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研究生:吳琲文
研究生(外文):WU, PEI-WEN
論文名稱:青少年含糖飲料攝取與心臟血管異常因子之聚集:危險因子之中介結構以及代謝症候群穩定性與型態變化之探討
論文名稱(外文):Sugar-sweetened Beverage Intake and Clustering of Cardiometabolic Risk Factors in Adolescents: Investigation of Mediating Structure and Assessment of Metabolic Syndrome Stability and Transformation
指導教授:李建宏李建宏引用關係
指導教授(外文):LEE, CHIEN-HUNG
口試委員:葉志嶸白其卉邱弘毅王姿乃
口試委員(外文):YEH, CHIH-JUNGBAI, CHYI-HUEYCHIOU, HUNG-YIWANG, TSU-NAI
口試日期:2022-06-07
學位類別:博士
校院名稱:高雄醫學大學
系所名稱:公共衛生學系博士班
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:英文
論文頁數:129
中文關鍵詞:青少年代謝症候群含糖飲料
外文關鍵詞:adolescentsmetabolic syndromesugar-sweetened beverage
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研究背景
青春期多項心臟代謝異常因子的聚集與成年期罹患心臟血管疾病顯著相關。含糖飲料為飲食攝入果糖的主要來源。果糖在肝臟可代謝生成尿酸,而尿酸可透過抑制內皮功能與降低一氧化氮濃度,影響血糖與血壓的恆定。過去的研究指出,飲用含糖飲料與體內升高的胰島素阻抗有關,而胰島素阻抗可影響多個代謝的病理和生理機制,且可能引發心臟代謝功能的紊亂。因此,含糖飲料的攝食可能經由胰島素阻抗與尿酸的生理機制,影響心臟代謝風險因子的聚集。此外,多項心臟代謝異常因子的聚集暗示著,某種潛在的代謝結構或機制可能與此種群聚有關。以流行病學方法探討此類潛在的代謝結構與組成,及其與含糖飲料攝食、胰島素阻抗和尿酸之間的關聯,有助於未來對青少年健康促進策略的擬定。另一方面,回顧國內外相關的文獻後發現,台灣青少年代謝症候群的穩定性及其變化形態缺乏專一的了解,需要深入的研究加以探索。

研究目的
本研究包含三個研究目的:(一)評估含糖飲料攝取與心臟血管異常因子聚集之間的關係,以及胰島素阻抗於兩者之關聯性的中介角色與修飾作用;(二)使用結構方程模型,分析胰島素阻抗與尿酸值,在含糖飲料攝取和代謝症候群成分因子潛在結構的中介效應;(三)探討青少年心臟血管代謝危險因子的潛在結構及其穩定性,並剖析代謝症候群的變化形態與其變化形態的決定因素。

材料方法
兩項南台灣青少年心臟血管代謝健康的系列研究被建構以探討研究問題。第一項為以多步驟分層隨機抽樣法進行之橫斷式研究,共召募1454位具代表性的青少年。研究者使用問卷調查進行參與者的基本人口學特質、飲食習慣及身體活動情形等生活型態的資料收集,並進行人體測量學與臨床生化學檢測。兩類穩態模型評估胰島素抵抗(homeostasis model assessment-insulin resistance, HOMA-IR)之指數(HOMA1-IR和HOMA2-IR)被用以測量參與者之胰島素阻抗狀況。研究者使用主成分分析簡化12項心血管疾病相關變數的維度,保留解釋大部分總變異的主成分因子,並計算此些主成分的分數,連同代謝症候群之狀態與異常成分因子之個數作為結果變數。此外,研究者使用結構方程式評估胰島素阻抗與尿酸在含糖飲料攝取與代謝症候群潛在結構之關聯性的中介作用。第二項為以多步驟分層隨機抽樣法召募國中一年級學生的追蹤型研究,參與者來自三種社會經濟發展程度的地區,總數為1516位青少年,每位參與者平均被追蹤2.2年。此研究以四種代謝症候群的診斷標準進行青少年追蹤前與追蹤後代謝症候群狀態之穩定性評估。研究者使用探索性因素分析評估個案於追蹤前後心血管代謝風險因子潛在結構的穩定性。

