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台灣年輕成人代謝症候群研究: 趨勢、肥胖重要性及風險預測

為了解決f-y20jh mobile01的問題，作者Bagas Suryo Bintoro 這樣論述：

TABLE OF CONTENTDECLARATION iACKNOWLEDGEMENT iiABBREVIATIONS iiiTABLE OF CONTENT vLIST OF TABLES viiiLIST OF FIGURES xGENERAL ABSTRACT xiiOVERVIEW OF Ph.D DISSERTATION 1Purposes 1Data selection process 21. Taiwan Survey on the Prevalence of Hypertension, Hyperglycemia, and Hyperlipidemia

(TwSHHH) 32. Nutrition and Health Surveys in Taiwan (NAHSIT) 33. Data selection 4PART 1: THE TREND OF HYPERTENSION, DIABETES, AND METABOLIC SYNDROME AMONG YOUNG ADULTS IN TAIWAN 6Abstract 7Chapter 1. Introduction 8Chapter 2. Material and method 111. Study population 112. Data 113. Sta

tistical analysis. 13Chapter 3. Results 151. Age, sex, and MetS components, in TwSHHH 2007 and NAHSIT 2013-2016 152. Hypertension, diabetes, metabolic unhealthiness, and MetS, in TwSHHH 2007 and NAHSIT 2013-2016 153. Awareness, treatment, and control of hypertension and diabetes, TwSHHH 2007

and NAHSIT 2013-2016 164. Diet-related knowledge, attitude, and behavior, by sex and BMI (NAHSIT 2013-2016) 16Chapter 4. Discussion 18Chapter 5. Conclusions 20PART II: THE IMPORTANCE OF OBESITY FOR METABOLIC SYNDROME INCIDENCE AND REMISSION AMONG TAIWANESE YOUNG ADULTS 21Abstract 22Chapter 1.

Introduction 23Chapter 2. Material and methods 281. Study design and population 282. Data collection and measurements 293. Variables 314. Covariates 335. Statistical analysis 35Chapter 3. Results 371. Baseline characteristic 372. Metabolic unhealthiness, and metabolic syndrome diagn

osis and components, in baseline (TwSHHH 2002) and follow up (TwSHHH 2007) 373. Incidence and remission of metabolic unhealthiness and metabolic syndrome (TwSHHH 2007) 374. Probability of incident metabolic syndrome 385. Probability of metabolic syndrome remission 386. The odds ratio of inci

dent MetS in 5 years 387. The odds ratio of MetS cumulative remission in 5 years 398. Sub-group analysis of non-MetS normo-weight individual 39Chapter 4. Discussion 40Chapter 5. Conclusion 43PART III: DEVELOPMENT OF RISK PREDICTION FOR METS AND DIABETES IN TAIWANESE YOUNG ADULTS USING NON-LAB

ORATORY METHOD 44Abstract 45Chapter 1. Introduction 46Chapter 2. Material and Method 491. Study population 492. Data 503. Statistical Analysis 52Chapter 3. Results 541. Baseline characteristic 542. Cumulative incidence of MetS and diabetes per 1000 person in 5 years, in TwSHHH 2007 54

3. Odds ratios of prevalent MetS and incident MetS in 5 years 544. Odds ratios of prevalent diabetes and of incident diabetes in 5 years 555. The area under ROC curves for prevalent and incident outcomes prediction 556. Calibration of predictive model for 5-years MetS and diabetes incidence

567. Risk score for 5-year metabolic syndrome and diabetes incidence 56Chapter 4. Discussion 57Chapter 5. Conclusions 60PART IV. GENERAL DISCUSSION AND FUTURE DIRECTIONS 61Chapter 1. General discussion 62Chapter 2. Future directions 67The research 671. To analysis with sample weighting 672

. To find the appropriate cut-off of risk category 673. To compare our prediction with artificial intelligence method 674. To validate the prediction scores in other populations 685. To evaluate the feasibility of early interventions 68The policy 691. To raise awareness of healthy lifestyl

es among young adults 692. To develop the friendly health promotion strategies 693. To encourage healthy lifestyles with support of policy 71TABLES AND FIGURES 72Overview section 72Part I 77Part II 82Part III 98REFERENCES 112APPENDIX 129LIST OF TABLESTable 1. Body mass index cut-off 31T

able 2. List of studies that using TwSHHH data for research in CVDs topic, in recent 10 years 72Table 3. List of studies that using NAHSIT data for research in CVDs topic, in recent 10 years 74Table 4. Age sex, and component of metabolic syndrome in the TwSHHH 2007 and NAHSIT 77Table 5. Prevalenc

