Multivariate time series clustering analysis of the Global Dietary Database to uncover patterns in dietary trends (1990–2018)

Understanding country-level nutrition intake is crucial to global nutritional policies that aim to reduce disparities and relevant disease burdens. Still, there are limited numbers of studies using clustering techniques to analyse the recent Global Dietary Database (GDD). This study aims to extend a...

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Veröffentlicht in:Public health nutrition Jg. 28; H. 1; S. e89
Hauptverfasser: Matousek, Adriano, Leung, Tiffany H, Pang, Herbert
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge, UK Cambridge University Press 21.04.2025
Schlagworte:
Adolescent
Adult
Aged
Algorithms
Beverages
Cluster Analysis
Clustering
Clustering algorithm
Databases, Factual
Demographic variables
Demographics
Demography
Diet - statistics & numerical data
Diet - trends
Diet Surveys
Female
Geography
Global Dietary Database
Global health
Global Health - statistics & numerical data
Humans
Literature reviews
Machine learning
Male
Middle Aged
Multivariate Analysis
Nutrient deficiency
Nutrition
Nutritional epidemiology
Public health
Research Paper
Sugar
Sugar-Sweetened Beverages - statistics & numerical data
Time series
Time-series clustering
Trend
Trends
Variables
Young Adult
Trend
Global Dietary Database
Time-series clustering
Clustering algorithm
Machine learning
ISSN:1368-9800, 1475-2727, 1475-2727
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Zusammenfassung:Understanding country-level nutrition intake is crucial to global nutritional policies that aim to reduce disparities and relevant disease burdens. Still, there are limited numbers of studies using clustering techniques to analyse the recent Global Dietary Database (GDD). This study aims to extend an existing multivariate time series (MTS) clustering algorithm to allow for greater customisability and provide the first cluster analysis of the GDD to explore temporal trends in country-level nutrition profiles (1990-2018). Trends in sugar-sweetened beverage intake and nutritional deficiency were explored using the newly developed programme 'MTSclust'. Time series clustering algorithms are different from simple clustering approaches in their ability to appreciate temporal elements. Nutritional and demographical data from 176 countries were analysed from the GDD. Population representative samples of the 176 in the GDD. In a three-class test specific to the domain, the MTSclust programme achieved a mean accuracy of 71·5 % (adjusted Rand Index [ARI] = 0·381) while the mean accuracy of a popular algorithm, DTWclust, was 58 % (ARI = 0·224). The clustering of nutritional deficiency and sugar-sweetened beverage intake identified several common trends among countries and found that these did not change by demographics. MTS clustering demonstrated a global convergence towards a Western diet. While global nutrition trends are associated with geography, demographic variables such as sex and age are less influential to the trends of certain nutrition intake. The literature could be further supplemented by applying outcome-guided methods to explore how these trends link to disease burdens.
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ISSN:1368-9800
1475-2727
1475-2727
DOI:10.1017/S136898002500059X