研究結果
第一項研究結果顯示,HOMA1-IR和HOMA2-IR均與心血管危險因子顯著相關。相較於沒有攝取含糖飲料者,攝取>500 mL/day含糖飲料之青少年具有顯著較高的胰島素阻抗數值(高出0.22–0.37個單位);攝取>500 mL/day手搖高果糖飲料者亦具有較高的代謝症候群異常成分因子個數(高出0.22個);其中,胰島素阻抗解釋了33.9–37.9%的含糖飲料攝取與代謝症候群異常成分因子個數的關聯性。主成分分析保留了與體重、血脂和血壓關聯較大的主成分因子;其中,胰島素阻抗亦解釋了26.5–31.0%的攝取>500 mL/day含糖飲料與體重主成分分數的關聯性。此外,攝取>350 mL/day手搖高果糖飲料與胰島素阻抗對體重主成分分數具有顯著的交互作用(Pinteraction < 0.05)。結構方程式的分析結果顯示,手搖高果糖飲料攝取量>500 mL/day對HOMA-IR數值與尿酸數值具有顯著的直接效應(both p < 0.05;標準化β係數分別為0.09和0.15),且HOMA-IR與尿酸亦對代謝症候群的潛在結構具有顯著的直接效應。瓶裝含糖飲料與手搖高果糖飲料>500 mL/day之攝取均經由HOMA-IR與尿酸之間接路徑對代謝症候群潛在結構發揮影響性;其中HOMA-IR的中介效應佔>500 mL/day手搖高果糖飲料攝取對代謝症候群潛在結構32.1%;尿酸則分別佔了瓶裝與手搖飲料攝取對代謝症候群潛在結構之總間接效應的100.0%與67.9%–100.0%。

第二項研究結果顯示,參與者於第一年與第三年在心血管代謝風險因子中皆具有一個相似的脂肪-血壓-血糖因子之潛在結構。然而,二分類的代謝症候群診斷結果在2.2年的追蹤期間並不穩定。第一年被歸類為代謝症候群的青少年中,52.0–61.9%於第三年轉變為無代謝症候群的狀態,但仍有38.1–48.0%維持代謝症候群的狀態。此外,收縮壓增加的變化程度與代謝症候群的發生風險呈現顯著的正相關,而收縮壓和血糖值降低的變化程度,與代謝症候群的改善呈現顯著的正相關。與第一年代謝症候群成分因子正常的青少年相比,第一年為中央型肥胖的青少年,兩年後為中央型肥胖的風險為15.0倍;第一年為三酸甘油脂過高的青少年,兩年後為三酸甘油脂過高的風險為5.7倍。

結論
第一項研究指出,含糖飲料攝取與青少年心臟血管危險因子之聚集顯著相關,且胰島素阻抗狀況解釋兩者之間的部分關聯性。胰島素阻抗對體重相關之心臟代謝危險因素的不利影響取決於含糖飲料使用的類型,高量攝食手搖高果糖飲料的效應比攝食瓶裝含糖飲料的效應高。此外,含糖飲料攝取對代謝症候群潛在結構的影響力可能經由胰島素阻抗與高尿酸的間接路徑而作用,其中尿酸解釋大部分的中介效應。第二項研究指出,中央型肥胖與低高密度脂蛋白膽固醇為青春期兩個高度持續存在的代謝症候群異常成分因子,為降低未來心血管代謝疾病風險的介入標的。

Background
The early onset of abnormal cardiometabolic factors aggregation predisposed adolescents to cardiovascular disease development in adulthood. Insulin resistance (IR) can derive a series of metabolic disturbances and promote cardiometabolic dysfunction. Sugar-sweetened beverages (SSBs) were the main source of fructose consumption in diet. In the process of fructose metabolism, production of uric acid was linked to serum glucose homeostasis and blood pressure maintenance through inhibiting the endothelial function and reducing endothelial nitric oxide levels. The SSBs consumption is an important issue in adolescent health, since that SSB consumption may be related to abnormal cardiometabolic factors aggregation via pathophysiological relationship with IR and uric acid production in adolescents. Alternatively, the formation of metabolic syndrome (MetS) was driven by a pathophysiological construct underlying MetS components. Investigations of metabolic structures underlying metabolic components, and the associations with SSB consumption, IR, and uric acid will provide the promising health promotion strategies for adolescents. However, the knowledge about the stability and transformation of MetS in adolescents is limited, and further research is needed.

