Meta-Analysis of Genes in Commercially Available Nutrigenomic Tests Denotes Lack of Association with Dietary Intake and Nutrient-Related Pathologies.

Nutrigenomics is an emerging discipline that aims to investigate how individual genetic composition correlates with dietary intake, as well as how nutrition influences gene expression. Herein, the fundamental question relates to the value of nutrigenomics testing on the basis of the currently available scientific evidence. A thorough literature search has been conducted in PubMed scientific literature database for nutrigenomics research studies on 38 genes included in nutrigenomics tests provided by various private genetic testing laboratories. Data were subsequently meta-analyzed to identify possible associations between the genes of interest and dietary intake and/or nutrient-related pathologies. Data analysis occurred according to four different models due to data sparsity and inconsistency. Data from 524,592 individuals (361,153 cases and 163,439 controls) in a total of 1,170 entries were obtained. Conflicting findings indicated that there was a great incompatibility regarding the associations (or their absence) identified. No specific--and statistically significant-association was identified for any of the 38 genes of interest. In those cases, where a weak association was demonstrated, evidence was based on a limited number of studies. As solid scientific evidence is currently lacking, commercially available nutrigenomics tests cannot be presently recommended. Notwithstanding, the need for a thorough and continuous nutrigenomics research is evident as it is a highly promising tool towards precision medicine.

Comparative study and meta-analysis of meta-analysis studies for the correlation of genomic markers with early cancer detection.

A large number of common disorders, including cancer, have complex genetic traits, with multiple genetic and environmental components contributing to susceptibility. A literature search revealed that even among several meta-analyses, there were ambiguous results and conclusions. In the current study, we conducted a thorough meta-analysis gathering the published meta-analysis studies previously reported to correlate any random effect or predictive value of genome variations in certain genes for various types of cancer. The overall analysis was initially aimed to result in associations (1) among genes which when mutated lead to different types of cancer (e.g. common metabolic pathways) and (2) between groups of genes and types of cancer. We have meta-analysed 150 meta-analysis articles which included 4,474 studies, 2,452,510 cases and 3,091,626 controls (5,544,136 individuals in total) including various racial groups and other population groups (native Americans, Latinos, Aborigines, etc.). Our results were not only consistent with previously published literature but also depicted novel correlations of genes with new cancer types. Our analysis revealed a total of 17 gene-disease pairs that are affected and generated gene/disease clusters, many of which proved to be independent of the criteria used, which suggests that these clusters are biologically meaningful.