Association of FTO, ABCA1, ADRB3, and PPARG variants with obesity, type 2 diabetes, and metabolic syndrome in a Northwest Mexican adult population

Velázquez-Román, Jorge; Angulo-Zamudio, Uriel A.; León-Sicairos, Nidia; Medina-Serrano, Julio; DeLira-Bustillos, Nora; Villamil‐Ramírez, Hugo; Canizales‐Quinteros, Samuel; Macías-Kauffer, Luis; Campos‐Romero, Abraham; Canizalez-Román, Adrian

doi:10.1016/j.jdiacomp.2021.108025

Cited by 15 publications

(10 citation statements)

References 36 publications

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“…The reason for this is that the hyperuricemic state causes oxidative stress, inflammatory response 17 thereby inducing insulin resistance 18 , which may be the key to promoting the development of MetS. The development of obesity is closely related to inflammation and adipokines, where the inflammatory response is involved in the pathogenesis of the MetS, and adipose tissue, as an endocrine organ, disrupts the balance of pro- and anti-inflammatory factors secreted by adipokines in the inflammatory state, interfering with insulin signaling pathways and leading to insulin resistance 19 ; on the other hand, it may lie in the genes related to lipid metabolism, obesity and insulin resistance and MetS and single nucleotide polymorphisms 20 , thus contributing to the occurrence and progression of the MetS. In addition to blood glucose, studies suggest that HbA1c levels can be an option for MetS screening 21 , due to the ability of HbA1c levels to effectively identify individuals at risk for MetS in people with normal fasting glucose 22 .…”

Section: Discussionmentioning

confidence: 99%

Causal association study of the dynamic development of the metabolic syndrome based on longitudinal data

Razbek,

Bao,

Zhang

et al. 2024

Sci Rep

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The dynamic progression of metabolic syndrome (MetS) includes developmental deterioration and reverse recovery; however, the key factors in this bidirectional progression have not been identified. Our study aimed to use the data obtained from the China Health and Retirement Longitudinal Study (CHARLS) and construct a Bayesian network to explore the causal relationship between influential factor and the development and recovery of MetS. Followed up at 4 years, forward progression of MetS occurred in 1543 and reverse recovery of MetS occurred in 1319 of 5581 subjects. Bayesian Networks showed that hyperuricemia and body mass index (BMI) levels directly influenced progression of MetS, and gender, exercise and age play an indirect role through hyperuricemia and BMI levels; high hemoglobin A1c (HbA1c) and BMI levels directly influenced recovery of MetS, and gender and exercise play an indirect role through BMI levels. Bayesian Network inference found that the rate of progression of MetS in subjects with hyperuricemia increases from 36 to 60%, the rate of progression of MetS in subjects with overweight or obese increases from 36 to 41% and the rate of reverse recovery rate of MetS in subjects with high HbA1c decreased from 33 to 20%. Therefore, attention to individuals at high risk of hyperuricemia, high HbA1c levels, and overweight/obesity should be enhanced, with early detection and following healthy behavioral interventions to prevent, control and delay the progression of MetS and its components.

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Section: Discussionmentioning

confidence: 99%

Causal association study of the dynamic development of the metabolic syndrome based on longitudinal data