e of hypertension, diabetes, metabolic unhealthiness and metabolic syndrome (MetS), in the TwSHHH 2007 and NAHSIT 2013-2016 78Table 6. Prevalence of hypertension, diabetes, metabolic unhealthiness, and metabolic syndrome in the TwSHHH 2007 and NAHSIT 2013-2016, sensitivity analysis 79Table 7. Awar

eness, treatment, and control of hypertension and diabetes, TwSHHH 2007 and NAHSIT 2013-2016 80Table 8. Diet-related knowledge, attitudes, and behaviors by sex and the body-mass index in NAHSIT 2013-2016 81Table 9. Baseline characteristic from TwSHHH 2002 (n= 2004) 82Table 10. Metabolic unhealthi

ness, and metabolic syndrome diagnosis and components, in baseline (TwSHHH 2002) and follow up (TwSHHH 2007) (n=2004) 83Table 11. Incidence and remission of metabolic unhealthiness and metabolic syndrome, in TwSHHH 2007 84Table 12. Sociodemographic and health characteristics of non-MetS normo-weig

ht individuals for sub-group analysis (n=1104) 96Table 13. Odd ratios of outcomes in non-MetS normo-weight individuals for sub-group analysis, by metabolic unhealthiness (n=1104) 97Table 14. Prevalence of metabolic syndrome and related components in TwSHHH 2002 (n=2004) 98Table 15. Cumulative inc

idence of MetS and diabetes per 1000 person in 5 years, from the NHIS, TwSHHH 2002, and TwSHHH 2007 (n=2004) 99Table 16. Odds ratios of prevalent MetS and of incident MetS in 5 years, by selected predictors 100Table 17. Odds ratios of prevalent diabetes and of incident diabetes in 5 years, by sele

cted predictors 102Table 18. Area under receiver-operating characteristic curve for prevalent and incident metabolic syndrome and diabetes predictions in young adults, from the NHIS and TwSHHH 2002 to TwSHHH 2007. 104 LIST OF FIGURESFigure 1. Participant flowchart of TwSHHH 3Figure 2. Flow chart

of participants from the TwSHHH 2007 and NAHSIT 2013-2016 study included in final analyses of Study 1 11Figure 3. Flow chart of participants from the NHIS, TwSHHH 2002 and TwSHHH 2007 study included in final analyses of Study 2 29Figure 4. Flow chart of participants from the NHIS, TwSHHH 2002 and

TwSHHH 2007 study included in final analyses of Study 3 50Figure 5. Probability of incident metabolic syndrome in 5 years, by BMI-ASIAN (n=1782) 85Figure 6. Probability of incident metabolic syndrome in 5 years, by BMI-PHAA (n=1782) 86Figure 7. Probability of incident metabolic syndrome in 5 year

s, by metabolic health - BMI ASIAN (n=1782) 87Figure 8. Probability of incident metabolic syndrome in 5 years, by metabolic health - BMI PHAA (n=1782) 88Figure 9. Probability of metabolic syndrome remission in 5 years, by BMI-ASIAN, (n=220) 89Figure 10. Probability of metabolic syndrome remission

in 5 years, by BMI-PHAA, (n=220) 90Figure 11. Odds ratio of incident MetS in 5 years, by BMI-ASIAN (n=1782) 91Figure 12. Odds ratio of incident MetS in 5 years, by BMI-PHAA (n=1782) 92Figure 13. Odds ratio of incident MetS in 5 years, by MetS-BMI-ASIAN (n=1782) 93Figure 14. Odds ratio of MetS i

ncidence in 5 years, by MetS-BMI-PHAA (n=1782) 94Figure 15. Odds ratio of MetS remission in 5 years, by BMI (n=220) 95Figure 16. Area under receiver-operating characteristic curve for metabolic syndrome and diabetes predictions in young adults, from the NHIS and TwSHHH 2002 to TwSHHH 2007. 105Fig

ure 17. Calibration of models for prevalent and incident metabolic syndrome in 5-years 106Figure 18. Calibration of models for prevalent and incident diabetes in 5-years 107Figure 19. Probability of incident metabolic syndrome in 5-years 108Figure 20. Relative risk scores of incident metabolic sy

ndrome in 5-years 109Figure 21. Probability of incident diabetes in 5-years 110Figure 22. Relative risk score of incident diabetes in 5-years 111