Dataset Information

Metabolic Biomarkers for Prognostic Prediction of Pre-diabetes: results from a longitudinal cohort study.

ABSTRACT: To investigate the metabolic biomarkers of predicting the transition from pre-diabetes (pre-DM) to normal glucose regulation (NGR) and diabetes (DM) in a longitudinal cohort study. 108 participants with pre-DM were followed up for ten years and divided into 3 groups according to different glycemic outcomes. 20 participants progressed to DM, 20 regressed to NGR, and 68 remained at pre-DM. Alterations in plasma metabolites in these groups were evaluated by untargeted ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). Twenty three metabolites related to glycerophospholipid metabolism, oxidation and antioxidation were associated with the process from pre-DM to NGR, while twenty two metabolites related to amino acid metabolism, glycerophospholipid metabolism and mitochondrial ?-oxidation played important roles in the progression to DM. Results from stepwise logistic regression analysis showed that five biomarkers (20-Hydroxy-leukotriene E4, Lysopc(20:4), 5-methoxytryptamine, Endomorphin-1, Lysopc(20:3)) were good prediction for the restoration to NGR, and five biomarkers (Iso-valeraldehyde, linoleic acid, Lysopc(18:1), 2-Pyrroloylglycine, Dityrosine) for the development of DM. The findings suggest that the combination of these potential metabolites may be used for the prognosis of pre-DM. Targeting the pathways that involved in these prognostic biomarkers would be beneficial for the regression to NGR and the early prevention of DM among pre-DM.

SUBMITTER: Zeng H

PROVIDER: S-EPMC5529507 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Metabolic Biomarkers for Prognostic Prediction of Pre-diabetes: results from a longitudinal cohort study.

Zeng Hailuan H Tong Renchao R Tong Wenxin W Yang Qiaoling Q Qiu Miaoyan M Xiong Aizhen A Sun Siming S Ding Lili L Zhang Hongli H Yang Li L Tian Jingyan J

Scientific reports 20170726 1

To investigate the metabolic biomarkers of predicting the transition from pre-diabetes (pre-DM) to normal glucose regulation (NGR) and diabetes (DM) in a longitudinal cohort study. 108 participants with pre-DM were followed up for ten years and divided into 3 groups according to different glycemic outcomes. 20 participants progressed to DM, 20 regressed to NGR, and 68 remained at pre-DM. Alterations in plasma metabolites in these groups were evaluated by untargeted ultra-performance liquid chrom ...[more]

PMID: 28747646

Similar Datasets

Project description:BackgroundSelective fetal growth restriction (sFGR) is an extreme complication that significantly increases the risk of perinatal mortality and long-term adverse neurological outcomes in offspring, affecting approximately 15% of monochorionic diamniotic (MCDA) twin pregnancies. The lack of longitudinal cohort studies hinders the early prediction and intervention of sFGR.MethodsWe constructed a prospective longitudinal cohort study of sFGR, and quantified 25 key metabolites in 337 samples from maternal plasma in the first, second, and third trimester and from cord plasma. In particular, our study examined fetal growth and brain injury data from ultrasonography and used the Ages and Stages Questionnaire-third edition subscale (ASQ-3) to evaluate the long-term neurocognitive behavioral development of infants aged 2-3 years. Furthermore, we correlated metabolite levels with ultrasound data, including physical development and brain injury indicators, and ASQ-3 data using Spearman's-based correlation tests. In addition, special combinations of differential metabolites were used to construct predictive models for the occurrence of sFGR and fetal brain injury.ResultsOur findings revealed various dynamic patterns for these metabolites during pregnancy and a maximum of differential metabolites between sFGR and MCDA in the second trimester (n = 8). The combination of L-phenylalanine, L-leucine, and L-isoleucine in the second trimester, which were closely related to fetal growth indicators, was highly predictive of sFGR occurrence (area under the curve [AUC]: 0.878). The combination of L-serine, L-histidine, and L-arginine in the first trimester and creatinine in the second trimester was correlated with long-term neurocognitive behavioral development and showed the capacity to identify fetal brain injury with high accuracy (AUC: 0.94).ConclusionsThe performance of maternal plasma metabolites from the first and second trimester is superior to those from the third trimester and cord plasma in discerning sFGR and fetal brain injury. These metabolites may serve as useful biomarkers for early prediction and promising targets for early intervention in clinical settings.

