Plasma levels of tissue inhibitor of metalloproteinase-1 in patients with type 1 diabetes mellitus associate with early diabetic neuropathy and nephropathy

Papadopoulou-Marketou, Nektaria; Whiss, Per A; Eriksson, Anna; Hyllienmark, Lars; Papassotiriou, Ioannis; Wahlberg, Jeanette

doi:10.1177/14791641211002470

Cited by 10 publications

(7 citation statements)

References 31 publications

(54 reference statements)

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“…Identifying risk factors (besides poor metabolic control) before complications from diabetes occur could also help doctors and nurses working with children and adolescents with type 1 diabetes to improve and personalise their treatment. Previous studies have suggested the association of circulating proteins as biomarkers of vascular disease in both adults and children with type 1 diabetes [9,10,30]. The current study observed increased sCD163 in patients with recent-onset type 1 diabetes with levels correlating to HbA1c.…”

Section: Discussionsupporting

confidence: 66%

“…Among other risk factors are high blood pressure and cholesterol levels [1]. Recently, studies have suggested soluble biomarkers which may identify the increased risk of developing diabetesrelated complications in adults [9] and young patients with type 1 diabetes [10]. These studies have found several markers correlating with risk for vascular complications in type 1 diabetes.…”

Section: Laboratory Methodsmentioning

confidence: 99%

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HbA1c levels and circulating inflammatory proteins at onset of type 1 diabetes in children and adolescents

Dereke

Nilsson

2022

J Diabetes Metab Disord

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Purpose Type 1 diabetes is an autoimmune disease that often develops during childhood. Complications such as retinopathy often occur during the course of the disease. Studies to identify possible predictors of complications in type 1 diabetes are needed; in particular markers able to identify risk of complications long before they occur. The first aim of this study was to investigate plasma levels of sCD163, sST2 and Gal-3 at diagnosis of type 1 diabetes in children and adolescents. The second aim was to study their correlation to HbA1c in this study cohort. Methods Patients (n = 242, 0–18 years) with type 1 diabetes, at Helsingborg’s Hospital were included in this study and circulating levels of sCD163, sST2 and Gal-3 were investigated in plasma using commercially available DuoSet ELISA and supplementary ancillary kit. Results Circulating sCD163 was significantly higher at diagnosis compared to after diagnosis (666 ± 318ng/ml and 505 ± 223ng/ml respectively; p < 0.001). Also sST2 was significantly higher (18.2 [12.7–25.6] ng/ml respectively 9.1 [6.3–13.5] ng/ml (p < 0.001), but Gal-3 levels did not differ from onset of diabetes to after diagnosis. HbA1c was shown to correlate to sCD163 (rs=0.36; p < 0.001), sST2 (rs=0.22; p = 0.016) and Gal-3 (rs=0.2; p = 0.020) in patients with a diabetes duration < 5 years. Conclusions sCD163 levels increased in patients with recent-onset type 1 diabetes and the levels increased with higher HbA1c. Patients included in this study will be followed annually until the eventual development of diabetic complications, while continuously studying circulating levels of inflammatory proteins such as sCD163.

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Section: Discussionsupporting

confidence: 66%

Section: Laboratory Methodsmentioning

confidence: 99%

HbA1c levels and circulating inflammatory proteins at onset of type 1 diabetes in children and adolescents

Dereke

Nilsson

2022

J Diabetes Metab Disord

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“…( 6 ). Type 1 diabetes (SAID in the current study) is known to be associated with an increased risk of cardiovascular disease and microvascular complications ( 16 , 17 ), the main mechanism of which has not been fully clarified, but it may be partly related to the high levels of metalloproteinase-9 and metalloproteinase-1 tissue inhibitors in the plasma and tissues of patients with type 1 diabetes ( 18 ). The SIRD group has the highest incidence of DKD, which is consistent with the finding of previous reports ( 8 , 19 ) and may be related to the high smoking rate and lipid metabolism disorder in patients with SIRD.…”

