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UK - Department for Business, Energy and Industrial Strategy

Innovating behaviour and health surveillance for cardiovascular disease prevention in Malaysia

Disclaimer: The data for this page has been produced from IATI data published by UK - Department for Business, Energy and Industrial Strategy. Please contact them (Show Email Address) if you have any questions about their data.

Programme Data Last Updated: 23/03/2022

IATI Identifier: GB-GOV-13-FUND--Newton-MR_T018984_1


In Malaysia the prevalence of obesity among adolescents is increasing and non-communicable diseases, like type 2 diabetes and cardiovascular disease, account for two out of three deaths. While research in European and US-based populations has found that particular lifestyle factors might cause adolescents to be fatter and less fit we do not have a complete picture of what causes these problems in Malaysia. In this study we are going to look at how different lifestyle factors, like foods eaten, timing or frequency of eating, physical activity, sedentary behaviours and their timing or location, relate to whether adolescents have good health. We plan to use a reproducible laboratory technique, known as metabolomics, to measure 150 different components of blood that indicate a range of metabolic processes. This will help us find out in much more detail than ever before how behaviour leads to better cardiovascular health via metabolic pathways. When we know more about the pathway that leads from lifestyle to disease we will be better able to predict who will stay healthy and who will not. This research is possible because of an ongoing large community study including over 6000 adolescents, called SEACO, in Segamat, Malaysia. The aim of SEACO is to monitor changes in population health using annual surveys. Data collection started in 2012 and is repeated yearly from over 13000 households. The participants have already had measurements of their height and weight at two previous times, which we will use to look at how body size changes in different groups. We will approach children in the cohort at school to collect samples of blood and urine and ask them to wear an activity monitor on their wrist for 7 days. In a smaller selection of the sample we'll measure metabolic components in urine and see if they can tell us about what foods have been eaten recently. We also give a small group of children a smartwatch to ask them regular questions about eating so we can explore the possibility of measuring eating behaviour using the activity monitors without have to ask in future. We are interested to see whether lifestyle behaviours are associated with changes in the blood chemical profile of participants before they develop clear symptoms of cardiovascular disease or diabetes. Finally, within this project we will lay the foundations for improving the measurement of food intake to make methods passive, rather than relying on participants to tell you every time they eat.


The overall aim of the project is to combine state-of-the-art metabolic phenotyping and novel objective measures of diet and physical activity in a community-based Malaysian cohort study. South East Asia Community Observatory (SEACO) health and demographic surveillance system (HDSS) cohort is a dynamic prospective community cohort of 13,335 households in Segamat, Malaysia including 6759 Malaysian children age 6-19yrs. We aim to combine state of the art epidemiology with methodological innovation to achieve 4 interlinked research objectives: 1. Identify associations of prospective of parent cardiovascular risk, age, ethnicity and urbanicity changes in child BMI. 2. Collect blood samples in a random sample of n=1500 children aged 7-17 years, measure in-depth metabolite profiles and identify associations of parent and sociodemographic factors with metabolic health. 3. Collect objective wrist-worn accelerometry, derive time-series physical activity data and examine associations with metabolic health. 4. Conduct sub-studies designed to innovate dietary assessment by exploring 1) the potential of urinary biomarkers to indicate food intake and 2) wearable sensors to identify eating occasions. We will use novel dietary data to investigate associations with metabolic health. Metabolomics is the comprehensive quantification of metabolites in human tissue and can emerging evidence suggests specific metabolites future cardiometabolic disease, before the onset of clinical symptoms. We will collect blood samples in SEACO using a school-based protocol in a random sample 1500 of children aged 7 to 17 years and analyse them using an automated high-throughput serum nuclear magnetic resonance (NMR) metabolomics platform. This end-to-end platform provides quantitative molecular information >150 measures, including 129 lipid measures (lipoprotein particle subclasses, particle size, cholesterols, fatty acids and apolipoproteins), 9 glycerides and phospholipids, and 20 low-molecular weight metabolites including branched-chain and aromatic amino acids, glycolysis-related markers, and ketone bodies. This will enable a deeper understanding of cardiometabolic health and strengthen the evidence for potential targets of interventions. Using literature collated by the FoodBall consortium (http://foodmetabolome.org) on putative biomarkers for more than 100 foods we will design a targeted assay for urinary metabolite analysis by liquid chromatography-triple quadrupole mass spectrometry (LC-QQQ-MS). We will select metabolites with the best evidence on validity and specificity from cohorts and dose-response intervention studies to indicate key foods identified in Malaysian diets. We will apply this assay to n=750 urine samples collected in SEACO children and examine associations between specific food biomarkers and metabolic health To explore the potential for passive measurement of eating architecture (timing and frequency of eating occasions) we will combine wearable sensor data (including wrist-worn accelerometry from objective 2) with a sub-study in n=150 collecting detailed eating behaviour data collected in real-time using ecological momentary assessment on smartwatches. We will apply machine learning techniques to labelled ground truth data and apply the model to generate eating architecture variables in n=1500 to look at associations with metabolic health. The findings of the proposed research will provide novel insights into the mechanisms linking behaviour to cardiometabolic disease and create a unique resource including a biobank of blood and urine samples combined with objective behavioural measures for future use in research.

Status - Implementation More information about Programme status
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Participating Organisation(s) More information about implementing organisation(s)

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