemiology of gestational anemia 4
2.1.2. Risk factors and outcomes of gestational anemia 6
2.2. ADVERSE HEALTH EFFECT OF ANEMIA 10
2.2.1. Maternal health outcomes 10
2.2.2. Fetal health outcomes 11
2.3. PHYSIOLOGICAL CHANGES DURING PREGNANCY 11
2.4. OVERVIEW OF VITAMIN D 14
2.4.1. Metabolism of vitamin D 14
2.4.2. Relationships between vitamin D and anemia 16
2.4.2.1. Regulation of iron homeostasis during pregnancy 19
2.4.2.2. Iron absorption 20
2.4.3. Epidemiology of vitamin D deficiency 21
2.4.4. Classifications of vitamin D status 21
2.5. DIETARY REFERENCE INTAKES (DRIs) FOR PREGNANT WOMEN 22
2.6. RELATIONSHIPS AMONG DIETARY PATTERNS, GESTATIONAL ANEMIA, AND SERUM VITAMIN D LEVEL 24
2.6.1. Diet and anemia associations 24
2.6.2. Dietary patterns 24
2.6.2.1. Principal component analysis (PCA) 25
2.6.2.2. Reduced rank regression (RRR) 26
2.6.2.3. K- means cluster analysis 29
CHAPTER 3. AIMS AND HYPOTHESIS 30
CHAPTER 4. MATERIALS AND METHODS 32
4.1. DATA SOURCE 32
4.2. STUDY POPULATION 33
4.2.1. First study 33
4.2.2. Second study 34
4.2.3. Third study 34
4.2.4. Fourth study 35
4.3. DATA COLLECTION 35
4.3.1. Pre-pregnancy Body Mass Index (pBMI) 36
4.3.2. 24-hour dietary recall and food frequency questionnaire (FFQ) 37
4.3.3. Dietary pattern 39
4.3.4. Laboratory quantifications 41
4.3.5. Biochemical analysis of the blood 41
4.4. DEFINITION 42
4.5. STATISTICAL ANALYSIS 43
CHAPTER 5
5.1. STUDY 1 48
5.1.1. RESULTS
5.1.1.1. General characteristics of the women across serum 25-hydroxy cholecalciferol 49
5.1.1.2. Bio-chemical features of the expectant women across the tertile of serum 25- hydroxy cholecalciferol 51
5.1.1.3. DPs (Dietary Patterns) derived by PCA 53
5.1.1.4. The association between dietary patterns (DPs) and blood-related factors associated with anemia related biochemical parameters 54
5.1.1.5. Correlation among dietary patterns (DPs) and the likelihood of having low anemia-related serum blood biomarkers 57
5.1.2. DISCUSSION
5.1.2.1. Association between serum 25-hydroxy cholecalciferol and other factors associated with anemia related serum bio-markers 62
5.1.2.2. The link between four dietary patterns with anemia-related serum blood parameters 63
5.1.3. Strengths and limitations 66
5.1.4. CONCLUSIONS 67
CHAPTER 6
6.1. STUDY 2 69
6.1.1. RESULTS
6.1.1.1. Identification of a dietary pattern related to ferritin 70
6.1.1.2. General characteristics of the subjects across FrDP 71
6.1.1.3. Serum parameters characteristics of the subjects 73
6.1.1.4. Risk associations of a dietary pattern related to ferritin (FrDP) with serum biochemical parameters 75
6.1.2. DISCUSSION
6.1.2.1. Associations of FrDP with other anemia-related serum biomarkers 76
6.1.3. Strengths and limitations 78
6.1.4. CONCLUSIONS 78
CHAPTER 7
7.1. STUDY 3 79
7.1.1. RESULTS
7.1.1.1. Dietary pattern evaluation 80
7.1.1.2. General characteristics of the subjects across the two dietary patterns 81
7.1.1.3. Serum bio-chemical characteristics 82
7.1.1.4. Characteristics of nutrients intake across the cluster 84
7.1.1.5. Binomial analysis of serum biocmecial parameters among the cluster 85
7.1.1.6. Clusters dependent performace measurement of the different machine learning algoriyhms 87
7.1.1.7. Features selection of different covariates based on the machine learning algorithms 88
7.1.2. DISCUSSION
7.1.2.1. Nutritional regimen, blood parameters associated with anemia, and the utilization of machine learning techniques 89
7.1.2.2. The moderate plant plus low animal dietary pattern (MP+LA DP) and its association with iron levels and serum vitamin D status 91
7.1.3. Limitations and strengths 92
7.1.4. CONCLUSIONS 92

CHAPTER 8
8.1. STUDY 4 94
8.1.1. RESULTS
8.1.1.1. Attributes of the participants 95
8.1.1.2. The serum biochemical attributes of pregnant mothers across various levels of vitamin D 97
8.1.1.3. Dietary patterns 98
8.1.1.4. Connections between dietary pattern scores and serum vitamin D status 100
8.1.1.5. Associations between two different types of dietary patterns (PCA and RRR) with serum anemia related biomarkers 101
8.1.1.6. Status of serum anemia-related biomarkers in relation to scores of dietary patterns derived from principal component analysis (PCA) and reduced rank regression analysis (RRR) 103
8.1.2. DISCUSSION
8.1.2.1. Contrasting the dietary patterns derived from PCA and RRR 107
8.1.2.2. PCA and RRR-derived dietary patterns, anemia-related blood biomarkers, and vitamin D status 107
8.1.3. Strengths and limitations 108
8.1.4. CONCLUSIONS 108
CHAPTER 9
9.1. Overall summary 110
9.1.1. Principal component analysis (PCA) 110
9.1.2. Reduced rank regression (RRR) 110
9.1.3. Machine learning approach 111
9.1.4. Comparison of PCA and RRR analyses 111
9.2. CONCLUSIONS 112
REFERENCES 150

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