Dataset Information

Endogenous estradiol and inflammation biomarkers: potential interacting mechanisms of obesity-related disease.

ABSTRACT: PURPOSE:Disentangling the effects of endogenous estrogens and inflammation on obesity-related diseases requires a clearer understanding of how the two biological mechanisms relate to each other. METHODS:We studied 155 healthy postmenopausal women not taking menopausal hormone therapy enrolled in the Prostate Lung Colorectal and Ovarian (PLCO) screening cancer trial. From a baseline blood draw, we measured endogenous estradiol and 69 inflammation biomarkers: cytokines, chemokines, adipokines, angiogenic factors, growth factors, acute phase proteins, and soluble receptors. We evaluated the estradiol-inflammation relationship by assessing associations across different models (linear, ordinal logistic, and binary logistic) using a variety of estradiol classifications. We additionally investigated the estradiol-inflammation relationship stratified by baseline obesity status (BMI < 30 stratum and BMI > 30 stratum). RESULTS:Associations of estradiol with 7 inflammation biomarkers met p < 0.05 statistical significance in linear and ordinal models: C-reactive protein (CRP), adiponectin, chemokine (C-X-C motif) ligand-6, thymus activation-regulated chemokine, eosinophil chemotactic protein, plasminogen activator inhibitor-1, and serum amyloid A. The positive association between estradiol and CRP was robust to model changes. Each standard deviation increase in endogenous estradiol doubled a woman's odds of having CRP levels higher than the study median (odds ratio 2.29; 95% confidence interval 1.28, 4.09). Estradiol was consistently inversely associated with adiponectin. Other estradiol-inflammation biomarker associations were not robust to model changes. CONCLUSIONS:Endogenous estradiol appears to be associated with CRP and adiponectin; the evidence is limited for other inflammation biomarkers.

SUBMITTER: Eldridge RC

PROVIDER: S-EPMC7472689 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Obesity can increase the risk of complex metabolic diseases, including insulin resistance. Moreover, obesity can be caused by environmental and genetic factors. However, the epigenetic mechanisms of obesity are not well defined. Therefore, the identification of novel epigenetic biomarkers of obesity allows for a more complete understanding of the disease and its underlying insulin resistance. The aim of our study was to identify DNA methylation changes in whole-blood that were strongly associated with obesity and insulin resistance. Whole-blood was obtained from lean (n = 10; BMI = 23.6 ± 0.7 kg/m2) and obese (n = 10; BMI = 34.4 ± 1.3 kg/m2) participants in combination with euglycemic hyperinsulinemic clamps to assess insulin sensitivity. We performed reduced representation bisulfite sequencing on genomic DNA isolated from the blood. We identified 49 differentially methylated cytosines (DMCs; q < 0.05) that were altered in obese compared with lean participants. We identified 2 sites (Chr.21:46,957,981 and Chr.21:46,957,915) in the 5' untranslated region of solute carrier family 19 member 1 (SLC19A1) with decreased methylation in obese participants (lean 0.73 ± 0.11 vs. obese 0.09 ± 0.05; lean 0.68 ± 0.10 vs. obese 0.09 ± 0.05, respectively). These 2 DMCs identified by obesity were also significantly predicted by insulin sensitivity (r = 0.68, P = 0.003; r = 0.66; P = 0.004). In addition, we performed a differentially methylated region (DMR) analysis and demonstrated a decrease in methylation of Chr.21:46,957,915-46,958,001 in SLC19A1 of -34.9% (70.4% lean vs. 35.5% obese). The decrease in whole-blood SLC19A1 methylation in our obese participants was similar to the change observed in skeletal muscle (Chr.21:46,957,981, lean 0.70 ± 0.09 vs. obese 0.31 ± 0.11 and Chr.21:46,957,915, lean 0.72 ± 0.11 vs. obese 0.31 ± 0.13). Pyrosequencing analysis further demonstrated a decrease in methylation at Chr.21:46,957,915 in both whole-blood (lean 0.71 ± 0.10 vs. obese 0.18 ± 0.06) and skeletal muscle (lean 0.71 ± 0.10 vs. obese 0.30 ± 0.11). Our findings demonstrate a new potential epigenetic biomarker, SLC19A1, for obesity and its underlying insulin resistance.

Project description:BackgroundCoronary artery disease (CAD) is a complex illness with unknown pathophysiology. Peripheral biomarkers are a non-invasive method required to track the onset and progression of CAD and have unbeatable benefits in terms of early identification, prognostic assessment, and categorization of the diagnosis. This study aimed to identify and validate the diagnostic and therapeutic potential of differentially expressed immune-related genes (DE-IRGs) in CAD, which will aid in improving our knowledge on the etiology of CAD and in forming genetic predictions.MethodsFirst, we searched coronary heart disease in the Gene Expression Omnibus (GEO) database and identified GSE20680 (CAD = 87, Normal = 52) as the trial set and GSE20681 (CAD = 99, Normal = 99) as the validation set. Functional enrichment analysis using protein-protein interactions (PPIs), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out on the identified differentially expressed genes. Optimal feature genes (OFGs) were generated using the support vector machine recursive feature elimination algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm. Furthermore, immune infiltration in CAD patients and healthy controls was compared using CIBERSORT, and the relationship between immune cells and OFGs was examined. In addition, we constructed potential targeted drugs for this model through the Drug-Gene Interaction database (DGIdb) database. Finally, we verify the expression of S100A8-dominated OFGs in the GSE20681 dataset to confirm the universality of our study.ResultsWe identified the ten best OFGs for CAD from the DE-IRGs. Functional enrichment analysis showed that these marker genes are crucial for receptor-ligand activity, signaling receptor activator activity, and positive control of the response to stimuli from the outside world. Additionally, CIBERSORT revealed that S100A8 could be connected to alterations in the immune microenvironment in CAD patients. Furthermore, with the help of DGIdb and Cytoscape, a total of 64 medicines that target five marker genes were subsequently discovered. Finally, we verified the expression of the OFGs genes in the GSE20681 dataset between CAD patients and normal patients and found that there was also a significant difference in the expression of S100A8.ConclusionWe created a 10-gene immune-related prognostic model for CAD and confirmed its validity. The model can identify potential biomarkers for CAD prediction and more accurately gauge the progression of the disease.

Dataset Information

Endogenous estradiol and inflammation biomarkers: potential interacting mechanisms of obesity-related disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets