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
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
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DOI：
10.1093/jn/nxab160
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发表时间：
2021-06-10
影响因子：
4.2
通讯作者：
Lichtenstein, Alice H.
中科院分区：
文献类型：
--
作者：
Gervis, Julie E.;Chui, Kenneth K. H.;Lichtenstein, Alice H.
背景资料：目前研究味觉与饮食质量之间关系的方法通常将每种味道--甜、盐、酸、苦、鲜味--单独或综合考虑为总味觉评分。与研究饮食模式而不是单一食物或总能量相一致，另一种方法可能是将所有5种味道作为“味觉感知特征”一起研究。“目的：我们开发了一种数据驱动的聚类方法，从味觉感知评分中获得味觉感知谱，并检查谱是否优于总味觉评分，以捕获味觉感知中的个体差异。方法：该队列包括367名来自瓦伦西亚PREDIMED-Plus的患有代谢综合征的社区居民成年人[55-75岁; 55%女性; BMI（kg/m2）：32.2 +/- 3.6]。聚类分析确定了具有相似味觉模式的个体亚组（味觉曲线）;使用定量标准来选择聚类算法，确定最佳聚类数，并评估曲线的有效性和稳定性。从调整后的线性回归拟合优度参数评估捕获的每个approaches.Results的个体变异性：一个k-means算法与6个集群最适合的数据，并确定了以下味觉配置文件：低所有，高苦，高鲜味，低苦和鲜味，高所有但苦和高所有但鲜味。所有曲线均有效且稳定。与总味觉评分相比，味觉感知谱解释了苦味和鲜味感知的更多变异性（调整后的R-0：分别为0.19与0.63;分别为0.40与0.65），并且对于甜味、咸味和酸味具有可比性。此外，味觉感知档案捕获个人内的每种口味的不同看法，而这些模式都失去了总的味道scores.Conclusions：在老年人代谢综合征，味觉感知档案通过数据驱动的聚类可能提供了一个有价值的方法来捕捉个人变异的感知所有5种口味和集体对饮食质量的影响。
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-0: 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.