Section: Discussionmentioning

confidence: 92%

Diabetes duration and types of diabetes treatment in data-driven clusters of patients with diabetes

et al. 2022

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BackgroundThis study aimed to cluster patients with diabetes and explore the association between duration of diabetes and diabetes treatment choices in each cluster.MethodsA Two-Step cluster analysis was performed on 1332 Chinese patients with diabetes based on six parameters (glutamate decarboxylase antibodies, age at disease onset, body mass index, glycosylated hemoglobin, homeostatic model assessment 2 to estimate β-cell function and insulin resistance). Associations between the duration of diabetes and diabetes treatment choices in each cluster of patients were analyzed using Kaplan-Meier survival curves and logistic regression models.ResultsThe following five replicable clusters were identified: severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). There were significant differences in blood pressure, blood lipids, and diabetes-related complications among the clusters (all P < 0.05). Early in the course of disease (≤5 years), compared with the other subgroups, the SIRD, MOD, and MARD populations were more likely to receive non-insulin hypoglycemic agents for glycemic control. Among the non-insulin hypoglycemic drug options, SIRD had higher rates of receiving metformin, alpha-glucosidase inhibitor (AGI), and glucagon-like peptide-1 drug; the MOD and MARD groups both received metformin, AGI and sodium-glucose cotransporter 2 inhibitor (SGLT-2i) drug ratio was higher. While the SAID and SIDD groups were more inclined to receive insulin therapy than the other subgroups, with SAID being more pronounced. With prolonged disease course (>5 years), only the MOD group was able to accept non-insulin hypoglycemic drugs to control the blood sugar levels, and most of them are still treated with metformin, AGI, and SGLT-2i drugs. While the other four groups required insulin therapy, with SIDD being the most pronounced.ConclusionsClustering of patients with diabetes with a data-driven approach yields consistent results. Each diabetes cluster has significantly different disease characteristics and risk of diabetes complications. With the development of the disease course, each cluster receives different hypoglycemic treatments.

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“…TIMP1 belongs to the TIMP gene family, and the protein encoded by this gene family is a natural inhibitor of matrix metallopeptidase, which can regulate cell differentiation, migration, and cell death. It has been reported that plasma levels of TIMP1 are associated with early diabetic neuropathy and nephropathy in patients with type 1 diabetes ( 42 ). TPM1is a member of the highly conserved tropomyosin family, widely distributed actin-binding proteins that are involved in the contractile system of striated and smooth muscles and the cytoskeleton of non-muscle cells.…”

Section: Discussionmentioning

confidence: 99%

Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments

Ming

Zhu

et al. 2023

Front. Endocrinol.

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IntroductionDiabetic kidney disease (DKD) is a long-term complication of diabetes and causes renal microvascular disease. It is also one of the main causes of end-stage renal disease (ESRD), which has a complex pathophysiological process. Timely prevention and treatment are of great significance for delaying DKD. This study aimed to use bioinformatics analysis to find key diagnostic markers that could be possible therapeutic targets for DKD.MethodsWe downloaded DKD datasets from the Gene Expression Omnibus (GEO) database. Overexpression enrichment analysis (ORA) was used to explore the underlying biological processes in DKD. Algorithms such as WGCNA, LASSO, RF, and SVM_RFE were used to screen DKD diagnostic markers. The reliability and practicability of the the diagnostic model were evaluated by the calibration curve, ROC curve, and DCA curve. GSEA analysis and correlation analysis were used to explore the biological processes and significance of candidate markers. Finally, we constructed a mouse model of DKD and diabetes mellitus (DM), and we further verified the reliability of the markers through experiments such as PCR, immunohistochemistry, renal pathological staining, and ELISA.ResultsBiological processes, such as immune activation, T-cell activation, and cell adhesion were found to be enriched in DKD. Based on differentially expressed oxidative stress and inflammatory response-related genes (DEOIGs), we divided DKD patients into C1 and C2 subtypes. Four potential diagnostic markers for DKD, including tenascin C, peroxidasin, tissue inhibitor metalloproteinases 1, and tropomyosin (TNC, PXDN, TIMP1, and TPM1, respectively) were identified using multiple bioinformatics analyses. Further enrichment analysis found that four diagnostic markers were closely related to various immune cells and played an important role in the immune microenvironment of DKD. In addition, the results of the mouse experiment were consistent with the bioinformatics analysis, further confirming the reliability of the four markers.ConclusionIn conclusion, we identified four reliable and potential diagnostic markers through a comprehensive and systematic bioinformatics analysis and experimental validation, which could serve as potential therapeutic targets for DKD. We performed a preliminary examination of the biological processes involved in DKD pathogenesis and provide a novel idea for DKD diagnosis and treatment.

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Plasma levels of tissue inhibitor of metalloproteinase-1 in patients with type 1 diabetes mellitus associate with early diabetic neuropathy and nephropathy

Cited by 10 publications

References 31 publications

HbA1c levels and circulating inflammatory proteins at onset of type 1 diabetes in children and adolescents

HbA1c levels and circulating inflammatory proteins at onset of type 1 diabetes in children and adolescents

Diabetes duration and types of diabetes treatment in data-driven clusters of patients with diabetes

Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments

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