Correction: Plasma 1-deoxysphingolipids are early predictors of incident type 2 diabetes mellitus.

[This corrects the article DOI: 10.1371/journal.pone.0175776.].

Plasma 1-deoxysphingolipids are early predictors of incident type 2 diabetes mellitus.

1-Deoxysphingolipids (1-deoxySLs) are atypical sphingolipids, which are formed in a side reaction during sphingolipid de-novo synthesis. Recently, we demonstrated that 1-deoxySLs are biomarkers for the prediction of T2DM in obese, non-diabetic patients. Here we investigated the relevance of 1-deoxySLs as long-term predictive biomarkers for the incidence of T2DM in an asymptomatic population. Here, we analyzed the plasma sphingoid base profile in a nested group of non-diabetic individuals (N = 605) selected from a population-based study including 5 year follow-up data (CoLaus study). 1-DeoxySLs at baseline were significantly elevated in individuals who developed T2DM during the follow-up (p<0.001), together with increased glucose (p<5.11E-14), triglycerides (p<0.001) and HOMA-IR indices (p<0.001). 1-Deoxy-sphinganine (1-deoxySA) and 1-deoxy-sphingosine (1-deoxySO) were predictive for T2DM, even after adjusting for fasting glucose levels in the binary regression analyses. The predictive value of the combined markers 1-deoxySA+glucose were superior to glucose alone in normal-weight subjects (p<0.001) but decreased substantially with increasing BMI. Instead, plasma adiponectin and waist-to-hip ratio appeared to be better risk predictors for obese individuals (BMI>30kg/m2). In conclusion, elevated plasma 1-deoxySL levels are strong and independent risk predictors of future T2DM, especially for non-obese individuals in the general population.

A methylome-wide mQTL analysis reveals associations of methylation sites with GAD1 and HDAC3 SNPs and a general psychiatric risk score.

Genome-wide association studies have identified a number of single-nucleotide polymorphisms (SNPs) that are associated with psychiatric diseases. Increasing body of evidence suggests a complex connection of SNPs and the transcriptional and epigenetic regulation of gene expression, which is poorly understood. In the current study, we investigated the interplay between genetic risk variants, shifts in methylation and mRNA levels in whole blood from 223 adolescents distinguished by a risk for developing psychiatric disorders. We analyzed 37 SNPs previously associated with psychiatric diseases in relation to genome-wide DNA methylation levels using linear models, with Bonferroni correction and adjusting for cell-type composition. Associations between DNA methylation, mRNA levels and psychiatric disease risk evaluated by the Development and Well-Being Assessment (DAWBA) score were identified by robust linear models, Pearson's correlations and binary regression models. We detected five SNPs (in HCRTR1, GAD1, HADC3 and FKBP5) that were associated with eight CpG sites, validating five of these SNP-CpG pairs. Three of these CpG sites, that is, cg01089319 (GAD1), cg01089249 (GAD1) and cg24137543 (DIAPH1), manifest in significant gene expression changes and overlap with active regulatory regions in chromatin states of brain tissues. Importantly, methylation levels at cg01089319 were associated with the DAWBA score in the discovery group. These results show how distinct SNPs linked with psychiatric diseases are associated with epigenetic shifts with relevance for gene expression. Our findings give a novel insight on how genetic variants may modulate risks for the development of psychiatric diseases.

A genetic risk score is significantly associated with statin therapy response in the elderly population.

The ability of statins to strongly reduce low-density lipoprotein cholesterol (LDL-C) varies interindividually and is partially influenced by genetic variants. Based on a comprehensive analysis of 23 single nucleotide polymorphisms (SNPs) known to be associated with pharmacokinetics and dynamics of statins, we developed a genetic risk score to study its impact on the therapy outcome in elderly individuals under at least 5 years statin therapy. The study was performed in a population-based cohort of 1016 elderly individuals, which comprised 168 statin users investigated at age 75 and 80. Using random forest models, the major variants influencing LDL-C levels were summarized in a weighted GRS (wGRS). The wGRS was tested with lipid and glucose outcomes and validated in an independent population-based cohort including 221 statin users. Four SNPs within the APOE cluster (rs7412, rs4420638), ABCC2 (rs2002042) and CELSR/SORT1/PSRC1 (rs646776), displayed a major impact on statin efficacy. The wGRS was significantly associated with lower LDL-C at age 75 and 80. This association was replicated displaying similar results. GRS analysis is a powerful tool to evaluate the additive effects of genetic variants on statin response and to estimate the magnitude of LDL-C reduction to a considerable extent in the older population.