scholarly journals Risk Factors for Kidney Disease in Type 1 Diabetes

Diabetes Care ◽  
2019 ◽  
Vol 42 (5) ◽  
pp. 883-890 ◽  
Author(s):  
Bruce A. Perkins ◽  
Ionut Bebu ◽  
Ian H. de Boer ◽  
Mark Molitch ◽  
William Tamborlane ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 1 Diabetes ◽  
Kidney Disease

scholarly journals Presence and Determinants of Cardiovascular Disease and Mortality in Individuals With Type 1 Diabetes of Long Duration: The FinnDiane 50 Years of Diabetes Study

2021 ◽  
Author(s):  
Valma Harjutsalo ◽  
Drazenka Pongrac Barlovic ◽  
Daniel Gordin ◽  
Carol Forsblom ◽  
George King ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Type 1 Diabetes ◽  
Kidney Disease ◽  
Cumulative Incidence ◽  
Diabetes Duration ◽  
Baseline Visit ◽  
Background Population ◽  
Long Duration

<b>Objective</b> <p>The aim of this study was to determine the incidence of cardiovascular disease (CVD) and mortality as well as their risk factors in type 1 diabetes (T1D) with more than 50-years duration. </p> <p><b>Methods</b><b></b></p> <p>From 5,396 individuals included in the Finnish Diabetic Nephropathy Study, 729 diagnosed in 1967 or earlier survived with T1D for more than 50 years. In this FinnDiane 50-year cohort, cumulative incidence of CVD events was assessed from the diagnosis of diabetes, and the excess CVD risk, compared to matched 12,710 individuals without diabetes, was calculated by Fine and Gray’s method. In addition, at the baseline visit (median duration of diabetes of 39 years) risk factors for different types of CVD (both non-fatal and fatal) and mortality were analyzed and cause-specific hazard ratios were estimated during a median follow-up of 16.6 years from the baseline visit. </p> <p><b>Results</b> </p> <p>In individuals with diabetes duration of more than 50 years, the 60-year cumulative incidence of CVD from the diagnosis of diabetes was 64.3% (62.5-66.0). Compared to individuals without diabetes, the standardized incidence ratio for CVD was 7.4 (6.5-8.3) and was in persons with normoalbuminuria 4.9 (4.0-5.9). Mean HbA<sub>1c</sub> and HbA<sub>1c</sub> variability, dyslipidemia, BMI, kidney disease, age and diabetes duration were the variables associated with incident CVD. In particular, HbA<sub>1c </sub>was associated with peripheral artery disease (PAD). Standardized mortality ratio compared with the Finnish background population was 3.2 (2.8-3.7). The factors, associated with mortality were diabetes duration, increased HbA<sub>1c</sub> variability, inflammation, insulin resistance, kidney disease and PAD.</p> <p><b> </b></p> <p><b>Conclusions</b></p> <p>Individuals with T1D of very long duration are at a high risk of CVD. In addition, throughout the lifespan, optimal glycemic control remains central to CVD and excess mortality prevention. </p>

Elevated Urinary VEGF-A,Transferrin, and Angiotensinogen Levels in Normoalbuminuric Children and Adolescents with Type 1 Diabetes; Can They Be Early Markers of Diabetic Kidney Disease?

2021 ◽  
Author(s):  
Aydilek Dağdeviren Çakır ◽  
Seha Kamil Saygılı ◽  
Nur Canpolat ◽  
Dildar Konukoğlu ◽  
Hande Turan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 1 Diabetes ◽  
Kidney Disease ◽  
Children And Adolescents ◽  
Diabetic Kidney Disease ◽  
Disease Duration ◽  
Healthy Children ◽  
Diabetic Kidney ◽  
The Mean

