scholarly journals Are zinc transporter type 8 antibodies a marker of autoimmune thyroiditis in non-obese adults with new-onset diabetes?

2014 ◽  
Vol 170 (4) ◽  
pp. 651-658 ◽  
Author(s):  
Anita Rogowicz-Frontczak ◽  
Dorota Zozulińska-Ziółkiewicz ◽  
Monika Litwinowicz ◽  
Paweł Niedźwiecki ◽  
Krystyna Wyka ◽  
...  
Keyword(s):  
Autoimmune Thyroiditis ◽  
Autoimmune Diabetes ◽  
Zinc Transporter ◽  
Islet Autoantibodies ◽  
Thyroid Peroxidase ◽  
Diabetic Patients ◽  
Obese Adults ◽  
Thyroid Peroxidase Antibodies ◽  
Onset Diabetes ◽  
New Onset

ObjectiveThe diagnosis of autoimmune diabetes in non-obese adults is based on the detection of glutamic acid decarboxylase autoantibodies (GADA), islet cell antibodies (ICA) and antibodies to tyrosine phosphatase (IA-2A). Zinc transporter 8 (ZnT8) has been identified as a new autoantigen in patients with type 1 diabetes mellitus. The coincidence of autoimmune thyroiditis (AITD) with diabetes is common; therefore, screening of TSH and thyroid peroxidase antibodies (ATPO) is recommended during the diagnosis of diabetes. In this study, we determined whether the occurrence of islet autoantibodies is associated with a positive titre of ATPO in newly diagnosed adult-onset autoimmune diabetic patients.Design and methodsThe study involved 80 non-obese adults aged 44 (interquartile range (IQR): 37–51) years with a BMI of 24.0 (IQR: 22.2–26.0) kg/m2and new-onset diabetes. The markers of autoimmune diabetes (GADA, ICA, IA-2A and ZnT8A), TSH and thyroid peroxidase antibodies (ATPO) were evaluated.ResultsIn the study population, 70% (n=56) of the subjects were positive for at least one of the four assessed markers of autoimmune diabetes (83.9% GADA, 62.5% ICA, 42.8% IA-2A and 33% ZnT8A) and 37.5% of the subjects were positive for ATPO. The ZnT8A-positive subjects had higher ATPO titres than the ZnT8A-negative subjects (172.7 (IQR: 0.36–410.4) vs 92.4 (IQR: 0–23.7) IU/ml,P=0.001). Based on the assessed islet autoantibodies, the occurrence of positive ZnT8A and GADA was found to be related to a positive titre of ATPO using logistic regression (OR=5.48, 95% CI: 1.65–18.14,P=0.006 and OR=3.42, 95% CI: 1.09–10.71,P=0.03 respectively).ConclusionsIn non-obese adults with new-onset diabetes, the presence of GADA and especially ZnT8 autoantibodies increases the risk of AITD.

scholarly journals Expression of Cytokines and Cytokine Receptors-Genes in Patients with Different Forms of Thyroid Pathology in Ukrainian Population

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Iryna Kamyshna ◽  
Aleksandr Kamyshnyi
Keyword(s):  
Autoimmune Thyroiditis ◽  
Interleukin 6 ◽  
Interleukin 1 ◽  
White Blood Cells ◽  
Elevated Serum ◽  
Interleukin 10 ◽  
Thyroid Peroxidase ◽  
Thyroid Peroxidase Antibodies ◽  
Interleukin 6 Receptor ◽  
Peripheral White Blood Cells

Abstract Multiple susceptibility genes can be involved in the development of Hashimoto’s thyroiditis. Some of these genes are implicated in other autoimmune diseases, while others are specific to thyroid autoimmune response. 153 patients with thyroid pathology were enrolled in the study (152 women and 1 man, the average age was 46,02±14,3). They were divided into 3 groups: 16 patients with postoperative hypothyroidism; 65 patients with hypothyroidism resulting from autoimmune thyroiditis, and 72 patients with both AIT and elevated serum an anti-thyroglobulin and anti-thyroid peroxidase antibodies. We used a pathway-specific real-time Polymerase chain reaction array to identify and verify cytokines and receptor pathway-associated gene expression in peripheral white blood cells in randomly selected 12 individuals from each group. In the patients with postoperative hypothyroidism and those with hypothyroidism resulting from autoimmune thyroiditis, the expression of Chemokine (C-X3-C motif) receptor 1, Chemokine (C-X-C motif) receptor 4, Interleukin 6, and Interleukin 6 receptor significantly decreased, while the expression of IL6ST and IL10RA increased. In contrast, mRNA levels of Chemokine (C-X3-C motif) receptor 1, Chemokine (C-X-C motif) receptor 4, Interleukin 6, and Interleukin 6 receptor increased in the autoimmune thyroiditis patients with elevated serum anti-thyroglobulin and anti-thyroid peroxidase antibodies, while the expression of Interleukin 6 signal transducer and Interleukin 10 receptor, alpha decreased in this group of patients. The patients with hypothyroidism resulting from autoimmune thyroiditis and patients with elevated serum anti-thyroglobulin and anti-thyroid peroxidase antibodies had significantly lowered expression of Interleukin 10, while the expression of Interleukin 1, beta and Interleukin 1 receptor, type I was elevated. autoimmune thyroiditis and hypothyroidism affect the mRNA-level expression of cytokines and cytokine receptor genes in a gene-specific manner, and these changes to gene expression can be among the triggers of autoimmune inflammation progression in the thyroid gland. Transcriptional activity of cytokines, inducer, and receptor genes in the peripheral white blood cells can be used as an important minimally invasive prognostic marker of the autoimmune thyroid disease severity.

