About SeDia

Background

SeDia is a longitudinal study of 12 years and an open observational cohort of previously diagnosed and newly diagnosed patients with diabetes who registered with National Diabetes Registry (NDR) at Klinik Kesihatan Seremban and their household family members. This study will recruit 10,000 participants among which 5,000 are diabetes patients and 5,000 are their household family members. SeDia is established in Negeri Sembilan as the prevalence of diabetes mellitus in Negeri Sembilan was found to be 33.2%, which is the highest among the states and regions of Malaysia. Besides, the population demographic is closely resembling the Malaysian population (in term of racial distribution, socio-economic status and etcetera).

The lack of real-world longitudinal data is limiting the translation of diabetes precision health in the country.

Most of the diabetes cohort studies conducted had limitations as follows:

1. Majority of the studies focus on western population with minimal involvement of Asian. Furthermore, the socio-demographics and culture might be completely different for Malaysian and may not be directly interpretable into the Malaysian context.
2. Lack of longitudinal studies on the family factors and gene environment in regard to diabetes
3. Most of these studies are considered “opportunistic” and focused on understanding limited factors in isolation and oversimplified complex relationships.
4. Most studies were using conventional methods and did not capitalise on the growing importance of capturing multi-dimensional data using state-of-the-art digital health technology.

Why do we need to set up a diabetes cohort?

1. To address the needs of the country in understanding the causal-effects relationship of diabetes in Malaysia
2. To address the non-linear interactions among genetics/physiology, lifestyle, psycho-social factors, socio-economy, and healthcare delivery in the prevention, management, and control of diabetes
3. To utilise mobile technology, electronic health records and genomics technology to capture multidimensional data from the same individual in the most cost-effective manner, creating opportunities for innovations in achieving better diabetes outcomes
4. To incorporate both observational and interventional elements in the same cohort for comparative effectiveness analyses