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Int J Epidemiol ; 44(2): 423-31, 2015 Apr.
Article in English | MEDLINE | ID: mdl-24729425

ABSTRACT

The Malaysian Cohort study was initiated in 2005 by the Malaysian government. The top-down approach to this population-based cohort study ensured the allocation of sufficient funding for the project which aimed to recruit 100,000 individuals aged 35-70 years. Participants were recruited from rural and urban areas as well as from various socioeconomic groups. The main objectives of the study were to identify risk factors, to study gene-environment interaction and to discover biomarkers for the early detection of cancers and other diseases. At recruitment, a questionnaire-based interview was conducted, biophysical measurements were performed and biospecimens were collected, processed and stored. Baseline investigations included fasting blood sugar, fasting lipid profile, renal profile and full blood count. From April 2006 to the end of September 2012 we recruited a total of 106,527 participants. The baseline prevalence data showed 16.6% participants with diabetes, 46.5% with hypertension, 44.9% with hypercholesterolaemia and 17.7% with obesity. The follow-up phase commenced in June 2013. This is the most comprehensive and biggest cohort study in Malaysia, and has become a valuable resource for epidemiological and biological research. For information on collaboration and also data access, investigators can contact the project leader at (rahmanj@ppukm.ukm.edu.my).


Subject(s)
Chronic Disease/ethnology , Adult , Age Distribution , Aged , Cause of Death , Chronic Disease/mortality , Diabetes Mellitus/ethnology , Diabetes Mellitus/mortality , Female , Gene-Environment Interaction , Humans , Hypercholesterolemia/ethnology , Hypercholesterolemia/mortality , Hypertension/ethnology , Hypertension/mortality , Malaysia/epidemiology , Malaysia/ethnology , Male , Middle Aged , Neoplasms/ethnology , Neoplasms/mortality , Obesity/ethnology , Obesity/mortality , Prevalence , Prospective Studies , Residence Characteristics/statistics & numerical data , Rural Health/statistics & numerical data , Sex Distribution , Socioeconomic Factors , Urban Health/statistics & numerical data
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