scholarly journals Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks

2019 ◽  
Vol 60 (2) ◽  
pp. 191 ◽  
Author(s):  
Byoung Geol Choi ◽  
Seung-Woon Rha ◽  
Suhng Wook Kim ◽  
Jun Hyuk Kang ◽  
Ji Young Park ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Diabetic Patients ◽  
Cardiovascular Risks ◽  
Onset Diabetes ◽  
New Onset Diabetes Mellitus ◽  
New Onset

scholarly journals Thyreotropin levels in diabetic patients on metformin treatment

2012 ◽  
Vol 167 (2) ◽  
pp. 261-265 ◽  
Author(s):  
Carlo Cappelli ◽  
Mario Rotondi ◽  
Ilenia Pirola ◽  
Barbara Agosti ◽  
Annamaria Formenti ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Treatment Group ◽  
Normal Serum ◽  
Thyroid Peroxidase ◽  
Diabetic Patients ◽  
Metformin Treatment ◽  
Lowering Effect ◽  
Thyroid Peroxidase Antibodies ◽  
Serum Tsh ◽  
Tsh Levels

ObjectiveA retrospective study to evaluate the changes in TSH concentrations in diabetic patients treated or not treated with metformin and/or l-thyroxine (l-T4).MethodsThree hundred and ninety three euthyroid diabetic patients were divided into three groups on the basis of metformin and/or l-T4 treatment: Group (M−/L−), 119 subjects never treated with metformin and l-T4; Group (M+/L−), 203 subjects who started metformin treatment at recruitment; and Group (M+/L+), 71 patients on l-T4 who started metformin recruitment.ResultsThe effect of metformin on serum TSH concentrations was analyzed in relation to the basal value of TSH (below 2.5 mIU/l (Q1) or between 2.51 and 4.5 mIU/l (Q2)). In patients of group M+/L+, TSH significantly decreased independently from the basal level (Q1, from 1.45±0.53 to 1.01±1.12 mU/l (P=0.037); Q2, from 3.60±0.53 to 1.91±0.89 mU/l (P<0.0001)). In M+/L− group, the decrease in TSH was significant only in those patients with a basal high-normal serum TSH (Q2: from 3.24±0.51 to 2.27±1.28 mU/l (P=0.004)); in M−/L− patients, no significant changes in TSH levels were observed. In patients of group M+/L− showing high-normal basal TSH levels, a significant decrease in TSH was observed independently from the presence or absence of thyroid peroxidase antibodies (AbTPO; Q2 AbTPO +: from 3.38±0.48 to 1.87±1.08 mU/l (P<0.001); Q2 AbTPO −: from 3.21±0.52 to 2.34±1.31 mU/l (P<0.001)).ConclusionsThese data strengthen the known TSH-lowering effect of metformin in diabetic patients on l-T4 treatment and shows a significant reduction of TSH also in euthyroid patients with higher baseline TSH levels independently from the presence of AbTPO.

scholarly journals Vesetranszplantáció utáni szénhidrátanyagcsere-változások és annak hatásai a cardiovascularis rizikóra

2017 ◽  
Vol 158 (38) ◽  
pp. 1512-1516
Author(s):  
Bernadett Borda ◽  
Edit Szederkényi ◽  
Zoltán Hódi ◽  
Aurél Ottlakán ◽  
Viktor Szabó ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Cardiovascular Risk ◽  
Baseline Data ◽  
Diabetic Patients ◽  
Ischemic Time ◽  
Heart Score ◽  
Onset Diabetes ◽  
Significant Difference ◽  
New Onset Diabetes Mellitus ◽  
New Onset

Abstract: Introduction: Cardiovascular disease is the major cause of deaths after transplantation, with diabetes mellitus being the main risk factor in development. Aim: The aim of our study was to assess the prevalence of new onset diabetes mellitus in connection with the cardiovascular risk predicted by the HEART Score. Method: 44 patients were involved in our study; after overview of baseline data, OGTT was performed, followed by patient classification into the following groups: normal, impaired fasting glucose/impaired glucose tolerance, and new onset diabetes mellitus. Insulin resistance and kidney function were also assessed. Results: Concerning baseline data, cold ischemic time (p = 0.016), body weight (p = 0.035), BMI (p = 0.025), and HbA1C (p = 0.0024) proved to be significantly different between normal and diabetic patients. Significant difference was found based on HOMA IR between the two groups 1.69±0.51 vs 6.46±1.42; p = 0.0017). Based on the HEART Score, patients with new onset diabetes mellitus were put into Group 3, which also reflects the risk which diabetes carries for the development of cardiovascular diseases. Conclusion: Cardiovascular risk can be decreased with increased allograft survival by early diagnosis and management of diabetes. Orv Hetil. 2017; 158(38): 1512–1516.

