Changes in the Expression of Insulin Pathway, Neutrophil Elastase and Alpha 1 Antitrypsin Genes from Leukocytes of Young Individuals with Insulin Resistance

Cruz-Pineda, Walter David; Garibay‐Cerdenares, Olga Lilia; Rodríguez-Ruiz, Hugo Alberto; Matia‐García, Inés; Marino-Ortega, Linda A; Espinoza-Rojo, Mónica; Reyes-Castillo, Zyanya; Castro-Alarcón, Natividad; Castañeda-Saucedo, Eduardo; Illades‐Aguiar, Berenice; Parra‐Rojas, Isela

doi:10.2147/dmso.s362881

Cited by 3 publications

(3 citation statements)

References 69 publications

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“…In addition, there are also some biochemical tests and genetic tests that have been shown to correlate with IR. 29 , 30 However, since these tests are not part of the routine physical examinations for children, they are likely to bring additional costs to families, and too many features would make the model more complex, so we did not choose them in the end so as to keep the model simpler and easier to generalize.…”

Section: Methodsmentioning

confidence: 99%

Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning

Zhang¹,

Wan²

2022

DMSO

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Background: Due to the increasing insulin resistance (IR) in childhood, rates of diabetes and cardiovascular disease may rise in the future and seriously threaten the healthy development of children. Finding an easy way to predict IR in children can help pediatricians to identify these children in time and intervene appropriately, which is particularly important for practitioners in primary health care. Patients and Methods: Seventeen features from 503 children 6-12 years old were collected. We defined IR by HOMA-IR greater than 3.0, thus classifying children with IR and those without IR. Data were preprocessed by multivariate imputation and oversampling to resolve missing values and data imbalances; then, recursive feature elimination was applied to further select features of interest, and 5 machine learning methods -namely, logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting with categorical features support (CatBoost)-were used for model training. We tested the trained models on an external test set containing information from 133 children, from which performance metrics were extracted and the optimal model was selected. Results: After feature selection, the numbers of chosen features for the LR, SVM, RF, XGBoost, and CatBoost models were 6, 9, 10, 14, and 6, respectively. Among them, glucose, waist circumference, and age were chosen as predictors by most of the models. Finally, all 5 models achieved good performance on the external test set. Both XGBoost and CatBoost had the same AUC (0.85), which was highest among those of all models. Their accuracy, sensitivity, precision, and F1 scores were also close, but the specificity of XGBoost reached 0.79, which was significantly higher than that of CatBoost, so XGBoost was chosen as the optimal model. Conclusion:The model developed herein has a good predictive ability for IR in children 6-12 years old and can be clinically applied to help pediatricians identify children with IR in a simple and inexpensive way.

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Section: Methodsmentioning

confidence: 99%

Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning

Zhang¹,

Wan²

2022

DMSO

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“…Like other epidemiological studies, EWAS designs include cross-sectional, case-control, intervention, family-based, and longitudinal designs (185). The logic of EWAS is comparable to GWAS, as rather than focusing on specific genes, all genome is screened for DNA methylation; thus, EWAS is hypothesis-free by nature (186). Hence, EWAS designs could provide new opportunities to identify biomarkers and discover pathways and links between environmental exposures and different health outcomes (185).…”

Section: Important Considerations For Ewas As An Epidemiologically Ba...mentioning

confidence: 99%

“…In the context of the studies in this thesis, blood tissue is a good choice as it is relevant for insulin resistance, serum folate, and physical activity. Insulin resistance affects glucose uptake and leukocyte metabolism, which is associated with the development of the metabolic syndrome and cardiovascular diseases (186). Serum folate gathered from blood is a good representation of recent folate intake (179), while physical activity increases the mitochondrial oxidative capacity of peripheral blood leukocytes (188).…”

Section: Important Considerations For Ewas As An Epidemiologically Ba...mentioning

confidence: 99%

Epigenome-Wide Association Study of Serum Folate in Maternal Peripheral Blood Leukocytes

et al. 2023

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Aim: To perform an epigenome-wide association study (EWAS) of serum folate in maternal blood. Methods: Cross-ancestry (Europeans = 302, South Asians = 161) and ancestry-specific EWAS in the EPIPREG cohort were performed, followed by methyl quantitative trait loci analysis and association with cardiometabolic phenotypes. Replication was attempted using maternal folate intake and blood methylation data from the MoBa study and verified if the findings were significant in a previous EWAS of maternal serum folate in cord blood. Results & conclusion: cg19888088 (cross-ancestry) in EBF3, cg01952260 (Europeans) and cg07077240 (South Asians) in HERC3 were associated with serum folate. cg19888088 and cg01952260 were associated with diastolic blood pressure. cg07077240 was associated with variants in CASC15. The findings were not replicated and were not significant in cord blood.

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Immunological and homeostatic pathways of alpha -1 antitrypsin: a new therapeutic potential

Mazzuca,

Vitiello,

Travaglini

et al. 2024

Front. Immunol.

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α -1 antitrypsin (A1AT) is a 52 kDa acute-phase glycoprotein belonging to the serine protease inhibitor superfamily (SERPIN). It is primarily synthesized by hepatocytes and to a lesser extent by monocytes, macrophages, intestinal epithelial cells, and bronchial epithelial cells. A1AT is encoded by SERPINA1 locus, also known as PI locus, highly polymorphic with at least 100 allelic variants described and responsible for different A1AT serum levels and function. A1AT inhibits a variety of serine proteinases, but its main target is represented by Neutrophil Elastase (NE). However, recent attention has been directed towards its immune-regulatory and homeostatic activities. A1AT exerts immune-regulatory effects on different cell types involved in innate and adaptive immunity. Additionally, it plays a role in metal and lipid metabolism, contributing to homeostasis. An adequate comprehension of these mechanisms could support the use of A1AT augmentation therapy in many disorders characterized by a chronic immune response. The aim of this review is to provide an up-to-date understanding of the molecular mechanisms and regulatory pathways responsible for immune-regulatory and homeostatic activities of A1AT. This knowledge aims to support the use of A1AT in therapeutic applications. Furthermore, the review summarizes the current state of knowledge regarding the application of A1AT in clinical and laboratory settings human and animal models.

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Changes in the Expression of Insulin Pathway, Neutrophil Elastase and Alpha 1 Antitrypsin Genes from Leukocytes of Young Individuals with Insulin Resistance

Cited by 3 publications

References 69 publications

Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning

Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning

Epigenome-Wide Association Study of Serum Folate in Maternal Peripheral Blood Leukocytes

Immunological and homeostatic pathways of alpha -1 antitrypsin: a new therapeutic potential

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