Diabetic retinopathy (DR) is a common complication of diabetes, and it is the consequence of microvascular retinal changes due to high glucose levels over a long time. Metabolomics profiling is a ...rapidly evolving method used to identify the metabolites in biological fluids and investigate disease progression. In this study, we used a targeted metabolomics approach to quantify the serum metabolites in type 2 diabetes (T2D) patients. Diabetes patients were divided into three groups based on the status of their complications: non-DR (NDR, n = 143), non-proliferative DR (NPDR, n = 123), and proliferative DR (PDR, n = 51) groups. Multiple logistic regression analysis and multiple testing corrections were performed to identify the significant differences in the metabolomics profiles of the different analysis groups. The concentrations of 62 metabolites of the NDR versus DR group, 53 metabolites of the NDR versus NPDR group, and 30 metabolites of the NDR versus PDR group were found to be significantly different. Finally, sixteen metabolites were selected as specific metabolites common to NPDR and PDR. Among them, three metabolites including total DMA, tryptophan, and kynurenine were potential makers of DR progression in T2D patients. Additionally, several metabolites such as carnitines, several amino acids, and phosphatidylcholines also showed a marker potential. The metabolite signatures identified in this study will provide insight into the mechanisms underlying DR development and progression in T2D patients in future studies.
We introduce the design and implementation of a new array, the Korea Biobank Array (referred to as KoreanChip), optimized for the Korean population and demonstrate findings from GWAS of blood ...biochemical traits. KoreanChip comprised >833,000 markers including >247,000 rare-frequency or functional variants estimated from >2,500 sequencing data in Koreans. Of the 833 K markers, 208 K functional markers were directly genotyped. Particularly, >89 K markers were presented in East Asians. KoreanChip achieved higher imputation performance owing to the excellent genomic coverage of 95.38% for common and 73.65% for low-frequency variants. From GWAS (Genome-wide association study) using 6,949 individuals, 28 associations were successfully recapitulated. Moreover, 9 missense variants were newly identified, of which we identified new associations between a common population-specific missense variant, rs671 (p.Glu457Lys) of ALDH2, and two traits including aspartate aminotransferase (P = 5.20 × 10
) and alanine aminotransferase (P = 4.98 × 10
). Furthermore, two novel missense variants of GPT with rare frequency in East Asians but extreme rarity in other populations were associated with alanine aminotransferase (rs200088103; p.Arg133Trp, P = 2.02 × 10
and rs748547625; p.Arg143Cys, P = 1.41 × 10
). These variants were successfully replicated in 6,000 individuals (P = 5.30 × 10
and P = 1.24 × 10
). GWAS results suggest the promising utility of KoreanChip with a substantial number of damaging variants to identify new population-specific disease-associated rare/functional variants.
Objective
To investigate sleep disturbances that induce cognitive changes over 4 years in nondemented elderlies.
Methods
Data were acquired from a nationwide, population‐based, prospective cohort of ...Korean elderlies (2,238 normal cognition NC and 655 mild cognitive impairment MCI). At baseline and 4‐year follow‐up assessments, sleep‐related parameters (midsleep time, sleep duration, sleep latency, subjective sleep quality, sleep efficiency, and daytime dysfunction) and cognitive status were measured using the Pittsburgh Sleep Quality Index and Consortium to Establish a Registry for Alzheimer's Disease Assessment, respectively. We used logistic regression models adjusted for covariates including age, sex, education, apolipoprotein E genotype, Geriatric Depression Scale, Cumulative Illness Rating Scale, and physical activity.
Results
In participants with NC, long sleep latency (>30 minutes), long sleep duration (≥7.95 hours), and late midsleep time (after 3:00 am) at baseline were related to the risk of cognitive decline at 4‐year follow‐up assessment; odds ratio (OR) was 1.40 for long sleep latency, 1.67 for long sleep duration, and 0.61 for late midsleep time. These relationships remained significant when these variables maintained their status throughout the follow‐up period. Newly developed long sleep latency also doubled the risk of cognitive decline. In those with MCI, however, only long sleep latency reduced the chance of reversion to NC (OR = 0.69).
