Day 1 :
- Human Health
New York University Abu Dhabi
Title: The distribution and burden of cardiometabolic risk factors by BMI class in United Arab Emirates
Noncommunicable diseases in the United Arab Emirates are on the rise and appearing in young adults, earlier than other nations. The top leading causes of death in the UAE are cardiovascular disease, type 2 diabetes and cancer. Obesity is a known risk factor associated with these diseases and reported to raise risk for cardiovascular disease by 3 folds and diabetes by 2 folds. In addition to obesity, these complex diseases are also associated with abnormalities in cardiometabolic markers such as glycaemia, increased glycated hemoglobin, central obesity, dyslipidemia and hypertension.
The objective of this study is to investigate the burden of cardiometabolic risk factors according to BMI categories in young adults aged 18 to 40.
Participants from the UAE Healthy Future Study were the study population. Demographic, health and behavioral data was collected through self-reported questionnaires. Anthropometric data and blood pressure were measured and blood samples were collected. Cardiometabolic risk factors were analyzed by age, gender and BMI class. Crude and adjusted prevalence rates were estimated.
A total of 5,126 eligible participants were recruited from February 2016 to December 2018. The age-adjusted prevalence was 30% (95% CI 28.7 - 31.3%) and 26.5% (95% CI 25.2 - 27.7) for overweight and obesity, respectively. Cardiometabolic markers significantly increased as BMI increased across both genders. The burden of abnormal metabolic factors is 3 folds higher in obese people compared with those of normal-BMI. Smoking was found associated with increased burden of metabolic risk factors. In the other hand, low physical activity was not found associated with the burden.
Cardiometabolic abnormalities are clustering in Emirati young adults. Clustering is associated with weight gain and obesity. Further research is being done to investigate how clustering manifests in young adults to prevent the early rise of noncommunicable diseases in the United Arab Emirates.
Oman College of Health Sciences, Oman
Title: The Theraputic Effect Of Abelmoschus Esculentus (Okra) on Rats Induced Diabetes that Throws New Light On Managing Type II Diabetic Patients
Dr. Hassan awarded his Ph.D in Clinical Chemistry in 1997 From USA in the field of Lipoprotein. He has been a professor in Medical Laboratory Sciences Program in Oman College of Health Sciences for more than 20 years. He is a certified clinical scientist of the American Society of Clinical Pathologists in the USA. He aslo holds a Clinical Consultant certificate and Allied Health instructor from American Medical Technologist in the USA. Prior to his work to Oman, He was an Clinical Rsearch Project Consultant in Iso-Tex Radiopharmaceutical Company in Friendswood, Texas, USA and Clinical Scientist in Quest Diagnostics Laboratory in Houston, Texas, USA.
Abelmoschus esculentus (Okra) is one of popular vegetable in many countries. Medically it is an excellent source of potassium, vitamins B, C, antioxidants and calcium. Nowadays it becomes an area of interest due to its antidiabetic effect. In this systematic review we are using mice to model human disease. Genetically and gnomically, the human and the mouse are very similar, with many of the disease-related genes are nearly identical. The major objective of this study was to investigate the therapeutic effect of Abelmoschus esculentus (okra) on diabetic mice, and the impact of the outcome results on type II diabetic patients.
PubMed, Cochrane databases, Access Medicine and Google Scholar search was conducted to find out studies that evaluate the ability of Okra to lower blood glucose on diabetic mice. As the result of the deep search and using inclusion and exclusion criteria and JBI critical appraisal tools 4 articles were selected. All of these articles evaluate the antidiabetic effect of Okra.
All the identified studies confirm the antidiabetic effect of Abelmoschus esculentus in diabetic rates. The results showed clear reduction in the level of blood glucose, HbA1c and others diabetic markers. In addition to this they revealed potential of hypolipidemic, antiinflammatory and anti-cancer effects of okra.
