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. 2022 Jan-Dec:13:21501319221096917.
doi: 10.1177/21501319221096917.

Network Analysis of Depressive Symptomatology in Underweight and Obese Adults

Cristian Ramos-Vera  1 , Antonio Serpa Barrientos  2 , José Vallejos-Saldarriaga  1 , Jacksaint Saintila  3
Affiliations

Network Analysis of Depressive Symptomatology in Underweight and Obese Adults

Cristian Ramos-Vera et al. J Prim Care Community Health. 2022 Jan-Dec.

Abstract

Background: Depressive symptoms can affect people's quality of life and social environment. In addition, in severe situations, they can lead to suicidal behaviors.

Objective: This study aimed to analyze the differences in depressive symptoms in underweight and obese Peruvian adults.

Methods: A cross-sectional study was carried out based on secondary data obtained from the Instituto Nacional de Estadística e Informática (INEI), Lima, Peru. A sample of 10 053 participants was considered, of which 55.96% were women. Two Gaussian plot models were estimated and the levels of depressive symptomatology were compared between the 2 groups (adults with underweight and obese).

Results: A total of 1510 (15.02%) were underweight adults and 8543 (84.98%) were obese adults. There were differences in the reporting of depressive symptoms in the underweight group; the most central items were "Depressed mood" (PH2), "Tiredness/low energy" (PH4), and "Psychomotor difficulties" (PH8).

Conclusion: This study provides new evidence on the dynamic relationship between depressive symptoms according to the body mass index categories (underweight and obese) assessed.

Keywords: Peru; body mass index; depression; obesity; underweight.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Network analysis for underweight.
Figure 2.
Figure 2.
Network analysis for obesity.
Figure 3.
Figure 3.
Accuracy of nodes in the network for the lowest BMI.
Figure 4.
Figure 4.
Accuracy of nodes in the network for higher BMI.
Figure 5.
Figure 5.
Stability of the strength centrality index in the underweight network.
Figure 6.
Figure 6.
Stability of the strength centrality index in the obesity network.
See this image and copyright information in PMC

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