Razbek,

Bao,

Zhang

et al. 2024

Sci Rep

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“…Studies have found that CEACAM1 [237], STAT1 [238], ARG1 [239], TLR4 [240], LRRK2 [241], ABCA1 [242], PTGS2 [243], CYP2D6 [244], JAK2 [245], TLR2 [246], DUSP6 [247], CYP1B1 [248], CCR1 [249], HDAC9 [250], LATS2 [251], IL1RN [252], GCH1 [253], PELI1 [254], EGR1 [255], HIPK3 [256], CCR2 [257], GCLC (glutamate-cysteine ligase catalytic subunit) [258], KLF3 [259], VEGFA (vascular endothelial growth factor A) [260], ITGB1 [261], RASA1 [262], PTPN12 [263], SNRK (SNF related kinase) [264], PRKAR1A [265], LDLR (low density lipoprotein receptor) [266], SIRT1 [267], NOD2 [268], VCAN (versican) [269], TET2 [270], PFKFB2 [271], ZBTB20 [272], MYBL2 [273], PF4 [274], VEGFB (vascular endothelial growth factor B) [275], CCR7 [276], PRDX2 [277], HSPB1 [278], ZNF791 [279], IGFBP4 [280], ESF1 [281], SNHG8 [282], LGALS3 [283] and LGMN (legumain) [284] are altered expression in myocardial infarction. Altered expression of CEACAM1 [176], ACSL1 [285], STAT1 [286], TLR4 [287], ABCA1 [288], TLR5 [183], F2RL1 [289], CYP2D6 [290], PDK4 [291], RNF213 [186], JAK2 [292], TLR8 [189], NOTCH2 [293], CENPJ (centromere protein J) [294], FNIP1 [295], TLR2 [296], KIDINS220 [297], DUSP6 [298], CYP1B1 [299], S1PR3 [300], NCOA2 [301], HDAC9 [302], PELI1 [303], EGR1 [304], HIF1A [305], CCR2 [306], IR...…”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA hub gene regulatory network and TF hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research.

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“… 28 The mechanism may be that genes related to lipid metabolism, obesity and insulin resistance are related to MetS and single nucleotide polymorphism. 29 Focusing on lifestyle, various toxic substances from cigarette stimulate the release of adrenaline, which increases blood pressure and heart rate, 30 and cause abnormalities in lipoprotein metabolism, endothelial cell dysfunction and insulin resistance, which increase the risk of MetS. 26 , 31 The less physical exercise, the higher the risk of overweight, obesity and MetS.…”

Section: Discussionmentioning

confidence: 99%

Study on Dynamic Progression and Risk Assessment of Metabolic Syndrome Based on Multi-State Markov Model

Razbek¹,

Zhang²,

Xia³

et al. 2022

DMSO

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Aim Metabolic syndrome (MetS) coexists with the occurrence and even death of cardiovascular disease and diabetes mellitus. It is essential to study the factors in the dynamic progression of MetS in the interest of prevention and control. Purpose The aim of this study was to analyze the dynamic progression of Mets and explore the potential factors influencing the progression or reversal of MetS. Patients and Methods This study involved 5581 individuals from two waves of the China Health and Retirement Longitudinal Study: 2011 and 2015. A multistate Markov model containing 4 states (free of metabolic disorder (FMD), mild metabolic disorder (MMD), severe metabolic disorder (SMD) and MetS) was adopted to study the dynamic progression of MetS and its influencing factors. Results After follow-up, a total of 2862 cases (50.28% of the total number) had disease state transition. The intensity of transition from MetS to SMD is the same as that from SMD to MMD, and is greater than that from MMD to Mets (0.06 vs 0.05). For the MetS state, a mean of 1/0.08=12.5 years was spent in the MetS state before recovery. The exercise, smoke, drink, BMI level, hyperuricemia had statistically significant effects on progression of MetS status ( P <0.05). The obesity or overweight, little exercise, smoke, drink and hyperuricemia increased the risk of forward progression of MetS disease status. There were significant nonmodifiable (age, gender) and modifiable factors (exercise, drink, BMI level, or high HbA1c) associated with reversion of MetS state. Conclusion The likelihood of progression from MMD to MetS is less likely than that of reversion from MetS to SMD and SMD to MMD. Old females were more resistant to recover from worse states than males. Prevention and intervention measures should be adopted early when MMD or SMD onset occurs.

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Association of FTO, ABCA1, ADRB3, and PPARG variants with obesity, type 2 diabetes, and metabolic syndrome in a Northwest Mexican adult population

Cited by 15 publications

References 36 publications

Causal association study of the dynamic development of the metabolic syndrome based on longitudinal data

Causal association study of the dynamic development of the metabolic syndrome based on longitudinal data

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Study on Dynamic Progression and Risk Assessment of Metabolic Syndrome Based on Multi-State Markov Model

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