Brech Guilherme Carlos, da Silva Vanderlei Carneiro, Alonso Angelica Castilho, Machado-Lima Adriana, da Silva Daiane Fuga, Micillo Glaucia Pegorari, Bastos Marta Ferreira, de Aquino Rita de Cassia
Postgraduate Program in Aging Sciences, Universidade São Judas Tadeu, São Paulo, Brazil.
Laboratory for the Study of Movement, Department of Orthopedics and Traumatology, School of Medicine, Universidade de São Paulo, São Paulo, Brazil.
Front Nutr. 2024 Jan 3;10:1183058. doi: 10.3389/fnut.2023.1183058. eCollection 2023.
The aim of the present study was to use cluster analysis and ensemble methods to evaluate the association between quality of life, socio-demographic factors to predict nutritional risk in community-dwelling Brazilians aged 80 and over.
This cross-sectional study included 104 individuals, both sexes, from different community locations. Firstly, the participants answered the sociodemographic questionnaire, and were sampled for anthropometric data. Subsequently, the Mini-Mental State Examination (MMSE) was applied, and Mini Nutritional Assessment Questionnaire (MAN) was used to evaluate their nutritional status. Finally, quality of life (QoL) was assessed by a brief version of World Health Organizations' Quality of Life (WHOQOL-BREF) questionnaire and its older adults' version (WHOQOL-OLD).
The K-means algorithm was used to identify clusters of individuals regarding quality-of-life characteristics. In addition, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms were used to predict nutritional risk. Four major clusters were derived. Although there was a higher proportion of individuals aged 80 and over with nutritional risk in cluster 2 and a lower proportion in cluster 3, there was no statistically significant association. Cluster 1 showed the highest scores for psychological, social, and environmental domains, while cluster 4 exhibited the worst scores for the social and environmental domains of WHOQOL-BREF and for autonomy, past, present, and future activities, and intimacy of WHOQOL-OLD.
Handgrip, household income, and MMSE were the most important predictors of nutritional. On the other hand, sex, self-reported health, and number of teeth showed the lowest levels of influence in the construction of models to evaluate nutritional risk. Taken together, there was no association between clusters based on quality-of-life domains and nutritional risk, however, predictive models can be used as a complementary tool to evaluate nutritional risk in individuals aged 80 and over.
本研究旨在运用聚类分析和集成方法，评估生活质量、社会人口学因素与巴西80岁及以上社区居民营养风险之间的关联。
这项横断面研究纳入了来自不同社区地点的104名男女个体。首先，参与者回答社会人口学问卷，并采集人体测量数据。随后，进行简易精神状态检查表（MMSE）测试，并使用微型营养评估问卷（MAN）评估其营养状况。最后，通过世界卫生组织生活质量简表（WHOQOL - BREF）及其老年人版本（WHOQOL - OLD）评估生活质量（QoL）。
采用K均值算法根据生活质量特征识别个体聚类。此外，使用随机森林（RF）和极端梯度提升（XGBoost）算法预测营养风险。得出了四个主要聚类。尽管聚类2中80岁及以上有营养风险的个体比例较高，聚类3中该比例较低，但无统计学显著关联。聚类1在心理、社会和环境领域得分最高，而聚类4在WHOQOL - BREF的社会和环境领域以及WHOQOL - OLD的自主性、过去、现在和未来活动及亲密关系方面得分最差。
握力、家庭收入和MMSE是营养状况最重要的预测因素。另一方面，性别、自我报告的健康状况和牙齿数量在评估营养风险模型构建中的影响程度最低。总体而言，基于生活质量领域的聚类与营养风险之间无关联，然而，预测模型可作为评估80岁及以上个体营养风险的补充工具。
