Type 1 Diabetes Biomarkers
NCBI: db=pubmed; Term="biomarker"[Title/Abstract] AND "Diabetes Mellitus, Type 1"[Mesh]
Updated: 6 hours 8 min ago
Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes.
Genes Immun. 2012 Dec;13(8):593-604
Authors: Levy H, Wang X, Kaldunski M, Jia S, Kramer J, Pavletich SJ, Reske M, Gessel T, Yassai M, Quasney MW, Dahmer MK, Gorski J, Hessner MJ
The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding therapeutic decisions and monitoring interventions. We previously demonstrated that plasma samples from recent-onset type 1 diabetes (RO T1D) patients induce a proinflammatory transcriptional signature in freshly drawn peripheral blood mononuclear cells (PBMCs) relative to that of unrelated healthy controls (HC). Here, using cryopreserved PBMC, we analyzed larger RO T1D and HC cohorts, examined T1D progression in pre-onset samples, and compared the RO T1D signature to those associated with three disorders characterized by airway infection and inflammation. The RO T1D signature, consisting of interleukin-1 cytokine family members, chemokines involved in immunocyte chemotaxis, immune receptors and signaling molecules, was detected during early pre-diabetes and found to resolve post-onset. The signatures associated with cystic fibrosis patients chronically infected with Pseudomonas aeruginosa, patients with confirmed bacterial pneumonia, and subjects with H1N1 influenza all reflected immunological activation, yet each were distinct from one another and negatively correlated with that of T1D. This study highlights the remarkable capacity of cells to serve as biosensors capable of sensitively and comprehensively differentiating immunological states.
PMID: 22972474 [PubMed - indexed for MEDLINE]
Bag of Naïve Bayes: biomarker selection and classification from genome-wide SNP data.
BMC Bioinformatics. 2012;13 Suppl 14:S2
Authors: Sambo F, Trifoglio E, Di Camillo B, Toffolo GM, Cobelli C
BACKGROUND: Multifactorial diseases arise from complex patterns of interaction between a set of genetic traits and the environment. To fully capture the genetic biomarkers that jointly explain the heritability component of a disease, thus, all SNPs from a genome-wide association study should be analyzed simultaneously.
RESULTS: In this paper, we present Bag of Naïve Bayes (BoNB), an algorithm for genetic biomarker selection and subjects classification from the simultaneous analysis of genome-wide SNP data. BoNB is based on the Naïve Bayes classification framework, enriched by three main features: bootstrap aggregating of an ensemble of Naïve Bayes classifiers, a novel strategy for ranking and selecting the attributes used by each classifier in the ensemble and a permutation-based procedure for selecting significant biomarkers, based on their marginal utility in the classification process. BoNB is tested on the Wellcome Trust Case-Control study on Type 1 Diabetes and its performance is compared with the ones of both a standard Naïve Bayes algorithm and HyperLASSO, a penalized logistic regression algorithm from the state-of-the-art in simultaneous genome-wide data analysis.
CONCLUSIONS: The significantly higher classification accuracy obtained by BoNB, together with the significance of the biomarkers identified from the Type 1 Diabetes dataset, prove the effectiveness of BoNB as an algorithm for both classification and biomarker selection from genome-wide SNP data.
AVAILABILITY: Source code of the BoNB algorithm is released under the GNU General Public Licence and is available at http://www.dei.unipd.it/~sambofra/bonb.html.
PMID: 23095127 [PubMed - indexed for MEDLINE]
Can improved glycemic control slow renal function decline at all stages of diabetic nephropathy?
Semin Nephrol. 2012 Sep;32(5):423-31
Authors: Goel G, Perkins BA
Observational studies have shown the strong association between level of glycemic control and the key outcome measure, risk of glomerular filtration rate (GFR) loss rather than subsequent course of albumin excretion, in type 1 diabetes patients at all stages of nephropathy. However, it has not been clear if clinical interventions designed to normalize glycemic control are equally effective at all stages, such as primary prevention in normoalbuminuric patients, secondary prevention in microalbuminuria and macroalbuminuria, or tertiary prevention aimed at slowing or reversing further loss of GFR once impaired. Substantial randomized controlled trial data from the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications exists to support postponement, but not outright prevention, of GFR loss in normoalbuminuric patients. Although secondary and tertiary prevention systematic studies are limited to methodologically insufficient insulin pump and transplantation trials, the reversal of advanced glomerular lesions observed in whole-pancreas transplant recipients who experienced long-term glycemic normalization offers convincing support for further research into glycemic interventions specifically for GFR preservation. In light of existing literature, we encourage the design of secondary and tertiary prevention trials that incorporate biomarker methods for identifying patients at highest risk of GFR loss because interventions to normalize hyperglycemia are resource-intensive and may be applied unnecessarily to clinical populations at low long-term GFR loss risk.
PMID: 23062982 [PubMed - indexed for MEDLINE]
Circulating miR-375 as a biomarker of β-cell death and diabetes in mice.
