Potential Prognostic Role of Immune System Activation Marker Neopterin in Patients with Type 2 Diabetes.
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ABSTRACT:
SUBMITTER: Unuvar S
PROVIDER: S-EPMC6298474 | biostudies-other | 2018 Dec
REPOSITORIES: biostudies-other
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Project description:Studying immunity and immune function in ecology and evolution requires field studies, but there has been a dearth of non-invasive markers of immune activation available for studying large wild mammals. Recently, we analytically and biologically validated the measurement of urinary neopterin (NEO), a biomarker of cellular immune activation, in captive macaques. However, applying this to free-ranging settings is complicated by issues involving sample collection, processing, storage, and transport. Here, we collected urine samples from captive macaques and undertook experiments simulating common field issues. We tested the effects on urinary NEO sample measurements following: dirt and faecal contamination; storage at room temperature; differences in processing and long-term storage methods (freezing, lyophilising, blotting onto filter paper); and freeze-thaw cycles. Our results show that concentrations of urinary NEO are highly stable--they are not affected by soil or faecal contamination, can be collected on filter paper and stored for many months frozen or lyophilised with minimal effect, and are resistant to multiple 24 hr freeze-thaws. With the addition of a biocidal preservative, concentrations are even stable at room temperature for long periods. Urinary NEO is remarkably resilient, and is highly suitable for non-invasive field studies of cellular immune responses in wild large mammals.
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Project description:The Leishmania parasite resides and replicates within host macrophages during visceral leishmaniasis (VL). This study aimed to evaluate neopterin, a marker of macrophage activation, as possible pharmacodynamic biomarker to monitor VL treatment response and to predict long-term clinical relapse of VL. Following informed consent, 497 plasma samples were collected from East-African VL patients receiving a 28-day miltefosine monotherapy (48 patients) or 11-day combination therapy of miltefosine and liposomal amphotericin B (L-AMB, 48 patients). Neopterin was quantified with ELISA. Values are reported as median (inter-quartile range). Baseline neopterin concentrations were elevated in all VL patients at 98.8 (63.9-135) nmol/L compared to reported levels for healthy controls (<10 nmol/L). During the first treatment week, concentrations remained stable in monotherapy patients (p = 0.807), but decreased two-fold compared to baseline in the combination therapy patients (p < 0.01). In the combination therapy arm, neopterin concentrations increased significantly 1 day after L-AMB infusion compared to baseline for cured patients [137 (98.5-197) nmol/L, p < 0.01], but not for relapsing patients [84.4 (68.9-106) nmol/L, p = 0.96]. The neopterin parameter with the highest predictive power for VL relapse was a higher than 8% neopterin concentration increase between end of treatment and day 60 follow-up (ROC AUC 0.84), with a 93% sensitivity and 65% specificity. In conclusion, the identified neopterin parameter could be a potentially useful surrogate endpoint to identify patients in clinical trials at risk of relapse earlier during follow-up, possibly in a panel of biomarkers to increase its specificity.
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Project description:Dickkopf-1(DKK-1), the downstream target of ?-catenin/T-cell factor, participates in a negative feedback loop in the Wnt signaling and reported as an important biomarker in many tumors. In this study, we analyzed the expression of DKK-1 in pancreatic ductal adenocarcinoma (PDAC) patients at both mRNA and protein levels. We used real-time PCR to detect the expression of DKK-1 in 32 PDAC and paired adjacent non-tumor tissues, results suggested that the expression of DKK-1 was increased in PDAC tissues. We found the similar results in the analysis of 3 independent microarray data sets. Immunohistochemical staining of 311 pairs of PDAC tissues suggested that DKK-1 expression was significantly associated with T classification (P = 0.039) and lymph node metastasis (P = 0.035). Furthermore, Kaplan-Meier analysis for DKK-1 expression demonstrated that patients with higher DKK-1 level had shorter overall survival (OS) and relapse-free survival (RFS) time in Ren Ji cohort and online PDAC database at both mRNA and protein levels. Univariable and multivariable Cox regression analysis confirmed that DKK-1 as well as lymph node metastasis and histology were independent predictors of OS in patients with PDAC. This study demonstrated that DKK-1 may be a predictor for prognosis in PDAC patients.
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Project description:Immune analytes have been widely tested in efforts to understand the heterogeneity of disease progression, risk, and therapeutic responses in type 1 diabetes (T1D). The future clinical utility of such analytes as biomarkers depends on their technical and biological variability, as well as their correlation with clinical outcomes. To assess the variability of a panel of 91 immune analytes, we conducted a prospective study of adults with T1D (<3 years from diagnosis), at 9-10 visits over 1 year. Autoantibodies and frequencies of T-cell, natural killer cell, and myeloid subsets were evaluated; autoreactive T-cell frequencies and function were also measured. We calculated an intraclass correlation coefficient (ICC) for each marker, which is a relative measure of between- and within-subject variability. Of the 91 analytes tested, we identified 35 with high between- and low within-subject variability, indicating their potential ability to be used to stratify subjects. We also provide extensive data regarding technical variability for 64 of the 91 analytes. To pilot the concept that ICC can be used to identify analytes that reflect biological outcomes, the association between each immune analyte and C-peptide was also evaluated using partial least squares modeling. CD8 effector memory T-cell (CD8 EM) frequency exhibited a high ICC and a positive correlation with C-peptide, which was also seen in an independent dataset of recent-onset T1D subjects. More work is needed to better understand the mechanisms underlying this relationship. Here we find that there are a limited number of technically reproducible immune analytes that also have a high ICC. We propose the use of ICC to define within- and between-subject variability and measurement of technical variability for future biomarker identification studies. Employing such a method is critical for selection of analytes to be tested in the context of future clinical trials aiming to understand heterogeneity in disease progression and response to therapy.
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