Deciphering the Relationship between Obesity and Various Diseases from a Network Perspective

Abstract

The number of obesity cases is rapidly increasing in developed and developing countries, thereby causing significant health problems worldwide. The pathologic factors of obesity at the molecular level are not fully characterized, although the imbalance between energy intake and consumption is widely recognized as the main reason for fat accumulation. Previous studies reported that obesity can be caused by the dysfunction of genes associated with other diseases, such as myocardial infarction, hence providing new insights into dissecting the pathogenesis of obesity by investigating its associations with other diseases. In this study, we investigated the relationship between obesity and diseases from Online Mendelian Inheritance in Man (OMIM) databases on the protein–protein interaction (PPI) network. The obesity genes and genes of one OMIM disease were mapped onto the network, and the interaction scores between the two gene sets were investigated on the basis of the PPI of individual gene pairs, thereby inferring the relationship between obesity and this disease. Results suggested that diseases related to nutrition and endocrine are the top two diseases that are closely associated with obesity. This finding is consistent with our general knowledge and indicates the reliability of our obtained results. Moreover, we inferred that diseases related to psychiatric factors and bone may also be highly related to obesity because the two diseases followed the diseases related to nutrition and endocrine according to our results. Numerous obesity–disease associations were identified in the literature to confirm the relationships between obesity and the aforementioned four diseases. These new results may help understand the underlying molecular mechanisms of obesity–disease co-occurrence and provide useful insights for disease prevention and intervention.

The gene regulatory network in different brain regions of neuropathic pain mouse models

“…Therefore, we mapped these three gene lists onto the comprehensive functional association network of STRING 34 , a widely used network for bioinformatics studies [35][36][37][38] . Only the high confidence interactions of STRING were included, in other words, the confidence score of the interaction must be greater than 0.900.…”

Sedentary behavior, physical activity, sleep duration and obesity risk: Mendelian randomization study

“…Furthermore, SB's potential impact on thyroid hormone synthesis, energy metabolism, and body temperature regulation is notable. The observed gene enrichment related to insulin secretion in visceral fat tissues implies that SB might indirectly heighten the risk of metabolic diseases like diabetes, a significance also emphasized in the study by Chen et al [56]. This comprehensive analysis enhances our understanding of the complex genetic underpinnings, biological mechanisms, and their association with obesity.…”

Integrative network analysis of differentially methylated regions to study the impact of gestational weight gain on maternal metabolism and fetal-neonatal growth

“…Enrichment analysis showed that these modules are related to diseases that involve cell adhesion, to the innate immune response, and to cardiovascular and hypertensive diseases, providing biological insights into weight gain during pregnancy. PPI networks have been studied in chronic diseases such as obesity ( Chen et al, 2017 ; Chen et al ., 2021 ) and in gestational diabetes mellitus ( Zhu et al, 2020 ) but have not yet been described in maternal EGWG.…”