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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Vydáno v:	Public health nutrition Ročník 28; číslo 1; s. e89
Hlavní autoři:	Matousek, Adriano, Leung, Tiffany H, Pang, Herbert
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cambridge, UK Cambridge University Press 21.04.2025
Témata:	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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Abstract	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.
AbstractList	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. Objective: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).Design: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.Setting:Nutritional and demographical data from 176 countries were analysed from the GDD.Participants:Population representative samples of the 176 in the GDD.Results: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.Conclusion: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. Abstract Objective: 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). Design: 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. Setting: Nutritional and demographical data from 176 countries were analysed from the GDD. Participants: Population representative samples of the 176 in the GDD. Results: 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. Conclusion: 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. 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. This study aims to extend an existing multivariate time-series clustering algorithm to allow for greater customisability and to provide the first cluster analysis of the Global Dietary Database to explore temporal trends in country-level nutrition profiles (1990-2018).OBJECTIVEUnderstanding 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. This study aims to extend an existing multivariate time-series clustering algorithm to allow for greater customisability and to provide the first cluster analysis of the Global Dietary Database 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 program 'MTSclust'. Time-series clustering algorithms are different from simple clustering approaches in their ability to appreciate temporal elements.DESIGNTrends in sugar-sweetened beverage intake and nutritional deficiency were explored using the newly developed program '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 Global Dietary Database.SETTINGNutritional and demographical data from 176 countries were analysed from the Global Dietary Database.Population representative samples of the 176 in the Global Dietary Database.PARTICIPANTSPopulation representative samples of the 176 in the Global Dietary Database.In a 3-class test specific to the domain, the MTSclust program 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. Multivariate time-series clustering demonstrated a global convergence towards a Western diet.RESULTSIn a 3-class test specific to the domain, the MTSclust program 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. Multivariate time-series 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.CONCLUSIONWhile 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.
ArticleNumber	e89
Author	Matousek, Adriano Leung, Tiffany H Pang, Herbert
Author_xml	– sequence: 1 givenname: Adriano surname: Matousek fullname: Matousek, Adriano organization: Department of Public Health and Primary Care, University of Cambridge, Robinson Way, Cambridge, UK – sequence: 2 givenname: Tiffany H orcidid: 0009-0002-1154-8234 surname: Leung fullname: Leung, Tiffany H organization: Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China – sequence: 3 givenname: Herbert orcidid: 0000-0002-7896-6716 surname: Pang fullname: Pang, Herbert email: pathwayrf@gmail.com organization: PD Data Science & Analytics, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
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Snippet	Understanding country-level nutrition intake is crucial to global nutritional policies that aim to reduce disparities and relevant disease burdens. Still,... Objective:Understanding country-level nutrition intake is crucial to global nutritional policies that aim to reduce disparities and relevant disease burdens.... Abstract Objective: Understanding country-level nutrition intake is crucial to global nutritional policies that aim to reduce disparities and relevant disease...
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SubjectTerms	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
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Title	Multivariate time series clustering analysis of the Global Dietary Database to uncover patterns in dietary trends (1990–2018)
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