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Lubna Alnaim, RD, MS Medical Nutrition Sciences University of Kansas Medical Center

This study explores the correlation between pediatric Metabolic Syndrome (MetS) and the development of Diabetes and Cardiovascular Diseases. It analyzes the criteria for diagnosing MetS, its significance, and implications on the young population, focusing on obesity trends and risk factors affecting children and adolescents in the United States. The research aims to assess the demographic differences and disease risks associated with MetS, utilizing retrospective data analysis and statistical methods to determine the impact and progression of metabolic diseases among youth, emphasizing the importance of early intervention and lifestyle changes.

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Lubna Alnaim, RD, MS Medical Nutrition Sciences University of Kansas Medical Center

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  1. The impact of Pediatric Metabolic Syndrome on the development of Diabetes and Cardiovascular diseases Lubna Alnaim, RD, MS Medical Nutrition Sciences University of Kansas Medical Center PVRM 868 class -Fall 2017

  2. Outline: • Introduction a- Definition b- MetS. criteria c- Mechanism II. Objectives III. Methods a- Study Design b- Statistical Analysis IV. Results V. conclusion VI. Limitations VII. Future Implications

  3. What is Metabolic Syndrome (MetS) • Metabolic syndrome may be diagnosed when a patient has a cluster of risk factors. Individuals with metabolic syndrome have an increased risk for Cardiovascular Disease and Diabetes when compared with individuals without metabolic syndrome. * • * American Heart Association

  4. MetS criteria: • Specific cluster of at least three out of five diagnostic criteria: 1- Abdominal Obesity: WC ≥ 90%ile or BMI ≥ 95th %ile. 2- High Blood Pressure: SBP ≥ 135 or DBP ≥ 85 HH mg 3- Elevated Fasting Blood Glucose ≥ 100 mg/dl 4- Elevated Triglyceride level ≥ 150 mg/dl 5- Low levels of High-Density Lipoprotein (HDL) ≤ 40 mg/dl

  5. Why Metabolic Syndrome Matters ? • The prevalence of obesity has tripled among children and adolescents in United States over last 3 decades reaching epidemic proportion(CDC, 2012). • Around 8% of children with obesity aged from 8- to 11-year-olds AND 35% of obese children aged from 12- to 14-year olds have metabolic syndrome(NHANES, 2012). • Evidence indicates that metabolic syndrome is a major risk factor that predict 2-fold increased risk of cardiovascular disease (CVD) and 5-fold increased risk of diabetes (Grundy,2004) • Therapeutic lifestyle Changes (TLC) have been shown to reverse the pathophysiology of the metabolic syndrome, improve biomarkers of risk, and treat comorbidities.

  6. Purpose: • Aim 1: Assess demographic difference of metabolic syndrome (MetS) among obese children and adolescents using metabolic syndrome criteria. • Aim 2: Examine the effect of MetS on developing type II Diabetes, and Cardiovascular Diseases among this population.

  7. Study Timeline(From 2000-2017) Define as Metabolic Syndrome Aged 2-19 Years old 3 months Disease Risk First Dx. of Cardiovascular disease Hypertension Diabetes II Stroke BMI % ile TG BP FG HDL

  8. Methodology • Study Design: Stage 1: Retrospective data obtained from the HERON Repository EMR database from University of Kansas Medical Center (KUMC) Study Variables: Demographic: Age from 2-19 years old Visit Vital: BMI ≥ 95th %ile. Laboratory test : TG, HDL, and FG values. ICD-9 and ICD-10 codes: type II Diabetes, Ischemic heart disease, stroke and Hypertension

  9. Methodology Stage 2: Define the study population that have Metabolic syndrome within 3 months and variables of interest for the research questions using SQLite program.

  10. Methodology • Stage 3:Statistical Analysis to examine the demographic prevalence of Metabolic Syndrome in pediatric and risk of development one of the metabolic diseases. • Descriptive analysis: to characterize the demographic data for MetS cohort. • Logistic Regression: to evaluate the effect of each MetS criteria on the likelihood of developing the disease. • Survival Analysis: to analyze the duration of time till the diseases developed. • SPSS statistic 23 program (SAS program)

  11. Study Flowchart 174421 obese children aged from 2-19 years ( defined as BMI ≥ 95 %ile ) Stage 1 ge1: 1979 patients with obesity aged from 2-19 years who has at least one of MetScriteria (from 2000 to 2017) 818 patients meet at least 3 MetS criteria ( 679 patients with 3 criteria and 140 with 4 criteria ) Stage 2 Exclude patients with clinical history of diseases before MetS 109 patients develop one of the diseases after MetS

  12. Descriptive Analysis: Age Analysis:

  13. Gender:

  14. Cont. Descriptive analysis: Race:

  15. Development of disease risk • 13.3 % of patients developed one of the diseases after having Metabolic Syndrome • Around 50% of cases (50 of 109) developed Hypertension.

  16. Logistic Regression AGE: Every unit increase in age increases the likelihood of a disease by 11% HDL: Every unit increase in HDL decreases the likelihood of disease by about 5% No. MetS: , those with 4 MetS are twice as more likely to have a diseases than those with 3 MetS factors. * Mean ± SD

  17. Survival Analysis (Kaplan-Meier) Cases with 4 MetS factors may develop the disease faster compered with cases with 3 MetSfactors: Mean (3 MetS): 662 days Mean (4 MetS): 335 days (Day) There is a significant difference in the time to disease development between the two groups of subjects.

  18. Conclusion: • Demographic differences between patients with Metabolic Syndrome in this sample population • With cardiovascular disease, obesity, and type 2 diabetes reaching epidemic proportions, it is of great importance to understand and control the risk factors at an early age. • Complications from metabolic syndrome may develop in less than 10 years among children and adolescents. • These data further confirm the need for research, public health, and clinical collaboration in combatting childhood MetS.

  19. Limitation • Retrospective design: the result may not be generalized to all obese children • Lack information of lifestyle factors may influence on the MetS such as dietary habits and physical activity level. • Waist Circumference (WC) is not measured in this population.

  20. Future implications: • Design prospective study with control group (obese population without MetS) • Assess other biomarkers that are additional components of metabolic syndrome, such as urine albumin and C-reactive protein • Examine the role of the intervention and medication on improving the risk factors. • Assess the severity of MetS using MetS. Severity Score (MetSSS).

  21. Challenges • Define the sample size before requesting the dataset. • some variables are poorly documented. In my case, only few patients have Fasting Glucose (FG). Thus, failing to examine the effect of having 5 criteria of MetS on developing disease risk. • In term of BMI% ile, there were only two values: 95 and 97th %ile. failing represent the severity of obesity degree.

  22. Acknowledge • Project coordinators: Dr. Russ Waitman. Director of Medical informatics, KUMC Ms. Maren Wennberg Mrs. Tamara McMahon • Statistician: Dr. Xing Song, PhD Duncan Ritch • Colleagues • Mentor: Dr. Debra Sullivan, KUMC Dr. Brooke Sweeny, CMH

  23. Thank You

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