Project description:ObjectivesWe examined the association between diabetes mellitus (DM) and longitudinal MRI biomarkers for thigh muscle degeneration in patients with knee osteoarthritis (KOA) and their mediatory role in worsening KOA-related symptoms.MethodsThe Osteoarthritis Initiative (OAI) participants with radiographic KOA (Kellgren-Lawrence grade ≥ 2) were included. Thighs and corresponding knees of KOA patients with versus without self-reported DM were matched for potential confounders using propensity score (PS) matching. We developed and used a validated deep learning method for longitudinal thigh segmentation. We assessed the association of DM with 4-year longitudinal muscle degeneration in biomarkers of muscle cross-sectional area (CSA) and contractile percentage (non-fat CSA/total CSA). We further investigated whether DM is associated with 9-year risk of KOA radiographic progression, knee replacement (KR), and symptoms worsening. Finally, we evaluated whether the DM-KOA worsening association is mediated through preceding muscle degeneration.ResultsAfter PS matching, 698 thighs/knees were included (185:513 with:without DM; average ± SD age:64 ± 8-years; female/male:1.4). Baseline DM was associated with a decreased contractile percent of total thigh muscles and quadriceps (mean difference, 95%CI -0.16%/year, -0.25 to -0.07, and -0.21%/year, -0.33 to -0.08). DM was also associated with an increased risk of worsening KOA-related symptoms (hazard ratio, 95%CI 1.70, 1.18-2.46) but not radiographic progression or KR. The decrease in quadriceps contractile percent partially mediated the increased risk of symptoms worsening in patients with DM.ConclusionsBaseline DM is associated with thigh muscle degeneration and KOA-related symptoms worsening. As a potentially modifiable risk factor, DM-associated longitudinal thigh muscle degeneration may partially mediate the symptoms worsening in patients with DM and coexisting KOA.Key points• Diabetes mellitus (DM) is associated with worsening knee osteoarthritis (KOA)-related symptoms. • As a potentially modifiable factor, DM-associated thigh muscle (quadriceps) degeneration partially mediates the worsening of KOA-related symptoms.

Project description:IntroductionGestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M.MethodsPlasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model.ResultsWe found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR < 0.05]. Based on the different clinical characteristics and Olink markers, a total of nine predictors, namely pre-pregnancy body mass index (BMI), weight gain at 24 gestational weeks (gw), parity, oral glucose tolerance test (OGTT) 2 h glucose at 24 gw, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an area under the ROC of 0.970 (sensitivity = 0.955, specificity = 0.886), and was well calibrated (P Hosmer-Lemeshow test = 0.897).ConclusionThe Model included pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw had good prediction performance for predicting macrosomia in women with GDM.

Project description:ObjectivesTo identify maternal plasma protein markers for early preeclampsia (delivery <34 weeks of gestation) and to determine whether the prediction performance is affected by disease severity and presence of placental lesions consistent with maternal vascular malperfusion (MVM) among cases.Study designThis longitudinal case-control study included 90 patients with a normal pregnancy and 33 patients with early preeclampsia. Two to six maternal plasma samples were collected throughout gestation from each woman. The abundance of 1,125 proteins was measured using high-affinity aptamer-based proteomic assays, and data were modeled using linear mixed-effects models. After data transformation into multiples of the mean values for gestational age, parsimonious linear discriminant analysis risk models were fit for each gestational-age interval (8-16, 16.1-22, 22.1-28, 28.1-32 weeks). Proteomic profiles of early preeclampsia cases were also compared to those of a combined set of controls and late preeclampsia cases (n = 76) reported previously. Prediction performance was estimated via bootstrap.ResultsWe found that 1) multi-protein models at 16.1-22 weeks of gestation predicted early preeclampsia with a sensitivity of 71% at a false-positive rate (FPR) of 10%. High abundance of matrix metalloproteinase-7 and glycoprotein IIbIIIa complex were the most reliable predictors at this gestational age; 2) at 22.1-28 weeks of gestation, lower abundance of placental growth factor (PlGF) and vascular endothelial growth factor A, isoform 121 (VEGF-121), as well as elevated sialic acid binding immunoglobulin-like lectin 6 (siglec-6) and activin-A, were the best predictors of the subsequent development of early preeclampsia (81% sensitivity, FPR = 10%); 3) at 28.1-32 weeks of gestation, the sensitivity of multi-protein models was 85% (FPR = 10%) with the best predictors being activated leukocyte cell adhesion molecule, siglec-6, and VEGF-121; 4) the increase in siglec-6, activin-A, and VEGF-121 at 22.1-28 weeks of gestation differentiated women who subsequently developed early preeclampsia from those who had a normal pregnancy or developed late preeclampsia (sensitivity 77%, FPR = 10%); 5) the sensitivity of risk models was higher for early preeclampsia with placental MVM lesions than for the entire early preeclampsia group (90% versus 71% at 16.1-22 weeks; 87% versus 81% at 22.1-28 weeks; and 90% versus 85% at 28.1-32 weeks, all FPR = 10%); and 6) the sensitivity of prediction models was higher for severe early preeclampsia than for the entire early preeclampsia group (84% versus 71% at 16.1-22 weeks).ConclusionWe have presented herein a catalogue of proteome changes in maternal plasma proteome that precede the diagnosis of preeclampsia and can distinguish among early and late phenotypes. The sensitivity of maternal plasma protein models for early preeclampsia is higher in women with underlying vascular placental disease and in those with a severe phenotype.