scholarly journals Data-Driven identification of temporal glucose patterns in a large cohort of Non-Diabetic patients with COVID-19 using time series clustering

JAMIA Open ◽  
2021 ◽  
Author(s):  
Sejal Mistry ◽  
Ramkiran Gouripeddi ◽  
Julio C Facelli
Keyword(s):  
Time Series ◽  
Blood Glucose ◽  
Low Complexity ◽  
Data Driven ◽  
Diabetic Patients ◽  
Time Series Clustering ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Onset Diabetes ◽  
New Onset

Abstract Objective Hyperglycemia has emerged as an important clinical manifestation of coronavirus disease 2019 (COVID-19) in diabetic and non-diabetic patients. Whether these glycemic changes are specific to a subgroup of patients and persist following COVID-19 resolution remains to be elucidated. This work aimed to characterize longitudinal random blood glucose in a large cohort of non-diabetic patients diagnosed with COVID-19. Materials and Methods De-identified electronic medical records of 7,502 patients diagnosed with COVID-19 without prior diagnosis of diabetes between January 1st and November 18th, 2020 were accessed through the TriNetX Research Network. Glucose measurements, diagnostic codes, medication codes, laboratory values, vital signs, and demographics were extracted before, during, and after COVID-19 diagnosis. Unsupervised time-series clustering algorithms were trained to identify distinct clusters of glucose trajectories. Cluster associations were tested for demographic variables, COVID-19 severity, glucose-altering medications, glucose values, and new-onset diabetes diagnoses. Results Time-series clustering identified a low-complexity model with 3 clusters and a high-complexity model with 19 clusters as the best-performing models. In both models, cluster membership differed significantly by death status, COVID-19 severity, and glucose levels. Clusters membership in the 19 cluster model also differed significantly by age, sex, and new-onset diabetes mellitus. Discussion and Conclusion This work identified distinct longitudinal blood glucose changes associated with subclinical glucose dysfunction in the low-complexity model and increased new-onset-diabetes incidence in the high complexity model. Together, these findings highlight the utility of data-driven techniques to elucidate longitudinal glycemic dysfunction in patients with COVID-19 and provide clinical evidence for further evaluation of the role of COVID-19 in diabetes pathogenesis. Lay Summary Hyperglycemia is defined as elevated blood glucose measurements and are common in diabetic patients. Recent findings suggest that patients diagnosed with COVID-19 may experience elevated blood glucose levels during COVID-19 infection. Whether blood glucose levels remain elevated after COVID-19 infection remains poorly understood. This study aimed to identify how patterns of blood glucose levels before, during, and after COVID-19 change. This work analyzed blood glucose levels from 2,059 patients diagnosed with COVID-19 and used machine learning to identify different patterns of blood glucose changes. We found patterns demonstrating an overall increase, overall decrease, temporal increase, and temporary decrease in blood glucose levels. Some of these patterns were associated with COVID-19 severity and proportion of patients with new-onset diabetes. These findings demonstrate the usefulness of machine learning in understanding glucose changes following COVID-19 diagnosis and indicate that more research is needed to understand if blood glucose monitoring in COVID-19 patients should be routinely performed.

scholarly journals Rosuvastatin and Atorvastatin: Comparative Effects on Glucose Metabolism in Non-Diabetic Patients with Dyslipidaemia

2012 ◽  
Vol 5 ◽  
pp. CMED.S7591 ◽  
Author(s):  
Ahmed Abbas ◽  
John Milles ◽  
Sudarshan Ramachandran
Keyword(s):  
Glucose Metabolism ◽  
Glycaemic Control ◽  
Cardiovascular Prevention ◽  
Diabetic Patients ◽  
Onset Diabetes ◽  
Prevention Guidelines ◽  
The Individual ◽  
Meta Analyses ◽  
Overall Mortality ◽  
New Onset

The ever increasing interventional CVD outcome studies have resulted in statins being an essential factor of cardiovascular prevention strategies. The JUPITER study in 2008, despite reducing CVD and overall mortality, highlighted an increase in new onset diabetes in the rosuvastatin treated arm. Since then there have been many meta-analyses of the RCTs and the largest carried out by Sattar et al showed a significant increase in the incidence of diabetes during the trials. The findings from the individual studies when comparing the different statins were less clear. A higher statin dosage and risk factors associated with diabetes appeared to predict this phenomenon. There have been many studies investigating the effects of statins on glycaemic control, but again no clear conclusion is apparent. Despite the increase in new onset diabetes observed, the risk is clearly out-weighed by the CVD benefits observed in nearly all the statin trials. Thus, no change is required to any of the prevention guidelines regarding statins. However, it may be prudent to monitor glycaemic control after commencing statin therapy. This review will focus on atorvastatin which is the most widely used statin worldwide and rosuvastatin which is the most efficacious. This will be against a background of the effects of other statins on glucose metabolism in non-diabetic patients.

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