Objective: We hypothesized that diabetic kidney disease (DKD) begins early, before albuminuria occurs. We therefore aimed to assess potential early urinary biomarkers of (DKD) in normoalbuminuric and normotensive children and adolescents with Type 1 Diabetes (T1D) to evaluate the relationship between these markers and clinical and laboratory risk factors for DKD. Methods: This cross-sectional study included 75 children and adolescents with T1D (62% females, mean age 13.9 ± 3.2 years) with normoalbuminuria [an albumin/creatinine ratio (ACR) below 30 mg/g creatinine]. Fifty-five age- and sex-matched healthy children and adolescents served as controls. For the assessment of early DKD, urinary levels of angiotensinogen (AGT), transferrin, nephrin, vascular endothelial growth factor-A (VEGF-A), and kidney injury molecule-1 (KIM-1) were measured in adequately collected 24-h urine samples using enzyme-linked immunoassays. Results: The mean disease duration was 7.3± 3.2 (ranged 2.1 - 15.7) years and the mean HbA1c level was 8.8±1.4%. The median levels of urine VEGF-A/Cr, AGT/Cr, and Transferrin/Cr were significantly higher in normoalbuminuric patients with T1D, compared with those of controls (p<0.001, p=0.02, and p=0.001, respectively), but there was no difference in nephrin/Cr and KIM-1/Cr between the two groups. Although, none of the patients had albuminuria, the median level of urine ACR was significantly higher in the patient group than the control group (p=0.003). The ACR was positively correlated with glomerular filtration rate (GFR). Urinary transferrin/Cr, AGT/Cr, and VEGF-A/Cr were significantly correlated with ACR, but not with either GFR or diabetic risk factors including HbA1c or disease duration. Conclusion: Normoalbuminuric and normotensive children and adolescents with T1D have elevated urinary VEGF, AGT and transferrin levels, which may indicate the development of DKD before albuminuria occurs.

scholarly journals Presence and Determinants of Cardiovascular Disease and Mortality in Individuals With Type 1 Diabetes of Long Duration: The FinnDiane 50 Years of Diabetes Study

2021 ◽  
Author(s):  
Valma Harjutsalo ◽  
Drazenka Pongrac Barlovic ◽  
Daniel Gordin ◽  
Carol Forsblom ◽  
George King ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Type 1 Diabetes ◽  
Kidney Disease ◽  
Cumulative Incidence ◽  
Diabetes Duration ◽  
Baseline Visit ◽  
Background Population ◽  
Long Duration

<b>Objective</b> <p>The aim of this study was to determine the incidence of cardiovascular disease (CVD) and mortality as well as their risk factors in type 1 diabetes (T1D) with more than 50-years duration. </p> <p><b>Methods</b><b></b></p> <p>From 5,396 individuals included in the Finnish Diabetic Nephropathy Study, 729 diagnosed in 1967 or earlier survived with T1D for more than 50 years. In this FinnDiane 50-year cohort, cumulative incidence of CVD events was assessed from the diagnosis of diabetes, and the excess CVD risk, compared to matched 12,710 individuals without diabetes, was calculated by Fine and Gray’s method. In addition, at the baseline visit (median duration of diabetes of 39 years) risk factors for different types of CVD (both non-fatal and fatal) and mortality were analyzed and cause-specific hazard ratios were estimated during a median follow-up of 16.6 years from the baseline visit. </p> <p><b>Results</b> </p> <p>In individuals with diabetes duration of more than 50 years, the 60-year cumulative incidence of CVD from the diagnosis of diabetes was 64.3% (62.5-66.0). Compared to individuals without diabetes, the standardized incidence ratio for CVD was 7.4 (6.5-8.3) and was in persons with normoalbuminuria 4.9 (4.0-5.9). Mean HbA<sub>1c</sub> and HbA<sub>1c</sub> variability, dyslipidemia, BMI, kidney disease, age and diabetes duration were the variables associated with incident CVD. In particular, HbA<sub>1c </sub>was associated with peripheral artery disease (PAD). Standardized mortality ratio compared with the Finnish background population was 3.2 (2.8-3.7). The factors, associated with mortality were diabetes duration, increased HbA<sub>1c</sub> variability, inflammation, insulin resistance, kidney disease and PAD.</p> <p><b> </b></p> <p><b>Conclusions</b></p> <p>Individuals with T1D of very long duration are at a high risk of CVD. In addition, throughout the lifespan, optimal glycemic control remains central to CVD and excess mortality prevention. </p>

scholarly journals A Machine Learning Approach to Predict 5-year Risk for Complications in Type 1 Diabetes

2021 ◽  
Author(s):  
Naba Al-Sari ◽  
Svetlana Kutuzova ◽  
Tommi Suvitaival ◽  
Peter Henriksen ◽  
Flemming Pociot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Type 1 Diabetes ◽  
Diabetic Retinopathy ◽  
Kidney Disease ◽  
Diabetic Kidney Disease ◽  
Clinical Risk Factors ◽  
Diabetic Kidney ◽  
Clinical Risk

