Identification of Pre-Diabetic Biomarkers in the Progression of Diabetes Mellitus.
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ABSTRACT:
SUBMITTER: Lee JH
PROVIDER: S-EPMC8773205 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundOn a global scale, type II diabetes mellitus (T2DM) remain a major health problem and it is the driver for chronic kidney disease (CKD). Despite this association, we still do not have sufficient biomarkers to anticipate better outcomes. N-glycosylation profiles are robust biomarkers and can be used for early monitoring of the progression of T2DM towards CKD.MethodsIn this cross-sectional study, we recruited 241 T2DM patients from January to May 2016. Demographic and anthropometric data were collected, following which fasting blood samples were collected for clinical analyses. Renal function decline was determined by estimation of glomerular filtration rate (eGFR) and N-glycosylation profiles were analysed by Ultra-performance liquid chromatography (UPLC).ResultsThe prevalence of undiagnosed CKD was 31.53%. Compared to men, women had a statistically significantly higher HbA1c (p?=?0.031), TG (p?=?0.015), HDL-c (p?<?0.0001), creatinine (<0.0001), urea (p?<?0.028) and uric acid (p?<?0.0001). T2DM patients with undiagnosed CKD had higher serum creatinine (145.75?±?50.83 vs 88.59?±?19.46, p?<?0.0001), higher uric acid (361.10?±?115.37 vs 294.54?±?97.75; p?<?0.0001) and higher urea (5.17?±?2.35 vs 3.58?±?1.19; p?<?0.0001). After performing logistic regression and adjusting for age, sex and BMI, three N-glycan peaks [OR (95%CI): (GP12 (0.05(0.01-0.54), p?=?0.013)); GP16 (0.61(0.43-0.87), p?=?0.006)); GP22 (0.60(0.39-0.92), p?=?0.018)) were associated with renal function.ConclusionThere was an increased prevalence of undiagnosed CKD among T2DM patients. This prevalence is the consequence of uncontrolled modifiable risk factors, which collectively may lead to end stage renal disease (ESRD). Although, the identified N-glycans could not adequately predict incident CKD, our investigation indicates the potential role of N-glycosylation in renal function and that their inclusion may improve risk stratification for CKD.
| S-EPMC6405396 | biostudies-literature
Project description:BACKGROUND:Gestational diabetes mellitus (GDM) is one of the most common problems during pregnancy. Lack of international consistent diagnostic procedures has limit improvement of current therapeutic effectiveness. Here, we aimed to screen potential gene biomarkers that might play vital roles in GDM progression for assistance of its diagnostic and treatment. METHODS:Gene expression profiles in four GDM placentae at first trimester, four GDM placentae at second trimester, and four normal placentae were obtained from the publicly available Gene Expression Omnibus (GEO). Weighted gene coexpression network analysis (WGCNA) indicated two gene modules, that is, black and brown module, that was significantly positively and negatively correlated with GDM progression time points, respectively. Additionally, a significant positive correlation between module membership (MM) and degree in protein-protein interaction network of brown module genes was observed. RESULTS:KIF2C, CENPE, CCNA2, AURKB, MAD2L1, CCNB2, CDC20, PLK1, CCNB1, and CDK1 all have degree larger than 50 and MM larger than 0.9, so they might be valuable biomarkers in GDM. Gene set enrichment analysis inferred tight relations between carbohydrate metabolism or steroid biosynthesis-related processes and GDM progression. CONCLUSIONS:All in all, our study should provide several novel references for GDM diagnosis and therapeutic.
