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. 2024 Feb 23;16(5):612.
doi: 10.3390/nu16050612.

Sleep Quality, Nutrient Intake, and Social Development Index Predict Metabolic Syndrome in the Tlalpan 2020 Cohort: A Machine Learning and Synthetic Data Study

Guadalupe Gutiérrez-Esparza  1   2 , Mireya Martinez-Garcia  3 , Tania Ramírez-delReal  4 , Lucero Elizabeth Groves-Miralrio  3 , Manlio F Marquez  5 , Tomás Pulido  6 , Luis M Amezcua-Guerra  3 , Enrique Hernández-Lemus  7   8
Affiliations

Sleep Quality, Nutrient Intake, and Social Development Index Predict Metabolic Syndrome in the Tlalpan 2020 Cohort: A Machine Learning and Synthetic Data Study

Guadalupe Gutiérrez-Esparza et al. Nutrients. .

Abstract

This study investigated the relationship between Metabolic Syndrome (MetS), sleep disorders, the consumption of some nutrients, and social development factors, focusing on gender differences in an unbalanced dataset from a Mexico City cohort. We used data balancing techniques like SMOTE and ADASYN after employing machine learning models like random forest and RPART to predict MetS. Random forest excelled, achieving significant, balanced accuracy, indicating its robustness in predicting MetS and achieving a balanced accuracy of approximately 87%. Key predictors for men included body mass index and family history of gout, while waist circumference and glucose levels were most significant for women. In relation to diet, sleep quality, and social development, metabolic syndrome in men was associated with high lactose and carbohydrate intake, educational lag, living with a partner without marrying, and lack of durable goods, whereas in women, best predictors in these dimensions include protein, fructose, and cholesterol intake, copper metabolites, snoring, sobbing, drowsiness, sanitary adequacy, and anxiety. These findings underscore the need for personalized approaches in managing MetS and point to a promising direction for future research into the interplay between social factors, sleep disorders, and metabolic health, which mainly depend on nutrient consumption by region.

Keywords: Mexico City; Tlalpan 2020 cohort; balancing methods; features selection; machine learning; nutrients; poor quality sleep; social development index.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Experimental process.
Figure 2
Figure 2
The most important variables obtained through logistic regression for men and women before data balancing.
Figure 3
Figure 3
PCA of features of men for metabolic syndrome with clusters.
Figure 4
Figure 4
PCA of features of women for metabolic syndrome with clusters.
Figure 5
Figure 5
Top features for men and women considering the results of RF and RPART applying balancing techniques.
See this image and copyright information in PMC

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