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.

Malnutrition and immunologic derangement were not uncommon in patients with chronic kidney disease (CKD). However, the long-term effects of prognostic nutritional index (PNI), an immunonutrition indictor, on renal outcomes in patients with diabetic nephropathy (DN) and type 2 diabetes mellitus (T2DM) are unknown. In this retrospective cohort study, 475 patients with T2DM and biopsy-confirmed DN from West China Hospital between January 2010 and September 2019 were evaluated. PNI was evaluated as serum albumin (g/L) + 5 × lymphocyte count (109/L). The study endpoint was defined as progression to end-stage renal disease (ESRD). The Cox regression analysis was performed to investigate the risk factors of renal failure in DN patients. A total of 321 eligible individuals were finally included in this study. The patients with higher PNI had a higher eGFR and lower proteinuria at baseline. Correlation analysis indicated PNI was positively related eGFR (r = 0.325, p < 0.001), and negatively correlated with proteinuria (r = −0.68, p < 0.001), glomerular lesion (r = −0.412, p < 0.001) and interstitial fibrosis and tubular atrophy (r = −0.282, p < 0.001). During a median follow-up of 30 months (16–50 months), the outcome event occurred in 164(51.09%) of all the patients. After multivariable adjustment, each SD (per-SD) increment of PNI at baseline was associated with a lower incidence of ESRD (hazard ratio, 0.705, 95% CI, 0.523–0.952, p = 0.023), while the hypoalbuminemia and anemia were not. For the prediction of ESRD, the area under curves (AUC) evaluated with time-dependent receiver operating characteristics were 0.79 at 1 year, 0.78 at 2 years, and 0.74 at 3 years, respectively, and the addition of PNI could significantly improve the predictive ability of the model incorporating traditional risk factors. In summary, PNI correlated with eGFR and glomerular injury and was an independent predictor for DN progression in patients with T2DM. Thus, it may facilitate the risk stratification of DN patients and contribute to targeted management.

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Several studies show that patients with early-onset diabetes have higher risk of diabetic complications than those diagnosed in middle age. However, whether early-onset of type 2 diabetes mellitus (T2DM) is a risk factor for diabetic nephropathy (DN) progression remains unclear, especially a lack of data in biopsy-confirmed cohort. In This study, we enrolled 257 patients with T2DM and biopsy-confirmed DN to investigate the role of early-onset T2DM in DN progression. Participants were divided into two groups according to the age of T2DM diagnosis: early-onset group (less than 40 years) and later-onset group (40 years or older). We found that patients with early-onset T2DM had higher glomerular grades and arteriolar hyalinosis scores than those in later-onset group. After adjusted for confounding factors, early-onset of T2DM remained an independent predictor of end-stage renal disease (ESRD) for patients with DN. In conclusion, although with the comparable renal function and proteinuria, patients with early-onset T2DM and DN had worse renal pathological changes than those with later-onset. Early-onset of T2DM might be an important predictor of ESRD for patients with DN, which called more attention to early supervision and prevention for patients with early-onset T2DM and DN.