Interpretation
As early markers of cognitive decline, long sleep latency can be used for elderlies with NC or MCI, whereas long sleep duration and relatively early sleep time might be used for cognitively normal elderlies only. Ann Neurol 2018;83:472–482
The single nucleotide polymorphism rs9939609 of the gene FTO, which encodes fat mass and obesity-associated protein, is strongly associated with obesity and type 2 diabetes (T2D) in multiple ...populations; however, the underlying mechanism of this association is unclear. The present study aimed to investigate FTO genotype-dependent metabolic changes in obesity and T2D. To elucidate metabolic dysregulation associated with disease risk genotype, genomic and metabolomic datasets were recruited from 2,577 participants of the Korean Association REsource (KARE) cohort, including 40 homozygous carriers of the FTO risk allele (AA), 570 heterozygous carriers (AT), and 1,967 participants carrying no risk allele (TT). A total of 134 serum metabolites were quantified using a targeted metabolomics approach. Through comparison of various statistical methods, seven metabolites were identified that are significantly altered in obesity and T2D based on the FTO risk allele (adjusted p < 0.05). These identified metabolites are relevant to phosphatidylcholine metabolic pathway, and previously reported to be metabolic markers of obesity and T2D. In conclusion, using metabolomics with the information from genome-wide association studies revealed significantly altered metabolites depending on the FTO genotype in complex disorders. This study may contribute to a better understanding of the biological mechanisms linking obesity and T2D.
Glycated hemoglobin (HbA1c) is an indicator of the average blood glucose concentration. Failing to control HbA1c levels can accelerate the development of complications in patients with diabetes. ...Although metabolite profiles associated with HbA1c level in diabetes patients have been characterized using different platforms, more studies using high-throughput technology will be helpful to identify additional metabolites related to diabetes. Type 2 diabetes (T2D) patients were divided into two groups based on the HbA1c level: normal (HbA1c ≤6%) and high (HbA1c ≥9%) in both discovery and replication sets. A targeted metabolomics approach was used to quantify serum metabolites and multivariate logistic regression was used to identify significant differences between groups. The concentrations of 22 metabolites differed significantly between the two groups in the discovery set. In the replication set, the levels of 21 metabolites, including 16 metabolites identified in the discovery set, differed between groups. Among these, concentrations of eleven amino acids and one phosphatidylcholine (PC), lysoPC a C16:1, were higher and four metabolites, including three PCs (PC ae C36:1, PC aa C26:0, PC aa C34:2) and hexose, were lower in the group with normal HbA1c group than in the group with high HbA1c. Metabolites with high concentrations in the normal HbA1c group, such as glycine, valine, and PCs, may contribute to reducing HbA1c levels in patients with T2D. The metabolite signatures identified in this study provide insight into the mechanisms underlying changes in HbA1c levels in T2D.
Systemic lupus erythematosus (SLE), an autoimmune disorder, has been associated with nearly 100 susceptibility loci. Nevertheless, these loci only partially explain SLE heritability and their ...putative causal variants are rarely prioritised, which make challenging to elucidate disease biology. To detect new SLE loci and causal variants, we performed the largest genome-wide meta-analysis for SLE in East Asian populations.
We newly genotyped 10 029 SLE cases and 180 167 controls and subsequently meta-analysed them jointly with 3348 SLE cases and 14 826 controls from published studies in East Asians. We further applied a Bayesian statistical approach to localise the putative causal variants for SLE associations.
We identified 113 genetic regions including 46 novel loci at genome-wide significance (p<5×10
). Conditional analysis detected 233 association signals within these loci, which suggest widespread allelic heterogeneity. We detected genome-wide associations at six new missense variants. Bayesian statistical fine-mapping analysis prioritised the putative causal variants to a small set of variants (95% credible set size ≤10) for 28 association signals. We identified 110 putative causal variants with posterior probabilities ≥0.1 for 57 SLE loci, among which we prioritised 10 most likely putative causal variants (posterior probability ≥0.8). Linkage disequilibrium score regression detected genetic correlations for SLE with albumin/globulin ratio (r
=-0.242) and non-albumin protein (r
=0.238).
This study reiterates the power of large-scale genome-wide meta-analysis for novel genetic discovery. These findings shed light on genetic and biological understandings of SLE.
