bims-cytox1 2018-04-15 papers

Issue of 2018‒04‒15
two papers selected by
Gavin McStay
New York Institute of Technology

Clin Chim Acta. 2018 Apr 06. pii: S0009-8981(18)30157-8. [Epub ahead of print]482 136-143

Meta-analysis of mitochondrial T16189C polymorphism for cancer and Type 2 diabetes risk.

Taruna Kumari, Meenakshi Vachher, Savita Bansal, Rameshwar N K Bamezai, Bhupender Kumar.

AIM: Whereas many previous studies have revealed that mitochondrial DNA (mtDNA) polymorphism T16189C is associated with the risk of cancer and Type 2 diabetes mellitus (T2DM), there are others that have disputed the same. As a result, clarity on the role of mitochondrial T16189C in these disorders is missing. The aim of this study is to evaluate the association of T16189C polymorphism with the risk of cancer and T2DM development by pooling all case-control studies available.METHODS: Published studies till November 2017 were searched from PubMed, Google scholar, Google and EMBASE and isolated a total of 36 studies having 44,203 subjects (20,439 cases and 23,764 controls) based on strict inclusion and exclusion criteria. We used the statistical software "R" to calculate the Pooled Odds Ratios and 95% confidence intervals to evaluate the association of T16189C polymorphism with a possible risk towards cancer and T2DM development.
RESULT: From the meta-analysis, we obtained Pooled Odds Ratios using Random effect model for cancer (OR: 1.20, 95% CI: 0.96-1.49, P = 0.104) and for T2DM (OR: 1.22, 95% CI: 1.09-1.36, P = 0.0004). In the subgroup analysis with Random effect model, we found that both Asians and Caucasians were at a statistically significant risk (OR: 1.25, P < 0.0001 and OR: 1.20, P < 0.0001, respectively) for the development of T2DM, whereas, a statistically non-significant risk (OR: 1.28 P = 0.1965 and OR: 1.16, P = 0.1148) emerged for the development of cancer. There was no evidence of a significant publication bias (Egger's and Begg's test) in this meta-analysis. Further sensitivity analysis also demonstrated that our meta-analysis was relatively stable and credible.
CONCLUSION: Individuals with 'C' allele at position 16,189 within the mitochondrial D-loop are seemingly at a higher risk of developing T2DM and cancer. However, before arriving at generalizations, it would be pertinent to conduct similar studies in different populations with larger numbers to corroborate these results, especially in cancer.

Keywords: Cancer; Meta-analysis; T16189C; T2DM; mtDNA

DOI: https://doi.org/10.1016/j.cca.2018.03.041

Biochem Biophys Res Commun. 2018 Apr 05. pii: S0006-291X(18)30672-7. [Epub ahead of print]

Lipidomic profiling of plasma samples from patients with mitochondrial disease.

Caixia Ren, Jia Liu, Juntuo Zhou, Hui Liang, Yizhang Zhu, Qingqing Wang, Yinglin Leng, Zhe Zhang, Yun Yuan, Zhaoxia Wang, Yuxin Yin.

Mitochondrial disease (MD) is a rare mitochondrial respiratory chain disorder with a high mortality and extremely challenging to treat. Although genomic, transcriptomic, and proteomic analyses have been performed to investigate the pathogenesis of MD, the role of metabolomics in MD, particularly of lipidomics remains unclear. This study was undertaken to identify potential lipid biomarkers of MD. An untargeted lipidomic approach was used to compare the plasma lipid metabolites in 20 MD patients and 20 controls through Liquid Chromatography coupled to Mass Spectrometry. Volcano plot analysis was performed to identify the different metabolites. Receiver operating characteristic (ROC) curves were constructed and the area under the ROC curves (AUC) was calculated to determine the potentially sensitive and specific biomarkers. A total of 41 lipids were significantly different in MD patients and controls. ROC curve analysis showed the top 5 AUC values of lipids (phosphatidylinositols 38:6, lysoPC 20:0, 19:0, 18:0, 17:0) are more than 0.99. Multivariate ROC curve based exploratory analysis showed the AUC of combination of top 5 lipids is 1, indicating they may be potentially sensitive and specific biomarkers for MD. We propose combination of these lipid species may be more valuable in predicting the development and progression of MD, and this will have important implications for the diagnosis and treatment of MD.

Keywords: Biomarkers; Lipidomics; Mitochondrial disease

DOI: https://doi.org/10.1016/j.bbrc.2018.03.160