Objective. Although hypoalbuminemia is frequently found in most patients with diabetic nephropathy (DN), its relationship to the severity and progression of DN remains largely unknown. Our aim was to investigate the association between the serum albumin levels and clinicopathological features and renal outcomes in patients with type 2 diabetes mellitus (T2DM) and biopsy-proven DN. Materials and Methods. A total of 188 patients with T2DM and biopsy-proven DN followed up for at least one year were enrolled. The patients were divided into four groups based on the albumin levels: normal group: ≥35 g/L (n=87); mild group: 30-35 g/L (n=34); moderate group: 25-30 g/L (n=36); and severe group: <25 g/L (n=31). The renal outcome was defined by progression to end-stage renal disease. The impact of the serum albumin level on renal survival was estimated using Cox regression analysis. Results. Among the cases, the serum albumin level had a significant correlation with proteinuria, renal function, and glomerular lesions. A multivariate Cox regression analysis indicated that the severity of hypoalbuminemia remained significantly associated with an adverse renal outcome, independent of clinical and histopathological features. In reference to the normal group, the risk of progression to ESRD increased such that the hazard ratio (HR) for the mild group was 2.09 (95% CI, 0.67-6.56, p=0.205), 6.20 (95% CI, 1.95-19.76, p=0.002) for the moderate group, and 7.37 (95% CI, 1.24-43.83, p=0.028) for the severe group. Conclusions. These findings suggested that hypoalbuminemia was associated with a poorer renal prognosis in patients with T2DM and DN.
Aims Diabetic kidney disease (DKD) is the most common cause of end-stage renal disease (ESRD) and is associated with increased morbidity and mortality in patients with diabetes. Identification of risk factors involved in the progression of DKD to ESRD is expected to result in early detection and appropriate intervention and improve prognosis. Therefore, this study aimed to establish a risk prediction model for ESRD resulting from DKD in patients with type 2 diabetes mellitus (T2DM). Methods Between January 2008 and July 2019, a total of 390 Chinese patients with T2DM and DKD confirmed by percutaneous renal biopsy were enrolled and followed up for at least 1 year. Four machine learning algorithms (gradient boosting machine, support vector machine, logistic regression, and random forest (RF)) were used to identify the critical clinical and pathological features and to build a risk prediction model for ESRD. Results There were 158 renal outcome events (ESRD) (40.51%) during the 3-year median follow up. The RF algorithm showed the best performance at predicting progression to ESRD, showing the highest AUC (0.90) and ACC (82.65%). The RF algorithm identified five major factors: Cystatin-C, serum albumin (sAlb), hemoglobin (Hb), 24-hour urine urinary total protein, and estimated glomerular filtration rate. A nomogram according to the aforementioned five predictive factors was constructed to predict the incidence of ESRD. Conclusion Machine learning algorithms can efficiently predict the incident ESRD in DKD participants. Compared with the previous models, the importance of sAlb and Hb were highlighted in the current model. Highlights What is already known? Identification of risk factors for the progression of DKD to ESRD is expected to improve the prognosis by early detection and appropriate intervention. What this study has found? Machine learning algorithms were used to construct a risk prediction model of ESRD in patients with T2DM and DKD. The major predictive factors were found to be CysC, sAlb, Hb, eGFR, and UTP. What are the implications of the study? In contrast with the treatment of participants with early-phase T2DM with or without mild kidney damage, major emphasis should be placed on indicators of kidney function, nutrition, anemia, and proteinuria for participants with T2DM and advanced DKD to delay ESRD, rather than age, sex, and control of hypertension and glycemia.
Background: Red cell distribution width (RDW) has been reported to be involved in metabolic syndrome and cardiovascular events. Patients with diabetic nephropathy (DN) are often found to be with high level of RDW. The aim of this study was to explore whether RDW was associated with DN severity and progression in patients with type-2 diabetes mellitus (T2DM).Methods: A total of 175 T2DM patients with biopsy-proven DN were enrolled. The baseline clinical and pathologic data of these patients was extracted from the medical records. The patients then were divided into two groups based on the median (13.6%) of RDW level; group 1: <13.6% and group 2: ≥13.6%. The effect of RDW level on the renal outcomes was evaluated by using cox regression analysis.Results: Compared with the patients with lower RDW level, the patients with higher level of RDW had higher proportions of female, longer DM duration, lower levels of eGFR, albumin and hemoglobin, and more serious glomerular damage. Moreover, the RDW levels were negatively corrected with eGFR (r = −0.283, p < 0.001), but positively related with proteinuria (r = 0.227, p = 0.003). In the follow-up period, 81(46.3%) patients had reached ESRD from baseline. Importantly, the Cox regression analyses showed that the levels of RDM had a significant effect on the risk of progression to ESRD (HR = 1.92, p < 0.01), albeit not emerged as an independent predictor.Conclusions: These data indicated that the levels of RDW were significantly associated with increased risk of progression to ESRD in patients with DN, despite did not an independent predictor.
The genetic effect of more pathogenic variants in various DKD susceptibility genes, especially variants in the gene, partially explained the more severe kidney phenotype in probands with kidney biopsy-proven DKD.
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