Endocrinology. 2013 Feb;154(2):603-8
Authors: Erener S, Mojibian M, Fox JK, Denroche HC, Kieffer TJ
Type 1 diabetes is a progressive autoimmune disease that is largely silent in its initial stages. Yet, sensitive methods for detection of β-cell death and prediction and prevention of diabetes are lacking. Micro-RNAs (miRNAs) have been found at high concentrations in body fluids. Here in this study we sought to determine whether an islet enriched miRNA, miR-375, is a suitable blood marker to detect β-cell death and predict diabetes in mice. We measured miR-375 levels by quantitative RT-PCR in plasma samples of streptozotocin (STZ)-treated C57BL/6 mice and nonobese diabetic (NOD) mice. We also measured miR-375 levels in media samples of cytokine- or STZ-treated islets in the presence or absence of cell-death inhibitors. High-dose STZ administration dramatically increased circulating miR-375 levels, prior to the onset of hyperglycemia. Similarly, in the NOD mouse model of autoimmune diabetes, circulating miR-375 levels were significantly increased 2 weeks before diabetes onset. Moreover, cytokine- and STZ-induced cell death in isolated mouse islets produced a striking increase in extracellular miR-375 levels, which was reduced by cell death inhibitors. These data suggest that circulating miR-375 can be used as a marker of β-cell death and potential predictor of diabetes.
PMID: 23321698 [PubMed - indexed for MEDLINE]
Metabolomic analysis of pancreatic β-cell insulin release in response to glucose.
Islets. 2012 May-Jun;4(3):210-22
Authors: Huang M, Joseph JW
Defining the key metabolic pathways that are important for fuel-regulated insulin secretion is critical to providing a complete picture of how nutrients regulate insulin secretion. We have performed a detailed metabolomics study of the clonal β-cell line 832/13 using a gas chromatography-mass spectrometer (GC-MS) to investigate potential coupling factors that link metabolic pathways to insulin secretion. Mid-polar and polar metabolites, extracted from the 832/13 β-cells, were derivatized and then run on a GC/MS to identify and quantify metabolite concentrations. Three hundred fifty-five out of 527 chromatographic peaks could be identified as metabolites by our metabolomic platform. These identified metabolites allowed us to perform a systematic analysis of key pathways involved in glucose-stimulated insulin secretion (GSIS). Of these metabolites, 41 were consistently identified as biomarker for GSIS by orthogonal partial least-squares (OPLS). Most of the identified metabolites are from common metabolic pathways including glycolytic, sorbitol-aldose reductase pathway, pentose phosphate pathway, and the TCA cycle suggesting these pathways play an important role in GSIS. Lipids and related products were also shown to contribute to the clustering of high glucose sample groups. Amino acids lysine, tyrosine, alanine and serine were upregulated by glucose whereas aspartic acid was downregulated by glucose suggesting these amino acids might play a key role in GSIS. In summary, a coordinated signaling cascade elicited by glucose metabolism in pancreatic β-cells is revealed by our metabolomics platform providing a new conceptual framework for future research and/or drug discovery.
PMID: 22847496 [PubMed - indexed for MEDLINE]
Relationship between circulating endothelial progenitor cells and endothelial dysfunction in children with type 1 diabetes: a novel paradigm of early atherosclerosis in high-risk young patients.
Eur J Endocrinol. 2013 Feb;168(2):153-61
Authors: Głowińska-Olszewska B, Moniuszko M, Hryniewicz A, Jeznach M, Rusak M, Dąbrowska M, Łuczyński W, Bodzenta-Łukaszyk A, Bossowski A
OBJECTIVE: The low number of circulating endothelial progenitor cells (EPCs) has emerged as a biomarker of cardiovascular (CV) risk in adults. Data regarding EPCs in paediatric populations with CV risk factors are limited. The aim of the study was to estimate the EPC number and its relationship with vascular function and structure in children with type 1 diabetes mellitus (T1DM).
DESIGN AND METHODS: We performed a comparative analysis of 52 children with T1DM (mean age 14.5 years; diabetes duration, 6.0 years; HbA1c level, 8.5%) and 36 healthy age- and gender-matched control children. EPCs were identified and analysed by flow cytometry with the use of MABs directed against CD34, CD144 (VE-cadherin) and CD309 (VEGFR-2). sICAM-1, hsCRP, thrombomodulin and adiponectin levels were also assessed. We evaluated vascular function (flow-mediated dilation (FMD)) and structure (carotid intima-media thickness (IMT)) ultrasonographically.
RESULTS: Frequencies of CD34+ cells were similar in both groups (P=0.30). In contrast, frequencies of CD34+VE-cadherin+ cells were significantly higher in diabetic children compared with the healthy group (P=0.003). Similarly, diabetic patients tended to present with higher frequencies of CD34+VEGFR+ cells (P=0.06). FMD was lower (6.9 vs 10.5%, P=0.002) and IMT was higher (0.50 vs 0.44 mm, P=0.0006) in diabetic children. We demonstrated a significant relationship between CD34+VEGFR-2+ cells and BMI (r=0.3, P=0.014), HDL (r=-0.27, P=0.04), sICAM-1 (r=0.47, P=0.023) and FMD (r=-0.45, P<0.001). Similarly, frequencies of CD34+VE-cadherin+ cells were significantly correlated with BMI (r=0.32, P=0.02) and FMD (r=-0.31, P=0.03).