OBJECTIVE: Our aim was to apply state-of-the-art machine learning algorithms to predict the risk of future progression to diabetes complications, including diabetic kidney disease (≥30% decline in eGFR) and diabetic retinopathy (mild, moderate or severe). RESEARCH DESIGN AND METHODS: Using data in a cohort of 537 adults with type 1 diabetes we predicted diabetes complications emerging during a median follow-up of 5.4 years. Prediction models were computed first with clinical risk factors at baseline (17 measures) and then with clinical risk factors and blood-derived metabolomics and lipidomics data (965 molecular features) at baseline. Participants were first classified into two groups: type 1 diabetes stable (n=195) or type 1 diabetes with progression to diabetes complications (n=190). Furthermore, progression of diabetic kidney disease (≥30% decline in eGFR; n=79) and diabetic retinopathy (mild, moderate or severe; n=111) were predicted in two complication-specific models. Models were compared by 5-fold cross-validated area under the receiver operating characteristic (AUROC) curves. The Shapley additive explanations algorithm was used for feature selection and for interpreting the models. Accuracy, precision, recall, and F-score were used to evaluate clinical utility. RESULTS: During a median follow-up of 5.4 years, 79 (21 %) of the participants (mean+-SD: age 54.8 +- 13.7 years) progressed in diabetic kidney disease and 111 (29 %) of the participants progressed to diabetic retinopathy. The predictive models for diabetic kidney disease progression were highly accurate with clinical risk factors: the accuracy of 0.95 and AUROC of 0.92 (95% CI 0.857;0.995) was achieved, further improved to the accuracy of 0.98 and AUROC of 0.99 (95% CI 0.876;0.997) when omics-based predictors were included. The predictive panel composition was: albuminuria, retinopathy, estimated glomerular filtration rate, hemoglobin A1c, and six metabolites (five identified as ribitol, ribonic acid, myo-inositol, 2,4- and 3,4-dihydroxybutanoic acids). Models for diabetic retinopathy progression were less predictive with clinical risk predictors at, AUROC of 0.81 (95% CI 0.754;0.958) and with omics included at AUROC of 0.87 (95% CI 0.781;0.996) curve. The final retinopathy-panel included: hemoglobin A1c, albuminuria, mild degree of retinopathy, and seven metabolites, including one ceramide and the 3,4-dihydroxybutanoic acid). CONCLUSIONS: Here we demonstrate the application of machine learning to effectively predict five-year progression of complications, in particular diabetic kidney disease, using a panel of known clinical risk factors in combination with blood small molecules. Further replication of this machine learning tool in a real-world context or a clinical trial will facilitate its implementation in the clinic.

scholarly journals Risk factors for diabetic kidney disease in adults with longstanding type 1 diabetes: results from the Canadian Study of Longevity in Diabetes

Renal Failure ◽  
2019 ◽  
Vol 41 (1) ◽  
pp. 427-433
Author(s):  
Nigar Sekercioglu ◽  
Leif Erik Lovblom ◽  
Petter Bjornstad ◽  
Julie A. Lovshin ◽  
Yuliya Lytvyn ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 1 Diabetes ◽  
Kidney Disease ◽  
Diabetic Kidney Disease ◽  
Canadian Study ◽  
Diabetic Kidney

scholarly journals Genetic risk factors affecting mitochondrial function are associated with kidney disease in people with Type 1 diabetes

2015 ◽  
Vol 32 (8) ◽  
pp. 1104-1109 ◽  
Author(s):  
E. J. Swan ◽  
R. M. Salem ◽  
N. Sandholm ◽  
L. Tarnow ◽  
P. Rossing ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 1 Diabetes ◽  
Kidney Disease ◽  
Mitochondrial Function ◽  
Genetic Risk ◽  
Genetic Risk Factors ◽  
Factors Affecting

Risk Factors for Adverse Kidney Disease Outcomes in Type 1 Diabetes (T1D)

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 25-LB
Author(s):  
JANET B. MCGILL ◽  
MENGDI WU ◽  
RODICA POP-BUSUI ◽  
KARA R. MIZOKAMI-STOUT ◽  
WILLIAM V. TAMBORLANE ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 1 Diabetes ◽  
Kidney Disease ◽  
Disease Outcomes

Combined analysis of perinatal and infant risk factors for type 1 diabetes

2014 ◽  
Vol 9 (S 01) ◽  
Author(s):  
R Chmiel ◽  
C Winkler ◽  
A Beyerlein ◽  
M Köhler ◽  
A Knopff ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 1 Diabetes ◽  
Combined Analysis
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