Data-Driven Clustering Approach to Derive Taste Perception Profiles from Sweet, Salt, Sour, Bitter, and Umami Perception Scores: An Illustration among Older Adults with Metabolic Syndrome

Author:

Gervis Julie E1ORCID,Chui Kenneth K H2,Ma Jiantao3,Coltell Oscar45ORCID,Fernández-Carrión Rebeca56,Sorlí José V56,Barragán Rocío56ORCID,Fitó Montserrat567,González José I56,Corella Dolores56,Lichtenstein Alice H1

Affiliation:

1. Cardiovascular Nutrition Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA

2. Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, USA

3. Department of Nutrition Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA

4. Department of Computer Languages and Systems, University of Jaume I, Castellón, Spain

5. CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain

6. Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain

7. Cardiovascular Risk and Nutrition Research Group, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain

Abstract

ABSTRACT Background Current approaches to studying relations between taste perception and diet quality typically consider each taste—sweet, salt, sour, bitter, umami—separately or aggregately, as total taste scores. Consistent with studying dietary patterns rather than single foods or total energy, an additional approach may be to study all 5 tastes collectively as “taste perception profiles.” Objective We developed a data-driven clustering approach to derive taste perception profiles from taste perception scores and examined whether profiles outperformed total taste scores for capturing individual variability in taste perception. Methods The cohort included 367 community-dwelling adults [55–75 y; 55% female; BMI (kg/m2): 32.2 ± 3.6] with metabolic syndrome from PREDIMED-Plus, Valencia. Cluster analysis identified subgroups of individuals with similar patterns in taste perception (taste perception profiles); quantitative criteria were used to select the cluster algorithm, determine the optimal number of clusters, and assess the profiles’ validity and stability. Goodness-of-fit parameters from adjusted linear regression evaluated the individual variability captured by each approach. Results A k-means algorithm with 6 clusters best fit the data and identified the following taste perception profiles: Low All, High Bitter, High Umami, Low Bitter & Umami, High All But Bitter and High All But Umami. All profiles were valid and stable. Compared with total taste scores, taste perception profiles explained more variability in bitter and umami perception (adjusted R2: 0.19 vs. 0.63, respectively; 0.40 vs. 0.65, respectively) and were comparable for sweet, salt, and sour. In addition, taste perception profiles captured differential perceptions of each taste within individuals, whereas these patterns were lost with total taste scores. Conclusions Among older adults with metabolic syndrome, taste perception profiles derived via data-driven clustering may provide a valuable approach to capture individual variability in perception of all 5 tastes and their collective influence on diet quality. This trial was registered at https://www.isrctn.com/ as ISRCTN89898870.

Funder

FEDER

Universitat Jaume I

Generalitat Valenciana

USDA

Tufts University

Publisher

Oxford University Press (OUP)

Subject

Nutrition and Dietetics,Medicine (miscellaneous)

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