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

The impact of Pediatric Metabolic Syndrome on the development of Diabetes and C ardiovascular diseases. Lubna Alnaim, RD, MS Medical Nutrition Sciences University of Kansas Medical Center PVRM 868 class - Fall 2017. Outline:. Introduction a- Definition

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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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