PMID- 37448880
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 1178-7007 (Print)
IS  - 1178-7007 (Electronic)
IS  - 1178-7007 (Linking)
VI  - 16
DP  - 2023
TI  - Prediction of Diabetic Kidney Disease in Newly Diagnosed Type 2 Diabetes 
      Mellitus.
PG  - 2061-2075
LID - 10.2147/DMSO.S417300 [doi]
AB  - BACKGROUND: Diabetic kidney disease (DKD), a common microvascular complication of 
      diabetes mellitus (DM), is always asymptomatic until it develops to the advanced 
      stage. Thus, we aim to develop a nomogram prediction model for progression to DKD 
      in newly diagnosed type 2 diabetes mellitus (T2DM). METHODS: This was a 
      single-center analysis of prospective data collected from 521 newly diagnosed 
      patients with T2DM. All related clinical records were incorporated, including the 
      triglyceride-glucose index (TyG index). The least absolute shrinkage and 
      selection operator (LASSO) was used to build a prediction model. In addition, 
      discrimination, calibration, and clinical practicality of the nomogram were 
      evaluated. RESULTS: In this study, 156 participants were incorporated as the 
      validation set, while the remaining 365 were incorporated into the training set. 
      The predictive factors included in the individualized nomogram prediction model 
      included 5 variables. The area under the curve (AUC) for the prediction model was 
      0.826 (95% CI 0.775 to 0.876), indicating excellent discrimination performance. 
      The model performed exceptionally well in terms of predictive accuracy and 
      clinical applicability, according to calibration curves and decision curve 
      analysis. CONCLUSION: The predictive nomogram for the risk of DKD in newly 
      diagnosed T2DM patients had outstanding discrimination and calibration, which 
      could help in clinical practice.
CI  - (c) 2023 Mu et al.
FAU - Mu, Xiaodie
AU  - Mu X
AUID- ORCID: 0000-0003-0665-9319
AD  - Department of Nephrology, The Third Affiliated Hospital of Soochow University, 
      Changzhou, People's Republic of China.
FAU - Wu, Aihua
AU  - Wu A
AUID- ORCID: 0000-0002-8251-6240
AD  - Department of Nephrology, The Third Affiliated Hospital of Soochow University, 
      Changzhou, People's Republic of China.
FAU - Hu, Huiyue
AU  - Hu H
AD  - Department of Nephrology, The Third Affiliated Hospital of Soochow University, 
      Changzhou, People's Republic of China.
FAU - Zhou, Hua
AU  - Zhou H
AUID- ORCID: 0000-0003-0130-8763
AD  - Department of Nephrology, The Third Affiliated Hospital of Soochow University, 
      Changzhou, People's Republic of China.
FAU - Yang, Min
AU  - Yang M
AD  - Department of Nephrology, The Third Affiliated Hospital of Soochow University, 
      Changzhou, People's Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20230708
PL  - New Zealand
TA  - Diabetes Metab Syndr Obes
JT  - Diabetes, metabolic syndrome and obesity : targets and therapy
JID - 101515585
PMC - PMC10337686
OTO - NOTNLM
OT  - diabetic kidney disease
OT  - nomogram
OT  - prediction model
OT  - risk assessment
OT  - type 2 diabetes mellitus
COIS- The authors report no conflicts of interest in this work.
EDAT- 2023/07/14 13:05
MHDA- 2023/07/14 13:06
PMCR- 2023/07/08
CRDT- 2023/07/14 03:55
PHST- 2023/05/06 00:00 [received]
PHST- 2023/07/04 00:00 [accepted]
PHST- 2023/07/14 13:06 [medline]
PHST- 2023/07/14 13:05 [pubmed]
PHST- 2023/07/14 03:55 [entrez]
PHST- 2023/07/08 00:00 [pmc-release]
AID - 417300 [pii]
AID - 10.2147/DMSO.S417300 [doi]
PST - epublish
SO  - Diabetes Metab Syndr Obes. 2023 Jul 8;16:2061-2075. doi: 10.2147/DMSO.S417300. 
      eCollection 2023.