A Preliminary Study Showing the Impact of Genetic and Dietary Factors on GC–MS-Based Plasma Metabolome of Patients with and without PROX1-Genetic Predisposition to T2DM up to 5 Years Prior to Prediabetes Appearance

Abstract

Risk factors for type 2 diabetes mellitus (T2DM) consist of a combination of an unhealthy, imbalanced diet and genetic factors that may interact with each other. Single nucleotide polymorphism (SNP) in the prospero homeobox 1 (PROX1) gene is a strong genetic susceptibility factor for this metabolic disorder and impaired β-cell function. As the role of this gene in T2DM development remains unclear, novel approaches are needed to advance the understanding of the mechanisms of T2DM development. Therefore, in this study, for the first time, postprandial changes in plasma metabolites were analysed by GC–MS in nondiabetic men with different PROX1 genotypes up to 5 years prior to prediabetes appearance. Eighteen contestants (12 with high risk (HR) and 6 with low risk (LR) genotype) participated in high-carbohydrate (HC) and normo-carbohydrate (NC) meal-challenge tests. Our study concluded that both meal-challenge tests provoked changes in 15 plasma metabolites (amino acids, carbohydrates, fatty acids and others) in HR, but not LR genotype carriers. Postprandial changes in the levels of some of the detected metabolites may be a source of potential specific early disturbances possibly associated with the future development of T2DM. Thus, accurate determination of these metabolites can be important for the early diagnosis of this metabolic disease.

Gas Chromatography–Mass Spectroscopy-Based Metabolomics Analysis Reveals Potential Biochemical Markers for Diagnosis of Gestational Diabetes Mellitus

“…Extraction of serum metabolites was performed as described previously (Mojsak et al, 2021). The derivatization procedure was carried out in two steps.…”

“…Recent findings have highlighted metabolomics as a prime candidate for evaluating potential markers for GDM (Mao et al, 2017) because of its capacity to detect early deregulations and disruptions in metabolism associated with different diseases (Mojsak et al, 2021). Therefore, it can be used as a potential tool to determine a metabolite or a set of metabolites allowing diagnosis or prediction of GDM (Sakurai et al, 2019).…”

Optimization of a GC-MS method for the profiling of microbiota-dependent metabolites in blood samples: An application to type 2 diabetes and prediabetes

“…The worldwide prevalence of type 2 diabetes mellitus (T2DM) has risen over the past two decades (Sobczak et al, 2019) and currently, this metabolic disease is a serious public health problem (Fikri et al, 2020). There is an increasing evidence that alterations in gut microbiota (GM) (Lin et al, 2017;Moon et al, 2018;Chen et al, 2019), apart from genetic (Mojsak et al, 2021) and life style factors (Ericson et al, 2018), are important for the development of metabolic diseases. Changes in the gut microbiome composition lead to an imbalanced gastrointestinal habitat which promotes abnormal production of metabolites, inflammatory status, glucose metabolism alteration and even insulin resistance (IR) (Tanase et al, 2020).…”

“…Changes in the gut microbiome composition lead to an imbalanced gastrointestinal habitat which promotes abnormal production of metabolites, inflammatory status, glucose metabolism alteration and even insulin resistance (IR) (Tanase et al, 2020). Particularly, various microbiota-dependent metabolites (MDMs) (Han et al, 2021), such as short-chain fatty acids, branched-chain fatty acids, amino acids (AAs), branched-chain amino acids (BCAAs), bile acids, tryptophanderived metabolites, and others (Mojsak et al, 2022;Zhou et al, 2022) have been reported to be closely associated with IR (Menni et al, 2022), prediabetes (Dorcely et al, 2017;Gar et al, 2018) and T2DM (Sobczak et al, 2019;Fikri et al, 2020;Gu et al, 2020;Mojsak et al, 2021).…”

“…In order to extend the coverage of MDMs measured with GC-MS, selection of a suitable solvent for the protein precipitation or extraction (He et al, 2021) and a transformation of analysed metabolites into more volatile forms through the derivatisation process, is required (Moldoveanu and David, 2018). Many solvents like ACN (Mojsak et al, 2021), MeOH (Trygg et al, 2005) or mixtures of solvents such as: ACN:isoProp:H 2 O (v:v:v; 3:3:2) (Fiehn, 2016;Kiseleva et al, 2021); MeOH:H 2 O (v:v; 9:1) (Malm et al, 2016), MeOH:isoProp:H 2 O (v:v:v; 3:3:2) (Kiseleva et al, 2021) or MeOH:EtOH (v:v; 1:1) (Szeremeta et al, 2021) are commonly used in metabolomics studies. A choice of proper solvent(s) is important, in the study of Fiehn (2016) it was confirmed that the use of hydrophilic, lipohilic and medium-polarity solvents demonstrated high analytical precision and comprehensiveness of the extracted metabolome.…”