| S-EPMC6382444 | biostudies-literature
Project description:BackgroundWe aimed to explore metabolite biomarkers that could be used to identify pre-diabetes and type 2 diabetes mellitus (T2DM) using systematic review and meta-analysis.MethodsFour databases, the Cochrane Library, EMBASE, PubMed and Scopus were selected. A random effect model and a fixed effect model were applied to the results of forest plot analyses to determine the standardized mean difference (SMD) and 95% confidence interval (95% CI) for each metabolite. The SMD for every metabolite was then converted into an odds ratio to create an metabolite biomarker profile.ResultsTwenty-four independent studies reported data from 14,131 healthy individuals and 3499 patients with T2DM, and 14 included studies reported 4844 healthy controls and a total of 2139 pre-diabetes patients. In the serum and plasma of patients with T2DM, compared with the healthy participants, the concentrations of valine, leucine, isoleucine, proline, tyrosine, lysine and glutamate were higher and that of glycine was lower. The concentrations of isoleucine, alanine, proline, glutamate, palmitic acid, 2-aminoadipic acid and lysine were higher and those of glycine, serine, and citrulline were lower in prediabetic patients. Metabolite biomarkers of T2DM and pre-diabetes revealed that the levels of alanine, glutamate and palmitic acid (C16:0) were significantly different in T2DM and pre-diabetes.ConclusionsQuantified multiple metabolite biomarkers may reflect the different status of pre-diabetes and T2DM, and could provide an important reference for clinical diagnosis and treatment of pre-diabetes and T2DM.
| S-EPMC7685632 | biostudies-literature
Project description:BackgroundType 2 diabetes mellitus (T2DM) increases the risk of coronary heart disease (CHD) by 2-4 fold, and is associated with endothelial dysfunction, dyslipidaemia, insulin resistance, and chronic hyperglycaemia. The aim of this investigation was to assess, by a multimarker mass spectrometry approach, the predictive role of circulating proteins as biomarkers of cardiovascular damage progression associated with diabetes mellitus.MethodsThe study considered 34 patients with both T2DM and CHD, 31 patients with T2DM and without CHD, and 30 patients without diabetes with a diagnosis of CHD. Plasma samples of subjects were analysed through a multiplexed targeted liquid chromatography mass spectrometry (LC-MS)-based assay, namely Multiple Reaction Monitoring (MRM), allowing the simultaneous detection of peptides derived from a protein of interest. Gene Ontology (GO) Analysis was employed to identify enriched GO terms in the biological process, molecular function, or cellular component categories. Non-parametric multivariate methods were used to classify samples from patients and evaluate the relevance of the analysed proteins' panel.ResultsA total of 81 proteins were successfully quantified in the human plasma samples. Gene Ontology analysis assessed terms related to blood microparticles, extracellular exosomes and collagen-containing extracellular matrix. Preliminary evaluation using analysis of variance (ANOVA) of the differences in the proteomic profile among patient groups identified 13 out of the 81 proteins as significantly different. Multivariate analysis, including cluster analysis and principal component analysis, identified relevant grouping of the 13 proteins. The first main cluster comprises apolipoprotein C-III, apolipoprotein C-II, apolipoprotein A-IV, retinol-binding protein 4, lysozyme C and cystatin-C; the second one includes, albeit with sub-grouping, alpha 2 macroglobulin, afamin, kininogen 1, vitronectin, vitamin K-dependent protein S, complement factor B and mannan-binding lectin serine protease 2. Receiver operating characteristic (ROC) curves obtained with the 13 selected proteins using a nominal logistic regression indicated a significant overall distinction (p < 0.001) among the three groups of subjects, with area under the ROC curve (AUC) ranging 0.91-0.97, and sensitivity and specificity ranging from 85 to 100%.ConclusionsTargeted mass spectrometry approach indicated 13 multiple circulating proteins as possible biomarkers of cardiovascular damage progression associated with T2DM, with excellent classification results in terms of sensitivity and specificity.
| S-EPMC10800045 | biostudies-literature
Project description:Reliable protein markers for pre-diabetes in humans are not clinically available. In order to identify novel and reliable protein markers for pre-diabetes in humans, healthy volunteers and patients diagnosed with pre-diabetes and stroke were recruited for blood collection. Blood samples were collected from healthy and pre-diabetic subjects 12 h after fasting. BMI was calculated from body weight and height. Fasting blood glucose (FBG), glycated hemoglobin (HbA1C), triglyceride (TG), total cholesterol, high-density lipoprotein, low-density lipoprotein (LDL), insulin and albumin were assayed by automated clinical laboratory methods. We used a quantitative proteomics approach to identify 1074 proteins from the sera of pre-diabetic and healthy subjects. Among them, 500 proteins were then selected using Mascot analysis scores. Further, 70 out of 500 proteins were selected via volcano plot analysis according to their statistical significance and average relative protein ratio. Eventually, 7 serum proteins were singled out as candidate markers for pre-diabetes due to their diabetic relevance and statistical significance. Immunoblotting data demonstrated that laminin subunit alpha 2 (LAMA2), mixed-lineage leukemia 4 (MLL4), and plexin domain containing 2 (PLXDC2) were expressed in pre-diabetic patients but not healthy volunteers. Receiver operating characteristic curve analysis indicated that the combination of the three proteins has greater diagnostic efficacy than any individual protein. Thus, LAMA2, MLL4 and PLXDC2 are novel and reliable serum protein markers for pre-diabetic diagnosis in humans.
