Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study.

Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical stage after which obesity-associated insulin resistance is more difficult to revert. Timely prediction of insulin resistance in pediatric obesity is therefore vital for mitigating the risk of its associated comorbidities.

Shared gene expression signatures between visceral adipose and skeletal muscle tissues are associated with cardiometabolic traits in children with obesity.

Obesity in children is related to the development of cardiometabolic complications later in life, where molecular changes of visceral adipose tissue (VAT) and skeletal muscle tissue (SMT) have been proven to be fundamental. The aim of this study is to unveil the gene expression architecture of both tissues in a cohort of Spanish boys with obesity, using a clustering method known as weighted gene co-expression network analysis. For this purpose, we have followed a multi-objective analytic pipeline consisting of three main approaches; identification of gene co-expression clusters associated with childhood obesity, individually in VAT and SMT (intra-tissue, approach I); identification of gene co-expression clusters associated with obesity-metabolic alterations, individually in VAT and SMT (intra-tissue, approach II); and identification of gene co-expression clusters associated with obesity-metabolic alterations simultaneously in VAT and SMT (inter-tissue, approach III).

Omics Data Preprocessing for Machine Learning: A Case Study in Childhood Obesity.

The use of machine learning techniques for the construction of predictive models of disease outcomes (based on omics and other types of molecular data) has gained enormous relevance in the last few years in the biomedical field. Nonetheless, the virtuosity of omics studies and machine learning tools are subject to the proper application of algorithms as well as the appropriate pre-processing and management of input omics and molecular data. Currently, many of the available approaches that use machine learning on omics data for predictive purposes make mistakes in several of the following key steps: experimental design, feature selection, data pre-processing, and algorithm selection.

Omics Approaches in Adipose Tissue and Skeletal Muscle Addressing the Role of Extracellular Matrix in Obesity and Metabolic Dysfunction.

Extracellular matrix (ECM) remodeling plays important roles in both white adipose tissue (WAT) and the skeletal muscle (SM) metabolism. Excessive adipocyte hypertrophy causes fibrosis, inflammation, and metabolic dysfunction in adipose tissue, as well as impaired adipogenesis. Similarly, disturbed ECM remodeling in SM has metabolic consequences such as decreased insulin sensitivity.