Variations of disease patterns among populations, geographies, and time periods underpin the use of descriptive and analytical cohort analysis to define causal relationships between exposures and outcomes. This epidemiological knowledge forms the basis for designing clinical experiments to inform practice and policies. In the 1990s, rapid environmental and lifestyle changes among Chinese living in Hong Kong led to a rising prevalence of diabetes. The benefits of structured care in a clinical trial setting motivated the establishment of the Hong Kong Diabetes Register (HKDR) in 1995 as a data-driven quality improvement program accompanied by a biobank. Systematic data collection and analysis revealed the Asian diabetes phenotype characterized by young age at diagnosis, moderately increased BMI, visceral fat excess, and propensity for chronic kidney disease. In 2000, the HKDR protocol was incorporated into the territory-wide electronic medical record (EMR) system with setup of diabetes centers/teams for implementation. This 2-yearly Risk Assessment and Management Programme for Diabetes Mellitus (RAMP-DM) contributed to marked decline in diabetes-related complication and death rates. In 2007, the HKDR and its equations were digitalized to become the first web-based Joint Asia Diabetes Evaluation (JADE) platform for risk stratification and personalized reporting with decision support aimed at closing care gaps in Asia. The HKDR with its multidimensional data provides the fulcrum for building capacity to transform care and discover knowledge, including on the vulnerability, multiple hits, and reduced capacity of β-cells underlying young-onset diabetes. Using well-designed registers and biobanks to redefine diabetes can lead to innovative diabetes prediction, prevention, classification, and treatment with value, quality, and precision.
Juliana C.N. Chan (Fri,) studied this question.
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