Malnutrition Is an Independent Risk Factor for Low Health-Related Quality of Life Among Centenarians

Quality of life and socio-demographic factors associated with nutritional risk in Brazilian community-dwelling individuals aged 80 and over: cluster analysis and ensemble methods

Introduction

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.

Methods

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).

Results

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.

Conclusion

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.

Relationship Between Dietary Patterns and Chronic Diseases in Rural Population: Management Plays an Important Role in the Link

Objective

Health dietary pattern is related with reduced risk of chronic metabolic disease, but the benefits were not fully clear in the Chinese population. The aim of this study was to explore the association between dietary patterns and multiple chronic metabolic diseases in middle-aged and elderly Chinese.

Methods

A total of 718 Chinese adults aged ≥ 45 who lived in the Huairou regions of Beijing were included in the present cross-sectional analysis from 2019 to 2020. Dietary data were obtained by food frequency questionnaires (FFQs). Dietary patterns were identified by principal components analysis (PCA). Logistic regression analysis and hierarchical analysis were used to examine the relationship among dietary patterns, health management, and chronic diseases.

Results

Five dietary patterns were discovered in the subjects. The pattern with the higher percentage of energy supply by lipid was a risk factor for hypertension [odds ratio (OR) = 2.067, p = 0.013]. Lower energy intake (OR = 0.512, p = 0.012) and a reasonable ratio of dietary energy supply (OR = 0.506, p = 0.011) were beneficial to diabetes. The substitution of potato for grain might be an effective way of reducing diabetes (OR = 0.372, p < 0.001). The higher intake of high-quality protein was the protective factor for coronary heart disease (CHD; OR = 0.438, p = 0.008). Moderate intervention (OR = 0.185, p = 0.033) and appropriate health education (OR = 0.432, p = 0.016) could greatly subserve the prevention of chronic diseases, especially for hyperlipidemia. Men were more likely to be affected by health education, intervention, and follow-up than women. The prevalence of multimorbidity was higher in women (43.2%) than men (41.5%). The staple food intake and health management were also important factors to prevent multimorbidity.

Conclusion

Dietary pattern with appropriate energy intake, a reasonable source of energy supply, high quality of macronutrients, and moderate management was associated with decreased risk of chronic metabolic diseases. Further studies are needed to clarify the cause–effect relationship between dietary patterns, health management, and chronic diseases and give suggestions to chronic metabolic disease prevention in middle-aged and elderly people in a rural area.