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Keywords = vitamin B3/nicotinamide riboside treatment

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21 pages, 2984 KiB  
Review
Nicotinamide Riboside for the Prevention and Treatment of Doxorubicin Cardiomyopathy. Opportunities and Prospects
by Ekaterina Podyacheva and Yana Toropova
Nutrients 2021, 13(10), 3435; https://doi.org/10.3390/nu13103435 - 28 Sep 2021
Cited by 12 | Viewed by 4115
Abstract
Despite the progress in the development of new anticancer strategies, cancer is rapidly spreading around the world and remains one of the most common diseases. For more than 40 years, doxorubicin has been widely used in the treatment of solid and hematological tumors. [...] Read more.
Despite the progress in the development of new anticancer strategies, cancer is rapidly spreading around the world and remains one of the most common diseases. For more than 40 years, doxorubicin has been widely used in the treatment of solid and hematological tumors. At the same time, the problem of its cardiotoxicity remains unresolved, despite the high efficiency of this drug. Symptomatic therapy is used as a treatment for side-effects of doxorubicin or pathological conditions that have already appeared in their background. To date, there are no treatment methods for doxorubicin cardiomyopathy as such. A drug such as nicotinamide riboside can play an important role in solving this problem. Nicotinamide riboside is a pyridine nucleoside similar to vitamin B3 that acts as a precursor to NAD+. There is no published research on nicotinamide riboside effects on cardiomyopathy, despite the abundance of works devoted to the mechanisms of its effects in various pathologies. The review analyzes information about the effects of nicotinamide riboside on various experimental models of pathologies, its role in the synthesis of NAD+, and also considers the possibility and prospects of its use for the prevention of doxorubicin cardiomyopathy. Full article
(This article belongs to the Section Nutritional Epidemiology)
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10 pages, 1509 KiB  
Article
Variable Selection in Untargeted Metabolomics and the Danger of Sparsity
by Gerjen H. Tinnevelt, Udo F.H. Engelke, Ron A. Wevers, Stefanie Veenhuis, Michel A. Willemsen, Karlien L.M. Coene, Purva Kulkarni and Jeroen J. Jansen
Metabolites 2020, 10(11), 470; https://doi.org/10.3390/metabo10110470 - 17 Nov 2020
Cited by 5 | Viewed by 3522
Abstract
The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set [...] Read more.
The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminate between groups and (2) methods that find all variables important for discrimination. Such important variables can be summarized into metabolic pathways using pathway analysis tools like Mummichog. As a test case, we studied the metabolic effects of treatment with nicotinamide riboside, a form of vitamin B3, in a cohort of patients with ataxia–telangiectasia. Vitamin B3 is an important co-factor for many enzymatic reactions in the human body. Thus, the variable selection method was expected to find vitamin B3 metabolites and also other secondary metabolic changes during treatment. However, sparse methods did not select any vitamin B3 metabolites despite the fact that these metabolites showed a large difference when comparing intensity before and during treatment. Univariate analysis or significance multivariate correlation (sMC) in combination with pathway analysis using Mummichog were able to select vitamin B3 metabolites. Moreover, sMC analysis found additional metabolites. Therefore, in our comparative study, sMC displayed the best performance for selection of relevant variables. Full article
(This article belongs to the Special Issue Development and Application of Statistical Methods for Analyzing Metabolomics Data)
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