The close association of obesity with an increased risk of metabolic diseases, such as insulin resistance, type 2 diabetes, and nonalcoholic fatty liver disease, is now well established. In this review, we aim first to describe the inflammatory process activated in response to overnutrition, especially in the liver and the adipose tissue. We then discuss the systemic effects of low-grade inflammation on the onset of insulin resistance. Particular attention is given to a series of very recent reports that identify not only processes but also molecules (lipids and metabolites) that interfere with the normal insulin signaling. Finally, special notes concerning the roles of peroxisome proliferatoractivated receptors in the various processes will be made.

Chronic inflammation that affects primarily metabolic organs, such as white adipose tissue (WAT), is considered as a major cause of human obesity-associated co-morbidities. However, the molecular mechanisms initiating this inflammation in WAT are poorly understood. By combining transcriptomics, ChIP-seq and modeling approaches, we studied the global early and late responses to a high-fat diet (HFD) in visceral (vWAT) and subcutaneous (scWAT) AT, the first being more prone to obesity-induced inflammation. HFD rapidly triggers proliferation of adipocyte precursors within vWAT. However, concomitant antiadipogenic signals limit vWAT hyperplastic expansion by interfering with the differentiation of proliferating adipocyte precursors. Conversely, in scWAT, residing beige adipocytes lose their oxidizing properties and allow storage of excessive fatty acids. This phase is followed by tissue hyperplastic growth and increased angiogenic signals, which further enable scWAT expansion without generating inflammation. Our data indicate that scWAT and vWAT differential ability to modulate adipocyte number and differentiation in response to obesogenic stimuli has a crucial impact on the different susceptibility to obesity-related inflammation of these adipose tissue depots.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.

Inguinal subcutaneous white adipose tissue (iWAT) is essential for conferring the beneficial effects of exercise training on metabolic health. Training alters adipocyte function, yet how the structural components of iWAT respond to training is not known. Using multi-omics approaches, we show that training in male mice results in iWAT remodeling, decreasing extracellular matrix (ECM) deposition, and increasing vascularization and innervation. We find adipose stem cells (ASC) to be the main ECM contributors and show an exercise-induced shift from hypertrophic to insulin sensitive adipocyte subpopulations. We demonstrate a critical role of the PRDM16 transcriptional complex in training-induced tissue remodeling, and propose a link between PRDM16 and neuronal growth regulator1 (NEGR1) in regulating neuritogenesis in trained iWAT. Combined, the major cellular and structural adaptations to iWAT induced by exercise training give rise to a remarkably flexible and multitasking tissue with a key role in modulating metabolic health.