Sub-Clustering General Population with High Body Mass Index to Inform Future Risk Of Diabetes
Applied/Translational Research
Care Continuum
Singapore Health Biomedical Congress
National Healthcare Group
31 December 2023
To identify individuals with high BMI and differential risk for diabetes using clustering techniques. BMI clustering provides insights for targeted diabetes prevention in primary care settings.
Year Submitted: 2023
Published Date: 31 December 2023
Tags: Applied/Translational Research, Care Continuum, Quantitative Research, Population Health
About this Content
Aims
To identify individuals with high BMI and differential risk for diabetes using clustering techniques.
Background
Obesity prevalence is increasing in Singapore, necessitating stratified risk assessments for better care.
Methods
Conducted two-step clustering based on BMI, waist circumference, triglycerides-to-HDL ratio, and fasting plasma glucose.
Results
Identified 4 clusters with differing risks; older participants in unhealthier clusters had a significantly higher risk for diabetes.
Conclusion
BMI clustering provides insights for targeted diabetes prevention in primary care settings.
Lessons Learnt
Stratified interventions can efficiently address high-risk clusters and improve metabolic health.
Additional Information
SHBC 2023 Singapore Young Investigator Award (Bronze).
Keywords
BMI, Diabetes Risk, Clustering Methods
Innovators' Details
Innovators' Details
Healthcare Cluster(s) | National Healthcare Group |
Organization(s) Involved | Khoo Teck Puat Hospital, Nanyang Technological University, National University of Singapore |
Platform(s) | Singapore Health Biomedical Congress |
Healthcare Professional Group(s) | Allied Health, Healthcare Administration, Medical |
Applicable Specialty or Discipline | Endocrinology |
Project Lead(s) | Zheng Huili |
Project Member(s) | Chalani Udhyami Ubeynarayana |
Connect with this contributor!
Zheng Huili - zheng.huili@ktph.com.sg