Dataset Information

Antioxidants in Translational Medicine.

ABSTRACT: It is generally accepted that reactive oxygen species (ROS) scavenging molecules or antioxidants exert health-promoting effects and thus their consumption as food additives and nutraceuticals has been greatly encouraged. Antioxidants may be beneficial in situations of subclinical deficiency and increased demand or acutely upon high-dose infusion. However, to date, there is little clinical evidence for the long-term benefit of most antioxidants. Alarmingly, recent evidence points even to health risks, in particular for supplements of lipophilic antioxidants.The biological impact of ROS depends not only on their quantities but also on their chemical nature, (sub)cellular and tissue location, and the rates of their formation and degradation. Moreover, ROS serve important physiological functions; thus, inappropriate removal of ROS may cause paradoxical reductive stress and thereby induce or promote disease.Any recommendation on antioxidants must be based on solid clinical evidence and patient-relevant outcomes rather than surrogate parameters.Such evidence-based use may include site-directed application, time-limited high dosing, (functional) pharmacological repair of oxidized biomolecules, and triggers of endogenous antioxidant response systems. Ideally, these approaches need guidance by patient stratification through predictive biomarkers and possibly imaging modalities.

SUBMITTER: Schmidt HH

PROVIDER: S-EPMC4657516 | biostudies-other | 2015 Nov

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Antioxidants in Translational Medicine.

Schmidt Harald H H W HH Stocker Roland R Vollbracht Claudia C Paulsen Gøran G Riley Dennis D Daiber Andreas A Cuadrado Antonio A Cuadrado Antonio A

Antioxidants & redox signaling 20150831 14

<h4>Significance</h4>It is generally accepted that reactive oxygen species (ROS) scavenging molecules or antioxidants exert health-promoting effects and thus their consumption as food additives and nutraceuticals has been greatly encouraged. Antioxidants may be beneficial in situations of subclinical deficiency and increased demand or acutely upon high-dose infusion. However, to date, there is little clinical evidence for the long-term benefit of most antioxidants. Alarmingly, recent evidence po ...[more]

PMID: 26154592

Similar Datasets

Project description:BackgroundTranslational medicine (TM) is an emerging domain that aims to facilitate medical or biological advances efficiently from the scientist to the clinician. Central to the TM vision is to narrow the gap between basic science and applied science in terms of time, cost and early diagnosis of the disease state. Biomarker identification is one of the main challenges within TM. The identification of disease biomarkers from -omics data will not only help the stratification of diverse patient cohorts but will also provide early diagnostic information which could improve patient management and potentially prevent adverse outcomes. However, biomarker identification needs to be robust and reproducible. Hence a robust unbiased computational framework that can help clinicians identify those biomarkers is necessary.MethodsWe developed a pipeline (workflow) that includes two different supervised classification techniques based on regularization methods to identify biomarkers from -omics or other high dimension clinical datasets. The pipeline includes several important steps such as quality control and stability of selected biomarkers. The process takes input files (outcome and independent variables or -omics data) and pre-processes (normalization, missing values) them. After a random division of samples into training and test sets, Least Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods are applied to identify the most important features representing potential biomarker candidates. The penalization parameters are optimised using 10-fold cross validation and the process undergoes 100 iterations and a combinatorial analysis to select the best performing multivariate model. An empirical unbiased assessment of their quality as biomarkers for clinical use is performed through a Receiver Operating Characteristic curve and its Area Under the Curve analysis on both permuted and real data for 1000 different randomized training and test sets. We validated this pipeline against previously published biomarkers.ResultsWe applied this pipeline to three different datasets with previously published biomarkers: lipidomics data by Acharjee et al. (Metabolomics 13:25, 2017) and transcriptomics data by Rajamani and Bhasin (Genome Med 8:38, 2016) and Mills et al. (Blood 114:1063-1072, 2009). Our results demonstrate that our method was able to identify both previously published biomarkers as well as new variables that add value to the published results.ConclusionsWe developed a robust pipeline to identify clinically relevant biomarkers that can be applied to different -omics datasets. Such identification reveals potentially novel drug targets and can be used as a part of a machine-learning based patient stratification framework in the translational medicine settings.

Dataset Information

Antioxidants in Translational Medicine.

Publications

Antioxidants in Translational Medicine.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets