Resumo:
En
|
Texto:
En
|
PDF:
En
ABSTRACT Objectives To evaluate the accuracy of routinely available parameters in screening for GCK maturity-onset diabetes of the young (MODY), leveraging data from two large cohorts – one of patients with GCK-MODY and the other of patients with type 1 diabetes (T1D). Materials and methods The study included 2,687 patients with T1D, 202 patients with clinical features of MODY but without associated genetic variants (NoVar), and 100 patients with GCK-MODY (GCK). Area under the receiver-operating characteristic curve (ROC-AUC) analyses were used to assess the performance of each parameter – both alone and incorporated into regression models – in discriminating between groups. Results The best parameter discriminating between GCK-MODY and T1D was a multivariable model comprising glycated hemoglobin (HbA1c), fasting plasma glucose, age at diagnosis, hypertension, microvascular complications, previous diabetic ketoacidosis, and family history of diabetes. This model had a ROC-AUC value of 0.980 (95% confidence interval [CI] 0.974-0.985) and positive (PPV) and negative (NPV) predictive values of 43.74% and 100%, respectively. The best model discriminating between GCK and NoVar included HbA1c, age at diagnosis, hypertension, and triglycerides and had a ROC-AUC value of 0.850 (95% CI 0.783-0.916), PPV of 88.36%, and NPV of 97.7%; however, this model was not significantly different from the others. A novel GCK variant was also described in one individual with MODY (7-44192948-T-C, p.Ser54Gly), which showed evidence of pathogenicity on in silico prediction tools. Conclusions This study identified a highly accurate (98%) composite model for differentiating GCK-MODY and T1D. This model may help clinicians select patients for genetic evaluation of monogenic diabetes, enabling them to implement correct treatment without overusing limited resources. maturityonset maturity onset MODY, , (MODY) GCKMODY TD . T D (T1D) 2687 2 687 2,68 20 NoVar, (NoVar) 10 GCK. (GCK) receiveroperating receiver operating ROCAUC ROC AUC (ROC-AUC groups HbA1c HbAc HbA c (HbA1c) glucose diagnosis hypertension complications ketoacidosis 0980 0 980 0.98 95% 95 (95 [CI 0.9740.985 09740985 0.974 0.985 974 985 0.974-0.985 (PPV (NPV 4374 43 74 43.74 100% respectively 0850 850 0.85 0.7830.916, 07830916 0.783 0.916 783 916 0.783-0.916) 8836 88 36 88.36% 97.7% 977 97 7 however others 744192948TC, 744192948TC TC 44192948 C, C (7-44192948-T-C p.Ser54Gly, pSer54Gly pSerGly p.Ser54Gly p Ser54Gly Ser Gly p.Ser54Gly) tools 98% 98 (98% resources (MODY (T1D 268 68 2,6 (NoVar (GCK (HbA1c 098 0.9 9 (9 9740 0.9740.98 0974098 0974 0.97 0985 0.974-0.98 437 4 43.7 085 85 0.8 7830 0.7830.916 0783091 0783 0.78 0916 0.91 78 91 0.783-0.916 883 8 3 88.36 97.7 4419294 pSer SerGly (98 26 6 2, 09 0. ( 0.9740.9 097409 097 0.974-0.9 43. 08 0.7830.91 078309 078 0.7 091 0.783-0.91 88.3 97. 441929 0.9740. 09740 0.974-0. 0.7830.9 07830 07 0.783-0.9 88. 44192 0.9740 0.974-0 0.7830. 0.783-0. 4419 0.974- 0.7830 0.783-0 441 0.783- 44