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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

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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

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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: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.

Project description:ObjectiveTo determine whether individual fruits are differentially associated with risk of type 2 diabetes.DesignProspective longitudinal cohort study.SettingHealth professionals in the United States.Participants66,105 women from the Nurses' Health Study (1984-2008), 85,104 women from the Nurses' Health Study II (1991-2009), and 36,173 men from the Health Professionals Follow-up Study (1986-2008) who were free of major chronic diseases at baseline in these studies.Main outcome measureIncident cases of type 2 diabetes, identified through self report and confirmed by supplementary questionnaires.ResultsDuring 3,464,641 person years of follow-up, 12,198 participants developed type 2 diabetes. After adjustment for personal, lifestyle, and dietary risk factors of diabetes, the pooled hazard ratio of type 2 diabetes for every three servings/week of total whole fruit consumption was 0.98 (95% confidence interval 0.97 [corrected] to 0.99). With mutual adjustment of individual fruits, the pooled hazard ratios of type 2 diabetes for every three servings/week were 0.74 (0.66 to 0.83) for blueberries, 0.88 (0.83 to 0.93) for grapes and raisins, 0.89 (0.79 to 1.01) for prunes, 0.93 (0.90 to 0.96) for apples and pears, 0.95 (0.91 to 0.98) for bananas, 0.95 (0.91 to 0.99) for grapefruit, 0.97 (0.92 to 1.02) for peaches, plums, and apricots, 0.99 (0.95 to 1.03) for oranges, 1.03 (0.96 to 1.10) for strawberries, and 1.10 (1.02 to 1.18) for cantaloupe. The pooled hazard ratio for the same increment in fruit juice consumption was 1.08 (1.05 to 1.11). The associations with risk of type 2 diabetes differed significantly among individual fruits (P<0.001 in all cohorts).ConclusionOur findings suggest the presence of heterogeneity in the associations between individual fruit consumption and risk of type 2 diabetes. Greater consumption of specific whole fruits, particularly blueberries, grapes, and apples, is significantly associated with a lower risk of type 2 diabetes, whereas greater consumption of fruit juice is associated with a higher risk.

Project description:BackgroundFactors predicting peripheral blood total HIV-1 DNA size in chronically infected patients with successfully suppressed viremia remain unclear. Prognostic power of such factors are of clinical significance for making clinical decisions.MethodsTwo sets of study populations were included: 490 China AIDS Clinical Trial (CACT) participants (Training cohort, followed up for 144 to 288 weeks) and 117 outpatients from Peking Union Medical College Hospital (PUMCH) (Validation cohort, followed up for more than 96 weeks). All patients were chronically HIV-1-infected and achieved successful HIV-1 plasma RNA suppression within week 48. Total HIV-1 DNA in blood at baseline, 12, 24, 48, 96, 144 and 288 weeks after combined antiretroviral therapy (cART) initiation were quantified. Generalized estimating equations and logistic regression methods were used to derive and validate a predictive model of total HIV-1 DNA after 96 weeks of cART.ResultsThe total HIV-1 DNA rapidly decreased from baseline [median = 3.00 log10 copies/106 peripheral blood mononuclear cells (PBMCs)] to week 24 (median = 2.55 log10 copies/106 PBMCs), and leveled off afterwards. Of the 490 patients who had successful HIV-1 plasma RNA suppression by 96 w post-cART, 92 (18.8%) had a low total HIV-1 DNA count (< 100 copies/106 PBMCs) at week 96. In the predictive model, lower baseline total HIV-1 DNA [risk ratio (RR) = 0.08, per 1 log10 copies/106 PBMCs, P < 0.001] and higher baseline CD4+ T cell count (RR = 1.72, per 100 cells/μL, P < 0.001) were significantly associated with a low total HIV-1 DNA count at week 96. In an independent cohort of 117 patients, this model achieved a sensitivity of 75.00% and specificity of 69.52%.ConclusionsBaseline total HIV-1 DNA and CD4+ T cell count are two independent predictors of total HIV-1 DNA after treatment. The derived model based on these two baseline factors provides a useful prognostic tool in predicting HIV-1 DNA reservoir control during cART.

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.

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