Genome-wide association studies (GWAS) in rheumatoid arthritis (RA) have discovered over 100 RA loci, explaining patient-relevant RA pathogenesis but showing a large fraction of missing heritability. ...As a continuous effort, we conducted GWAS in a large Korean RA case-control population.
We newly generated genome-wide variant data in two independent Korean cohorts comprising 4068 RA cases and 36 487 controls, followed by a whole-genome imputation and a meta-analysis of the disease association results in the two cohorts. By integrating publicly available omics data with the GWAS results, a series of bioinformatic analyses were conducted to prioritise the RA-risk genes in RA loci and to dissect biological mechanisms underlying disease associations.
We identified six new RA-risk loci (
,
,
,
,
and
) with p
<5×10
and consistent disease effect sizes in the two cohorts. A total of 122 genes were prioritised from the 6 novel and 13 replicated RA loci based on physical distance, regulatory variants and chromatin interaction. Bioinformatics analyses highlighted potentially RA-relevant tissues (including immune tissues, lung and small intestine) with tissue-specific expression of RA-associated genes and suggested the immune-related gene sets (such as CD40 pathway, IL-21-mediated pathway and citrullination) and the risk-allele sharing with other diseases.
This study identified six new RA-associated loci that contributed to better understanding of the genetic aetiology and biology in RA.
Prediabetes (PD) is a high-risk state of developing type 2 diabetes, and cardiovascular and metabolic diseases. Metabolomics-based biomarker studies can provide advanced opportunities for prediction ...of PD over the conventional methods. Here, we aimed to identify metabolic markers and verify their abilities to predict PD, as compared to the performance of the traditional clinical risk factor (CRF) and previously reported metabolites in other population-based studies. Targeted metabolites quantification was performed in 1723 participants in the Korea Association REsource (KARE) cohort, from which 500 normal individuals were followed up for 6 years. We selected 12 significant metabolic markers, including five amino acids, four glycerophospholipids, two sphingolipids, and one acylcarnitine, at baseline, resulting in a predicted incidence of PD with an area under the curve (AUC) of 0.71 during follow-up. The performance of these metabolic markers compared to that of fasting glucose was significantly higher in obese patients (body mass index: BMI ≥ 25 kg/m
, 0.79 vs. 0.58, P < 0.001). The combination with metabolic markers, CRF, and fasting glucose yielded the best prediction performance (AUC = 0.86). Our results revealed that metabolic markers were not only associated with the risk of PD, but also improved the prediction performance in combination with conventional approaches.
Mitochondrial translocation of pro-apoptotic Bax prior to apoptosis is well established after treatment with many cell death stimulants or under apoptosis-inducing conditions. The mechanism of ...mitochondrial translocation of Bax is, however, still unknown. The aim of this work was to investigate the mechanism of Bax activation and mitochondrial translocation to initiate apoptosis of human hepatoma HepG2 and porcine kidney LLC-PK1 cells exposed to various cell death agonists. Phosphorylation of Bax by JNK and p38 kinase activated after treatment with staurosporine, H2O2, etoposide, and UV light was demonstrated by the shift in the pI value of Bax on two-dimensional gels and confirmed by metabolic labeling with inorganic 32Pphosphate in HepG2 cells. Specific inhibitors of JNK and p38 kinase significantly inhibited Bax phosphorylation and mitochondrial translocation and apoptosis of HepG2 cells. A specific small interfering RNA to MAPKK4 (the upstream protein kinase of JNK and p38 kinase) markedly decreased the levels of MAPKK4 and MAPKK3/6, blocked the activation of JNK or p38 kinase, and inhibited Bax phosphorylation. However, the negative control small interfering RNA did not cause these changes. Confocal microscopy of various Bax mutants showed differential rates of mitochondrial translocation of Bax before and after staurosporine treatment. Among the Bax mutants, T167D did not translocate to mitochondria after staurosporine exposure, suggesting that Thr167 is a potential phosphorylation site. In conclusion, our results demonstrate, for the first time, that Bax is phosphorylated by stress-activated JNK and/or p38 kinase and that phosphorylation of Bax leads to mitochondrial translocation prior to apoptosis.
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