| S-EPMC8113970 | biostudies-literature
Project description:IntroductionMicroRNAs (miRNAs) are small, non-coding RNAs that play a critical role in diabetes development. While individual studies investigating the mechanisms of miRNA in diabetes provide valuable insights, their narrow focus limits their ability to provide a comprehensive understanding of miRNAs' role in diabetes pathogenesis and complications.MethodsTo reduce potential bias from individual studies, we employed a text mining-based approach to identify the role of miRNAs in diabetes and their potential as biomarker candidates. Abstracts of publications were tokenized, and biomedical terms were extracted for topic modeling. Four machine learning algorithms, including Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machines (SVM), were employed for diabetes classification. Feature importance was assessed to construct miRNA-diabetes networks.ResultsOur analysis identified 13 distinct topics of miRNA studies in the context of diabetes, and miRNAs exhibited a topic-specific pattern. SVM achieved a promising prediction for diabetes with an accuracy score greater than 60%. Notably, miR-146 emerged as one of the critical biomarkers for diabetes prediction, targeting multiple genes and signal pathways implicated in diabetic inflammation and neuropathy.ConclusionThis comprehensive approach yields generalizable insights into the network miRNAs-diabetes network and supports miRNAs' potential as a biomarker for diabetes.
| S-EPMC10407569 | biostudies-literature
Project description:Diabetes mellitus (DM) is one of most common chronic diseases with an increasing incidence in most countries. Diabetic neuropathy (DN) is one of the earliest and main complications of diabetic patients, which is characterized by progressive, distal-to-proximal degeneration of peripheral nerves. The cellular and molecular mechanisms that trigger DN are highly complex, heterogeneous and not completely known. Animal models have constituted a valuable tool for understanding diabetes pathophysiology; however, the temporal course of DN progression in animal models of type 2 diabetes (T2DM) is not completely understood. In this work, we characterized the onset and progression of DN in BKS diabetic (db/db) mice, including the main functional and histological features observed in the human disease. We demonstrated that diabetic animals display progressive sensory loss and electrophysiological impairments in the early-to-mid phases of the disease. Furthermore, we detected an early decrease in intraepidermal nerve fiber (IENF) density in 18-week-old diabetic mice, which is highly associated with sensory loss and constitutes a reliable marker of DN. Other common histological parameters of DN - like Schwann cells apoptosis and infiltration of CD3+ cells in the sciatic nerve - were altered in mid-to-late phases of the disease. Our results support the general consensus that DN evolves from initial functional to late structural changes. This work aimed to characterize the progression of DN in a reliable animal model sharing the main human disease features, which is necessary to assess new therapies for this complex disease. Finally, we also aimed to identify an effective temporal window where these potential treatments could be successfully applied.
