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. Issue 9 (10th June 2021)

• Record Type: Journal Article
• Authors: Gervis, Julie E; Chui, Kenneth K H; Ma, Jiantao; Coltell, Oscar; Fernández-Carrión, Rebeca; Sorlí, José V; Barragán, Rocío; Fitó, Montserrat; González, José I; Corella, Dolores; Lichtenstein, Alice H
• Is Part Of: Journal of nutrition. Volume 151:Issue 9(2021)
• Page Start: 2843
• Page End: 2851
• Publication Date: 2021-06-10
• Subjects: taste -- individual differences -- cluster analysis -- sweet -- salt -- sour -- bitter -- umami -- data-driven; Nutrition -- Periodicals; Diet -- Periodicals; 613.205
• DOI: 10.1093/jn/nxab160
• Languages: English
• ISSNs: 0022-3166

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/m 2 ): 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 R 2 : 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.