Aims
The aims of this series of study have 3 folds: (1) to evaluate possible mediating and modifying effects of homeostatic model assessment-based IR (HOMA-IR) on the association between SSB intake and the clustering of cardiometabolic abnormalities in adolescents; (2) to evaluate possible mediation effects of HOMA-IR and uric acid levels on the relationship between SSB intake and the structure underlying MetS components using structural equation modeling (SEM); (3) to investigate the latent clustering structure and its stability for MetS during adolescence, and assess the anthropometric and clinical metabolic determinants for MetS transformation.

Methods
We conducted two investigations to answer our research questions. First, a large-scale cross-sectional study with 1454 representative adolescents was conducted in southern Taiwan. Comprehensive data on sociodemographic factors, dietary intakes, physical activity, and anthropometric and biochemical parameters were obtained. The original (HOMA1-IR) and updated nonlinear (HOMA2-IR) HOMA-IR indicators were used as IR biomarkers. Principal component (PC) analysis was employed to create reduced groups of variables and risk scores for retained PCs. The MetS, the number of MetS abnormal component, and a group of PC scores were used to measure the clustering of adolescent cardiometabolic abnormalities. The mediation effects of HOMA-IR and uric acid on the association between SSB intake and the metabolic syndrome construct (MetsC) were evaluated using SEM. Second, a community-based representative adolescent cohort (n = 1516) in 3 different socioeconomic areas was conducted to evaluate the MetS stability using 4 diagnostic criteria, and was followed for 2.2 years to identify new-onset MetS. The exploratory factor analysis was used to investigate the latent factor clustering structure across the cardiometabolic parameters for baseline and follow-up surveys.

Results
Our first study showed that IR biomarkers were positively associated with a constellation of adolescent cardiometabolic abnormalities. Compared with SSB nondrinkers, adolescents with >500 mL/day of SSB consumption had a 0.22–0.37 increase in the levels of IR biomarkers. Adolescents who consumed >500 mL/day of hand-shaken high-fructose corn syrup beverages (HHB) had a 0.22 increase in the number of abnormal MetS components, and HOMA-IR mediation explained 33.9–37.9% of the effect. Results from principal component analysis showed that 3 PCs were retained from 12 cardiometabolic risk factors, and enhanced by body weight-, lipids-, and blood pressure (BP)-related factors, respectively. IR biomarkers accounted for 26.5–31.0% of the relationship between >500 mL/day of SSB consumption and bodyweight-enhanced PC scores. The effects of HOMA-IR indicators on all bodyweight-related factors were consistently intensified among >350 mL/day HHB drinkers (all Pinteraction < 0.05). Furthermore, the >500 mL/day of HHB intake had direct effects on HOMA-IR and uric acid levels (both p < 0.05; standardized β, 0.09 and 0.15, respectively). The HOMA-IR and uric acid levels had significant direct effects on the MetsC. The >500 mL/day of BSB and HHB intake had indirect influence on the MetsC through the HOMA-IR and uric acid. The HOMA-IR values accounted for 32.1% of the indirect effects of >500 mL/day of HHB consumption on the MetsC. The uric acid levels accounted for 100.0% and 67.9%–100.0% of the indirect effects of BSB and HHB intake on the MetsC, respectively.

Our second study showed that a fat—BP—glucose factor structure underlying cardiometabolic parameters was stable at baseline and follow-up (total variance explained: 68.8% and 69.7% at baseline and follow-up, respectively). The MetS status exhibited unstable by 4 MetS criteria over the two years of follow-up. Among adolescents with MetS-positive at baseline, 52.0–61.9% experienced MetS remission, and 38.1–48.0% experienced MetS persistence. Changes in increased of systolic BP (SBP) were associated with a high MetS incidence risk, while changes in decreased of SBP and glucose values were associated with an increased likelihood of MetS remission. Compared with adolescents with a normal metabolic status at baseline, those with an initial abdominal obesity and increased triglycerides level had a 15.0- and 5.7-fold greater risk for persistent abnormality, respectively.