| S-EPMC6176942 | biostudies-literature
Project description:Background/objectiveThe prevalence of diabetic ketoacidosis (DKA) in gestational diabetes mellitus (GDM) is very low. We describe a patient with GDM in whom severe DKA with intrauterine fetal demise developed in the setting of nonadherence to therapy.Case reportA 33-year-old woman, G2P0010, with no preexisting diabetes mellitus (DM) presented at 30 weeks of gestation with acute-onset altered sensorium, nausea, and emesis. GDM was diagnosed at 15 weeks of gestation with a serum glucose level of 266 mg/dL (70-134 mg/dL) after 1-hour 50-gram glucose challenge test. Glycated hemoglobin (HbA1C) was 5.9% (41 mmol/mol) at the time of GMD diagnosis. Insulin was initiated at week 20 of gestation. On presentation, serum glucose level of 920 mg/dL (70-110 mg/dL), pH of 7.02 (7.32-7.43), anion gap level of 38 mmol (5-17 mmol), bicarbonate level of 5.0 mEq/L (22-29 mEq/L), and large serum ketones were found. Ultrasound showed intrauterine fetal demise. She received intravenous fluids and continuous insulin. Following the spontaneous delivery of a nonviable fetus, DKA was resolved. Negative antiglutamic acid decarboxylase, islet cell, and zinc transporter 8 antibodies, C-peptide level of 2.4 ng/dL (1.1-4.4 ng/dL), and HbA1C level of 9% (75 mmol/mol) were found. Inpatient management included basal-bolus and sliding scale insulin therapies. Metformin was added upon discharge 7 days after admission. The HbA1C levels were 5.3% (34 mmol/mol) and 5% (31 mmol/mol) at the 3- and 6-month follow-ups, respectively. Insulin was discontinued. Currently, the patient is on metformin and glucagon-like peptide 1 receptor agonist.DiscussionThe development of insulin resistance during pregnancy is driven by multiple factors. Approximately 1% to 2% of pregnant women with impaired glucose tolerance develop DKA; most cases occur in women with type 1 DM. The approximate incidence of DKA in GDM is 0.02%.ConclusionDKA complicating GDM is extremely infrequent, but it cannot be dismissed. Early recognition along with prompt and appropriate medical and obstetrical management is critical.
| S-EPMC9508574 | biostudies-literature
Project description:We previously reported the association of positive psychosocial conditions with diabetic nephropathy (DN) in patients with type 2 diabetes in a cross-sectional setting. In the present study, we evaluated the association between six indicators related to psychosocial conditions and the progression of DN assessed by estimated glomerular filtration rate (eGFR) and albuminuria during 2-year observation period in 252 patients with type 2 diabetes. In unadjusted model, the subjects with higher happiness score attenuated reduced eGFR, and those with more social support attenuated increase in alubuminuria and decline eGFR. However, in adjusted model for happiness score and social support, only happiness score indicated the significant association with reduced eGFR. Gender-segregated analysis showed a significant association between happiness score and ΔeGFR in male but not in female subjects. On the other hand, decrease in eGFR was significantly attenuated in the subjects with more social support as compared to those with less social support in women but not in men. These results suggested that that psychosocial conditions could be related to the progression of DN, and that the psychosocial factors that influence in DN might differ between men and women, in Japanese patients with type 2 diabetes.
| S-EPMC8172713 | biostudies-literature
Project description:Aims/introductionWe aimed to investigate whether there are differences in the risk factors or markers for the progression of diabetic retinopathy (DR) and diabetic nephropathy (DN) in type 2 diabetes mellitus.Materials and methodsWe carried out a 3-year retrospective cohort study of 604 patients with type 2 diabetes mellitus. The outcomes were the progression of DR (worsening of the DR stage) and DN (an estimated glomerular filtration rate decline >12%) at the 3-year follow up. The mean hemoglobin A1c (HbA1c) level and HbA1c variability (HbA1c-VAR) were calculated.ResultsThe mean HbA1c and HbA1c-VAR levels were higher in the DR progressors (n = 67) than in the DR non-progressors (n = 537). The mean HbA1c was a significant predictor for DR progression independent of the duration of diabetes and HbA1c-VAR levels. The urine albumin-to-creatinine ratio at baseline and HbA1c-VAR levels were higher in the DN progressors (n = 34) than in the DN non-progressors (n = 570). The triglyceride to high-density lipoprotein cholesterol ratio at baseline tended to be higher in the DN progressors than in the DN non-progressors. HbA1c-VAR levels and the triglyceride-to-high-density lipoprotein cholesterol ratio were significant predictors for DN progression independent of estimated glomerular filtration rate and the urine albumin-to-creatinine ratio.ConclusionsAverage glycemia was significantly associated with progression of DR, whereas glycemic variability and dyslipidemia were significantly associated with progression of DN in type 2 diabetes mellitus.
| S-EPMC6497586 | biostudies-literature