The chemometric models in metabolomics

The metabolomic analysis provides a powerful approach for delving into intricate biological metabolism. Given metabolomics data's complexity and high-dimensional nature, it necessitates applying advanced analytical techniques for meaningful interpretation. This chapter centers on the pivotal role of chemometric tools in metabolomic analysis. These tools encompass a wide range of statistical and computational methods that empower us to extract valuable insights from extensive and intricate metabolomics datasets. They play a itical role in tasks such as data preprocessing, noise reduction, feature selection, and multivariate analysis, thereby enhancing our ability to unveil biologically relevant information. Moreover, these tools facilitate the integration of data from diverse analytical platforms, allowing researchers to identify and validate metabolites that are indicative of specific biological conditions. Additionally, chemometric methods aid in elucidating metabolic pathways and exploring interactions among metabolites, shedding light on the underlying biology. Given the intricacies involved, it is ucial to utilize specific analytical tools, including but not limited to Principal Component Analysis (PCA), Partial Least Squares Projection to Latent Structures (PLS), permutation tests, Random Forest, and K-Nearest Neighbors (KNN). Hence, this chapter is dedicated to navigating the multifaceted landscape of multivariate metabolomics analysis, highlighting both its advantages and limitations.<br/

Multi-Omics Reveal Interplay between Circadian Dysfunction and Type2 Diabetes

Type 2 diabetes is one of the leading threats to human health in the 21st century. It is a metabolic disorder characterized by a dysregulated glucose metabolism resulting from impaired insulin secretion or insulin resistance. More recently, accumulated epidemiological and animal model studies have confirmed that circadian dysfunction caused by shift work, late meal timing, and sleep loss leads to type 2 diabetes. Circadian rhythms, 24-h endogenous biological oscillations, are a fundamental feature of nearly all organisms and control many physiological and cellular functions. In mammals, light synchronizes brain clocks and feeding is a main stimulus that synchronizes the peripheral clocks in metabolic tissues, such as liver, pancreas, muscles, and adipose tissues. Circadian arrhythmia causes the loss of synchrony of the clocks of these metabolic tissues and leads to an impaired pancreas β-cell metabolism coupled with altered insulin secretion. In addition to these, gut microbes and circadian rhythms are intertwined via metabolic regulation. Omics approaches play a significant role in unraveling how a disrupted circadian metabolism causes type 2 diabetes. In the present review, we emphasize the discoveries of several genes, proteins, and metabolites that contribute to the emergence of type 2 diabetes mellitus (T2D). The implications of these discoveries for comprehending the circadian clock network in T2D may lead to new therapeutic solutions