The results of this systematic review confirm that Okra has the potential to be an excellent choice for managing glucose level on type II diabetic patients. However, direct studies on human type II diabetic patients need to be done before confirming its effect in human.
AE: Abelmoschus esculentus. HbA1C: Haemoglobin A1C test. TG: Triglyceride HDL: High Density Lipoprotein. LDL: Low Density Lipoprotein.
Northwestern University Department of German, USA
Title: Type 2 Diabetes Care and Management: A Comparison of German and American Approaches
Sarah has completed her BA in Biological Sciences and German at the age of 22 years from Northwestern University. She is currently applying for medical school for fall 2020 matriculation. She is working as a healthcare analyst at DaVita Integrated Kidney Care on their Patient Education team for her gap year between undergraduate and medical school. She presented her research comparing German and American chronic disease management methods at Northwestern University Research Exposition and has earned Honors recognition from Northwestern for her thesis on this research.
Type 2 diabetes (T2D) affects over 422 million people worldwide. Within their multi-payer healthcare system, Germany has employed Disease Management Programs (“DMP”s) nationwide to manage T2D since 2002. Studies have suggested T2D financial burden reduction and improved health outcomes since DMP implementation. No such standardized programs exist in the fragmented U.S. healthcare-system. This study evaluated German and American primary care physicians’ opinions of efficacy of their nation’s respective T2D management methods in improving health outcomes, healthcare costs, and quality of care. German physicians reported consistent protocol and resource availability for T2D management, while American physicians’ responses varied widely by their clinical network and their patients’ insurers. Strengths of Germany’s T2D DMPs included lack of financial barrier to care and increased frequency of diabetic visits and patient accountability. Weaknesses included bureaucratic documentation and lack of customization. Strengths of American methods revolved around opportunity for innovation, resulting in increasing utilization of technological tools and motivational interviewing techniques. Weaknesses stemmed from systematic inequality of access, including lack of insurance-covered diabetes education, prohibitively high costs of medication, and lack of affordable preventive care. Despite these differences between German and American T2D care, both nations struggle with imperfect patient compliance and difficulty of achieving sustainable lifestyle changes. This study provides primary care physicians’ opinions on best directions forward for chronic disease management, particularly addressing these universal challenges. Recommendations included widespread insurance coverage of dieticians, intensive diabetes education courses, and counselors, and increased use of team-based care, telemedicine & apps improving patient accountability, and value-based reimbursement.
Yonsei University, Seoul, South Korea
Title: Predictive Modeling for unmet needs in medical utilization by machine learning approach: using Korea health panel data 2011~2013 year
Ho Kim, the first author, is on M.P.H. degree candidates from Institute for Health Promotion & Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea. He has published 2 papers in Neurosurgery(2015 Apr;76(4):372-80 and 2014 Aug;75(2):124-33).
Unmet need for medical care in Korea has been increasing due to a lot of reasons such as social, econmic or mental status, which has been associated with health inequalities. Therefore, the aim of this study is to develop predictive models for unmet medical need using both logistic regression model and random forest machine-learning approach. Korea Health Panel data from 2011 to 2013 year, surveyed by Korea Institute for Health and Social Affairs and National Health Insrance Service, were used for this study. There were 38 variables including independent variables such as age, educational level, marital status, economic activity, hosehold income quintile, health insurance type, national pension participation status and others. Dependent variable refered to the experience of an unmet need in using medical service although being needed. Total observations in 12,256 subjects were 30,061 and devided into training (18,037:60%), validation (6,013:20%) and test (6,013:20%). Machine-learning approach with logistic regression model and random forest model were used to predict the unmet need. Overall, 16.2% (n=4,864) of observations experiened unmet need in using medical service even though they wanted or had to use it. The logistic regression model showed area under curve (AUC) as 0.696 and random forest model presented 0.937 AUC in training, validation and test data-set. Conclusivly, prediction result could be different based on which machine-learning approach would be used for unmet need for medical service. And AUC of random forest machine-learning approach is shown to be better than that of logistic regression model.