Conclusions
The first study indicated that fructose-rich SSB consumption was linked to a constellation of adolescent cardiometabolic abnormalities, and the association was partly explained by HOMA-IR levels. The adverse effects of HOMA-IR on bodyweight-related cardiometabolic risk factors were enhanced in the intake of high amounts of HFCS-containing SSBs. However, SSBs consumption had no direct effects on the MetsC after controlling the mediation of HOMA-IR and uric acid. Heavy HHBs had indirect effects on the MetsC through HOMA-IR and uric acid, with a great proportion of mediation explained by uric acid. In our second study, abdominal obesity and low high-density lipoprotein cholesterol levels were highly persist during adolescence. These 2 metabolic components may serve as screening targets for more beneficial cardiometabolic disorder improvement than preventing new-onset abnormal components.

Content
中文摘要 I
Abstract V
致 謝 IX
論文內容發表說明 1
Introduction 1
1.1 The importance of metabolic syndrome in adolescents 2
1.1 Research background and purposes 1 3
1.2 Research background and purposes 2 4
1.3 Research background and purposes 3 5
1.4 Research background and purposes 4 6
1.5 Research background and purposes 5 7
Summary of research purpose 9
Literature review 11
2.1 The relationship between IR and MetS 12
2.2 The relationship between sweetened-beverage consumption, IR and MetS 13
2.3 The relationship between SSB consumption and hyperuricemia 14
2.4 The transformation of MetS in adolescents 15
2.5 The cardiometabolic risk assessment of youth aged 12-16 years 16
Materials and Methods 18
For Topic 1 and Topic 2 19
3.1 Participants 19
3.2 Methods 19
3.3 Constellation of cardiometabolic abnormalities 23
3.4 Statistical analysis 23
For Topic 3 27
4.1 Participants 27
4.2 Methods 28
4.3 Statistical Analysis 30
Results (Topic 1) 32
Results (Topic 2) 37
Results (Topic 3) 41
Discussion (Topic 1) 45
Discussion (Topic 2) 51
Discussion (Topic 3) 57
Conclusions 62
References 64
Tables and Figures 76
Figure 1. Schematic diagram of data analysis for possible associations between sugar-sweetened beverage (SSB) intake, homeostasis model assessment (HOMA)-based insulin resistance (IR) and cardiometabolic (CarMet) risk. 77
Figure 2. Adjusted linear regression lines of HOMA1-IR values against, (A) waist circumference (WC), and (B) body mass index (BMI) associated with the type and the amount of sugar-sweetened beverage (SSB) intake. 78
Figure 3. The structural equation modeling for potential mediation of HOMA-IR and uric acid on the association between sugar-sweetened beverage consumption and the structure underlying MetS risk factors. 79
Figure 4. Reviews of cardiometabolic homeostasis system 80
Figure 5. One-factor CFA model with standardized factor loadings. 81
Figure 6. The model fitness of the structural equation modeling with potential causal relationship between amount of SSB intake, HOMA-IR, uric acid, and the metabolic syndrome construct (MetsC). 82
Figure 7. The model fitness of the structural equation modeling with potential causal relationship between SSB type-amount combinations, HOMA-IR, uric acid, and the metabolic syndrome construct (MetsC). 83
Figure 8. The direct effects of SSB intake, HOMA-IR, and uric acid levels on the metabolic syndrome construct (MetsC) in the structural equation modeling. 84
Figure 9. The direct effects of SSB type-amount combinations, HOMA-IR, and uric acid levels on the metabolic syndrome construct (MetsC) in the structural equation modeling. 85
Figure 10. Scree plots for exploratory factor analysis of 9 metabolic risk variables measured at (A) baseline and (B) follow-up, respectively. 86
Table 1. Distributions of demographic characteristics, metabolic syndrome factors, and potential confounders in adolescents, stratified by the use of sugar-sweetened beverage 87
Table 2. Factor loadings, proportions of variance explained and factor characteristics for the first third principal components of cardiometabolic risk factors in adolescents 88
