A nomogram for predicting the risk of new-onset albuminuria based on baseline urinary ACR, orosomucoid, and HbA1c in patients with type 2 diabetes.

OBJECTIVES: Numerous biomarkers have been shown to be associated with albuminuria. However, few of them are valuable separate predictors of albuminuria development. This study aimed to develop a model for predicting the short-term risk of new-onset albuminuria in normoalbuminuric patients with type 2 diabetes (T2D). METHODS: 213 patients with T2D who were normoalbuminuric at the baseline were enrolled in this study. Basal levels of clinical characteristics and renal biomarkers including urinary orosomucoid (alpha-1-acid-glycoprotein, UORM), neutrophil gelatinase-associated lipocalin, retinol-binding protein, alpha-1-microglobulin, transferrin, and albumin-to-creatinine ratio (ACR) were utilized to analyze the association with the short-term risk of new-onset albuminuria. RESULTS: 19.72% of normoalbuminuric subjects at baseline progressed to albuminuria over the 2-year follow-up period. Except for NGAL, the basal levels of the other five renal biomarkers were significantly associated with new-onset albuminuria risk in the univariate analysis. In the multivariate logistic regression analysis using Forward: LR method, a model incorporating UORM/Cr, ACR, and HbA1c was established. Comparatively, this model had a higher potential to predict new-onset albuminuria risk compared with the single use of renal markers. In the validation of this model performed by 5-fold cross-validation method, the accuracy of this model was 0.818 ± 0.008 in the training sets, 0.827 ± 0.062 in the test sets, indicating a good capability for assessing albuminuria risk. Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice. CONCLUSION: The combined analysis of UORM/Cr, ACR and HbA1c may be of potential value for predicting the short-term risk of new-onset albuminuria in such patients.

HbA1c variability in type 2 diabetes is associated with the occurrence of new-onset albuminuria within three years.

AIMS: To evaluate the association between HbA1c coefficient of variation (HbA1c-CV) and 3-year new-onset albuminuria risk. METHODS: A retrospective cohort study involving 716 normoalbuminuric type 2 diabetes patients was conducted between 2010 and 2014. HbA1c-CV was used to categorize patients into low, moderate or high variability groups. Multivariate logistic models were constructed and validated. Integrated discrimination (IDI) and net reclassification (NRI) improvement indices were used to quantify the added predictive value of HbA1c-CV. RESULTS: The mean age of our cohort was 56.1±12.9years with a baseline HbA1c of 8.3±1.3%. Over 3-years of follow-up, 35.2% (n=252) developed albuminuria. An incremental risk of albuminuria was observed with moderate (6.68-13.43%) and high (above 13.44%) HbA1c-CV categories demonstrating adjusted odds ratios of 1.63 (1.12-2.38) and 3.80 (2.10-6.97) for 3-year new-onset albuminuria, respectively. Including HbA1c-CV for 3-year new-onset albuminuria prediction improved model discrimination (IDI: 0.023, NRI: 0.293, p<0.05). The final model had a C-statistic of 0.760±0.018 on validation. CONCLUSION: HbA1c-CV improves 3-year prediction of new-onset albuminuria. Together with mean HbA1c, baseline urine albumin-to-creatinine ratio and presence of hypertension, accurate 3-year new-onset albuminuria prediction may be possible.