CONCLUSIONS: We demonstrated here that increased frequencies of EPCs observed in diabetic children are negatively correlated with endothelial function. Further studies are warranted to assess whether this phenomenon might result from effective mobilisation of EPCs in order to repair damaged endothelium in children at increased risk for atherosclerosis.
PMID: 23111589 [PubMed - indexed for MEDLINE]
Soluble RAGE and malondialdehyde in type 1 diabetes patients without chronic complications during the course of the disease.
Diab Vasc Dis Res. 2012 Oct;9(4):309-14
Authors: Reis JS, Veloso CA, Volpe CM, Fernandes JS, Borges EA, Isoni CA, Dos Anjos PM, Nogueira-Machado JA
Malondialdehyde (MDA), an end product of lipid peroxidation and biomarker for oxidative stress, and its soluble receptor (sRAGE) were evaluated in 42 patients with type 1 diabetes mellitus, but without chronic complications, during the early years after diagnosis (0-10 years) and through the further progression of the disease (10-20 and > 20 years after diagnosis). Clinical and biochemical parameters of the cohort of diabetic patients were compared with those determined in 24 healthy individuals. The median levels of MDA in plasma were similar in type 1 diabetes patients and in healthy subjects. In contrast, statistically significant increases were detected in the median values of sRAGE in patients with type 1 diabetes compared with healthy subjects (2423.75 versus 1472.75 pg/ml; p=0.001, Mann-Whitney test). However, no significant between-group differences (p>0.05) were observed in levels of sRAGE when diabetic patients were grouped according to time elapsed after diagnosis. It is concluded that increased plasma levels of sRAGE in type 1 diabetes may provide protection against cell damage and may be sufficient to eliminate excessive circulating MDA during early years after disease onset.
PMID: 22337892 [PubMed - indexed for MEDLINE]
Urinary angiotensinogen as a novel early biomarker of intrarenal renin-angiotensin system activation in experimental type 1 diabetes.
J Pharmacol Sci. 2012 Aug 18;119(4):314-23
Authors: Kamiyama M, Zsombok A, Kobori H
Urinary excretion of albumin (UAlb) is used clinically as a marker of diabetic nephropathy (DN). Although DN was thought to be a unidirectional process, recent studies demonstrated that a large proportion of patients diagnosed with DN reverted to normoalbuminuria. Moreover, despite the normoalbuminuria, one-third of them exhibited reduced renal function even during the microalbuminuric stage. This study was performed to investigate whether urinary angiotensinogen (UAGT) level may serve as a useful marker of the early stage of experimental type 1 diabetes (T1DM). T1DM was induced by a single intraperitoneal injection of streptozotocin. Control mice were injected with citrate buffer. Two days after streptozotocin injection, half of the mice received continuous insulin treatment. Our data showed that UAlb excretion was increased 6 days after streptozotocin injection compared to controls, whereas UAGT excretion was increased at an earlier time point. These increases were reversed by insulin treatment. The UAGT to UAlb ratio was increased in diabetic mice compared to control mice. Furthermore, the increased AGT expression in the kidneys was observed in diabetic mice. These data suggest that UAGT might be useful as a novel early biomarker of activation of the renin-angiotensin system in experimental type 1 diabetes.
PMID: 22850612 [PubMed - indexed for MEDLINE]
Urinary proteomics for early diagnosis in diabetic nephropathy.
Diabetes. 2012 Dec;61(12):3304-13
Authors: Zürbig P, Jerums G, Hovind P, Macisaac RJ, Mischak H, Nielsen SE, Panagiotopoulos S, Persson F, Rossing P
Diabetic nephropathy (DN) is a progressive kidney disease, a well-known complication of long-standing diabetes. DN is the most frequent reason for dialysis in many Western countries. Early detection may enable development of specific drugs and early initiation of therapy, thereby postponing/preventing the need for renal replacement therapy. We evaluated urinary proteome analysis as a tool for prediction of DN. Capillary electrophoresis-coupled mass spectrometry was used to profile the low-molecular weight proteome in urine. We examined urine samples from a longitudinal cohort of type 1 and 2 diabetic patients (n = 35) using a previously generated chronic kidney disease (CKD) biomarker classifier to assess peptides of collected urines for signs of DN. The application of this classifier to samples of normoalbuminuric subjects up to 5 years prior to development of macroalbuminuria enabled early detection of subsequent progression to macroalbuminuria (area under the curve [AUC] 0.93) compared with urinary albumin routinely used to determine the diagnosis (AUC 0.67). Statistical analysis of each urinary CKD biomarker depicted its regulation with respect to diagnosis of DN over time. Collagen fragments were prominent biomarkers 3-5 years before onset of macroalbuminuria. Before albumin excretion starts to increase, there is a decrease in collagen fragments. Urinary proteomics enables noninvasive assessment of DN risk at an early stage via determination of specific collagen fragments.
PMID: 22872235 [PubMed - indexed for MEDLINE]