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Objective: Polyamines are naturally occurring cationic molecules present in all living cells. Dysregulation of circulating polyamines has been reported in several conditions, but little is known about the levels of serum polyamines in chronic metabolic disorders such as type 2 diabetes (T2D). Therefore, the aim of this study was to evaluate the polyamine-related metabolome in a cohort of metabolic syndrome individuals with and without T2D. Design and methods: This was a nested case–control study within the PREDIMED-Plus trial that included 44 patients with T2D and 70 patients without T2D. We measured serum levels of arginine, ornithine, polyamines, and acetyl polyamines with an ultra-high performance liquid chromatography tandem mass spectrometry platform. Results: Our results showed that serum putrescine, directly generated from ornithine by the catalytic action of the biosynthetic enzyme ornithine decarboxylase, was significantly elevated in patients with T2D compared to those without T2D, and that it significantly correlated with the levels of glycosylated hemoglobin (HbA1c). Correlation analysis revealed a significantly positive association between fasting insulin levels and spermine. Multiple logistic regression analysis (adjusted for age, gender and body weight index) revealed that serum putrescine and spermine levels were associated with a higher risk of T2D. Conclusions: Our study suggests that polyamine metabolism is dysregulated in T2D, and that serum levels of putrescine and spermine are associated with glycemic control and circulating insulin levels, respectively.

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Polyamines are involved in the regulation of many cellular functions and are promising biomarkers of numerous physiological conditions. Since the concentrations of these compounds in biological fluids are low, sample extraction is one of the most critical steps of their analysis. Here, we developed a comprehensive, sensitive, robust, and high-throughput LC-MS/MS stable-isotope dilution method for the simultaneous determination of 19 metabolites related to polyamine metabolism, including polyamines, acetylated and diacetylated polyamines, precursors, and catabolites from liquid biopsies. The sample extraction was optimized to remove interfering compounds and to reduce matrix effects, thus being useful for large clinical studies. The method consists of two-step liquid-liquid extraction with a Folch extraction and ethyl acetate partitioning combined with dansyl chloride derivatization. The developed method was applied to a small gender-related trial concerning human serum and urine samples from 40 obese subjects. Sex differences were found for cadaverine, putrescine, 1,3-diaminopropane, γ-aminobutyric acid, N8-acetylspermidine, and N-acetylcadaverine in urine; N1-acetylspermine in serum; and spermine in both serum and urine. The results demonstrate that the developed method can be used to analyze biological samples for the study of polyamine metabolism and its association with human diseases.

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.