Project description:ObjectiveThere is still limited evidence regarding the relationship between the estimated glomerular filtration rate (eGFR) and pre-diabetes. For that reason, our research aims to survey the association of eGFR with pre-diabetes.MethodsThis study was a retrospective cohort study, which consecutively and non-selectively collected a total of 173301 participants from Rich Healthcare Group in China from January 2010 to 2016. We then used the Cox proportional-hazards regression model to explore the relationship between baseline eGFR and pre-diabetes risk. Using a Cox proportional hazards regression with cubic spline function and smooth curve fitting (cubical spline smoothing), we were able to determine the non-linear relationship between eGFR and pre-diabetes. Additionally, we also conducted a series of sensitivity analyses and subgroup analyses. The DATADRYAD website was updated with data.ResultsThe mean age of the included individuals was 40.95 ± 11.94 years old, and 92318 (53.27%) were male. The mean baseline eGFR was 111.40 ± 14.77 ml/min per 1.73 m2. During a median follow-up time of 3.0 years, 18333 (10.58%) people experienced pre-diabetes. As a result of adjusting for covariates, eGFR had a negative association with incident pre-diabetes (HR=0.993, 95%CI: 0.992-0.995). There was also a U-shaped curve relationship between eGFR and pre-diabetes, and the inflection point of eGFR was 129.793 ml/min per 1.73 m2. HRs on the left and right sides of the inflection point were respectively 0.993 (0.991-0.994) and 1.023 (1.010- 1.037). Our results were robust in the sensitivity analysis. Subgroup analyses indicated that eGFR was strongly associated with the risk of pre-diabetes among participants who were younger than 30 years and 40-70 years, as well as among those who had never smoked. In contrast, the association of eGFR with the risk of pre-diabetes was attenuated among participants who were 30-40 years of age and 70 years of age or older, and among those who currently smoked.ConclusionThis study demonstrates a negative and U-shaped curve association between eGFR and the risk of pre-diabetes among the general Chinese population. Either reduced renal function or glomerular hyperperfusion status may be associated with an increased risk of prediabetes.

Project description:BackgroundLate-onset preeclampsia is the most prevalent phenotype of this syndrome; nevertheless, only a few biomarkers for its early diagnosis have been reported. We sought to correct this deficiency using a high through-put proteomic platform.MethodsA case-control longitudinal study was conducted, including 90 patients with normal pregnancies and 76 patients with late-onset preeclampsia (diagnosed at ≥34 weeks of gestation). Maternal plasma samples were collected throughout gestation (normal pregnancy: 2-6 samples per patient, median of 2; late-onset preeclampsia: 2-6, median of 5). The abundance of 1,125 proteins was measured using an aptamers-based proteomics technique. Protein abundance in normal pregnancies was modeled using linear mixed-effects models to estimate mean abundance as a function of gestational age. Data was then expressed as multiples of-the-mean (MoM) values in normal pregnancies. Multi-marker prediction models were built using data from one of five gestational age intervals (8-16, 16.1-22, 22.1-28, 28.1-32, 32.1-36 weeks of gestation). The predictive performance of the best combination of proteins was compared to placental growth factor (PIGF) using bootstrap.Results1) At 8-16 weeks of gestation, the best prediction model included only one protein, matrix metalloproteinase 7 (MMP-7), that had a sensitivity of 69% at a false positive rate (FPR) of 20% (AUC = 0.76); 2) at 16.1-22 weeks of gestation, MMP-7 was the single best predictor of late-onset preeclampsia with a sensitivity of 70% at a FPR of 20% (AUC = 0.82); 3) after 22 weeks of gestation, PlGF was the best predictor of late-onset preeclampsia, identifying 1/3 to 1/2 of the patients destined to develop this syndrome (FPR = 20%); 4) 36 proteins were associated with late-onset preeclampsia in at least one interval of gestation (after adjustment for covariates); 5) several biological processes, such as positive regulation of vascular endothelial growth factor receptor signaling pathway, were perturbed; and 6) from 22.1 weeks of gestation onward, the set of proteins most predictive of severe preeclampsia was different from the set most predictive of the mild form of this syndrome.ConclusionsElevated MMP-7 early in gestation (8-22 weeks) and low PlGF later in gestation (after 22 weeks) are the strongest predictors for the subsequent development of late-onset preeclampsia, suggesting that the optimal identification of patients at risk may involve a two-step diagnostic process.

Dataset Information

Metabolic Biomarkers for Prognostic Prediction of Pre-diabetes: results from a longitudinal cohort study.

Publications

Metabolic Biomarkers for Prognostic Prediction of Pre-diabetes: results from a longitudinal cohort study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets