Predicting diabetic kidney disease in youth with type 1 diabetes: Insights from genetic risk assessment".

Diabetic kidney disease (DKD) is influenced by multiple factors, yet its precise progression mechanisms remain largely unclear. This study aimed to create a clinical risk-scoring system based on genetic polymorphisms in the AFF3, CARS, CERS2, ERBB4, GLRA3, RAET1L, TMPO, and ZMIZ1 genes. The study included a DKD group diagnosed with diabetic kidney disease before age 18 and a WDC group matched by age, gender, and age at diabetes diagnosis. Genetic data and clinical data from diabetes diagnosis to moderately increased albuminuria (MIA) detection were compared between the groups. Among 43 DKD cases, 22 were girls and 21 were boys. At MIA diagnosis, mean body weight SDS was -0.24 ± 0.94, height SDS was 0.34 ± 1.15, and BMI SDS was -0.26 ± 0.94. Systolic blood pressure was at the 72nd percentile (2-99), and diastolic blood pressure was at the 74th percentile (33-99). Significant differences in rs267734, rs267738, and rs942263 polymorphisms were found between DKD and non-complication diabetic groups (13[30.2 %] vs 5[11.6 %], p = 0.034; 14[32.6 %] vs 5[11.6 %], p = 0.019; 26[60.5 %] vs 40[93 %], p < 0.001). Several factors were identified as significant in DKD onset, including low follow-up weight SDS, elevated diastolic blood pressure, presence of rs267734, and absence of rs942263 polymorphisms. The model demonstrated a specificity of 81.4 % and a sensitivity of 74.4 %. Copyright © 2024 Elsevier Inc. All rights reserved.

Citation

Ferda Evin, Tarık Kırkgöz, Tahir Atik, Güneş Ak, Timur Köse, Caner Kabasakal, Behzat Özkan, Samim Özen, Şükran Darcan, Damla Gökşen. Predicting diabetic kidney disease in youth with type 1 diabetes: Insights from genetic risk assessment". Journal of diabetes and its complications. 2024 Nov;38(11):108833

Mesh Tags

PMID: 39293150

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