Table 3. Adjusted effects of sugar-sweetened beverage intake on HOMA-IR indicators in adolescents 89
Table 4. Adjusted effects of HOMA-IR indicators on metabolic syndrome factors and principal component risk scores in adolescents 90
Table 5. Adjusted associations of sugar-sweetened beverage intake with the clustering of cardiometabolic abnormalities and the excessive association explained by HOMA-IR indicators in adolescents 91
Table 6. Adjusted effects of sugar-sweetened beverage (SSB) intake on principal components 2 (PC2) and 3 (PC3) in adolescents 92
Table 7. Adjusted main and interaction effects of sugar-sweetened beverage intake and HOMA-IR indicators on the clustering of cardiometabolic abnormalities in adolescents 93
Table 8-1. Adjusted main and interaction effects of sugar-sweetened beverage intake and HOMA-IR on enhanced factors in PC1 scores 94
Table 8-2. Adjusted main and interaction effects of sugar-sweetened beverage intake and HOMA-IR on enhanced factors in PC1 scores 95
Table 9. Adjusted effects of sugar-sweetened beverage intake on the clustering of cardiometabolic abnormalities and the excessive association explained by HOMA-IR indicators in adolescents 96
Table 10. Adjusted main and interaction effects of sugar-sweetened beverage intake and HOMA1-IR indicators on body weight–enhanced cardiometabolic risk factors in adolescents 97
Table 11-1. Characteristics of study participants stratified by the daily sugar-sweetened beverage intake categories 98
Table 11-2. Metabolic components and clinical parameters study participants stratified by the daily sugar-sweetened beverage intake categories 99
Table 11-3. Adjusted effects of sugar-sweetened beverage intake on HOMA1-IR and uric acid in adolescents 100
Table 12. Comparison of the one-factor CFA model fit index 101
Table 13. The unstandardized factor loadings and standardized factor loadings between indicators of metabolic syndrome in one-factor model 102
Table 14. Comparison of the structural equation modeling fit index 103
Table 15-1. The unstandardized and standardized direct effects of SSB consumption on HOMA-IR and uric acid levels in the structural equation modeling 104
Table 15-2. The direct effects, indirect effects, and total effects of SSB consumption, HOMA-IR, and uric acid levels on the metabolic syndrome construct (MetsC) in the structural equation modeling 105
Table 16-1. The unstandardized and standardized direct effects of SSB type-amount combinations on HOMA-IR and uric acid levels in the structural equation modeling 106
Table 16-2. The direct effects, indirect effects, and total effects of SSB type-amount combinations, HOMA-IR, and uric acid levels on the metabolic syndrome construct (MetsC) in the structural equation modeling 107
Table 17. Demographic and cardiometabolic risk factors of the adolescent cohort measured at baseline and follow-up 108
Table 18. Exploratory factor analysis-derived factors, factor loadings, and proportions of variance explained for cardiometabolic risk factors measured at baseline and follow-up in adolescents 109
Table 19. Baseline and follow-up prevalences and proportions of metabolic syndrome transformation in adolescents over the 2 years of follow-up 110
Table 20. Distributions and changes of cardiometabolic risk factors between baseline and follow-up for never, incident, remitted, and persistent metabolic syndromea in adolescents 111
Table 21. Adjusted associations of the changes in cardiometabolic risk factors over 2 years of follow-up with incident, remitted, and persistent metabolic syndromea in adolescents 112
Table 22. Baseline prevalences, follow-up incidence densities, and adjusted hazard ratios of ab-normal components of metabolic syndrome associated with initial status over 2 years of follow-up in adolescents 113
Table 23. Adjusted effects of sugar-sweetened beverage intake on HOMA-IR indicators, adjusting for covariates and waist circumference 114
Supplementary Table 1. Diagnostic criteria for the determination of metabolic syndrome defined by IDF, TPA, JIS-Adult, and IDF–TPA 115


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