PMID- 36908240
OWN - NLM
STAT- MEDLINE
DCOM- 20230314
LR  - 20240121
IS  - 1365-2060 (Electronic)
IS  - 0785-3890 (Print)
IS  - 0785-3890 (Linking)
VI  - 55
IP  - 1
DP  - 2023 Dec
TI  - Novel model predicts diastolic cardiac dysfunction in type 2 diabetes.
PG  - 766-777
LID - 10.1080/07853890.2023.2180154 [doi]
AB  - OBJECTIVE: Diabetes mellitus complicated with heart failure has high mortality 
      and morbidity, but no reliable diagnoses and treatments are available. This study 
      aimed to develop and verify a new model nomogram based on clinical parameters to 
      predict diastolic cardiac dysfunction in patients with Type 2 diabetes mellitus 
      (T2DM). METHODS: 3030 patients with T2DM underwent Doppler echocardiography at 
      the First Affiliated Hospital of Shenzhen University between January 2014 and 
      December 2021. The patients were divided into the training dataset (n = 1701) and 
      the verification dataset (n = 1329). In this study, a predictive diastolic 
      cardiac dysfunction nomogram is developed using multivariable logical regression 
      analysis, which contains the candidates selected in a minor absolute shrinkage 
      and selection operator regression model. Discrimination in the prediction model 
      was assessed using the area under the receiver operating characteristic curve 
      (AUC-ROC). The calibration curve was applied to evaluate the calibration of the 
      alignment nomogram, and the clinical decision curve was used to determine the 
      clinical practicability of the alignment map. The verification dataset was used 
      to evaluate the prediction model's performance. RESULTS: A multivariable model 
      that included age, body mass index (BMI), triglyceride (TG), creatine 
      phosphokinase isoenzyme (CK-MB), serum sodium (Na), and urinary 
      albumin/creatinine ratio (UACR) was presented as the nomogram. We obtained the 
      model for estimating diastolic cardiac dysfunction in patients with T2DM. The 
      AUC-ROC of the training dataset in our model was 0.8307, with 95% CI of 
      0.8109-0.8505. Similar to the results obtained with the training dataset, the 
      AUC-ROC of the verification dataset in our model was 0.8083, with 95% CI of 
      0.7843-0.8324, thus demonstrating robust. The function of the predictive model 
      was as follows: Diastolic Dysfunction = -4.41303 + 0.14100*Age(year)+0.10491*BMI 
      (kg/m(2)) +0.12902*TG (mmol/L) +0.03970*CK-MB (ng/mL) -0.03988*Na(mmol/L) 
      +0.65395 * (UACR > 30 mg/g) + 1.10837 * (UACR > 300 mg/g). The calibration plot 
      diagram of predicted probabilities against observed DCM rates indicated excellent 
      concordance. Decision curve analysis demonstrated that the novel nomogram was 
      clinically useful. CONCLUSION: Diastolic cardiac dysfunction in patients with 
      T2DM can be predicted by clinical parameters. Our prediction model may represent 
      an effective tool for large-scale epidemiological study of diastolic cardiac 
      dysfunction in T2DM patients and provide a reliable method for early screening of 
      T2DM patients with cardiac complications.KEY MESSAGESThis study used clinical 
      parameters to predict diastolic cardiac dysfunction in patients with T2DM. This 
      study established a nomogram for predicting diastolic cardiac dysfunction by 
      multivariate logical regression analysis. Our predictive model can be used as an 
      effective tool for large-scale epidemiological study of diastolic cardiac 
      dysfunction in patients with T2DM and provides a reliable method for early 
      screening of cardiac complications in patients with T2DM.
FAU - Hao, Mingyu
AU  - Hao M
AUID- ORCID: 0000-0001-8418-2996
AD  - Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic 
      Diseases, Shenzhen Second People's Hospital, the First Affiliated Hospital of 
      Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, 
      China.
AD  - Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 
      Shenzhen, China.
FAU - Huang, Xiaohong
AU  - Huang X
AUID- ORCID: 0000-0002-0364-2696
AD  - Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic 
      Diseases, Shenzhen Second People's Hospital, the First Affiliated Hospital of 
      Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, 
      China.
AD  - Guangzhou Medical University, Guangzhou, China.
FAU - Liu, Xueting
AU  - Liu X
AUID- ORCID: 0000-0003-0992-8860
AD  - Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic 
      Diseases, Shenzhen Second People's Hospital, the First Affiliated Hospital of 
      Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, 
      China.
FAU - Fang, Xiaokang
AU  - Fang X
AUID- ORCID: 0000-0002-5850-8638
AD  - Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic 
      Diseases, Shenzhen Second People's Hospital, the First Affiliated Hospital of 
      Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, 
      China.
FAU - Li, Haiyan
AU  - Li H
AUID- ORCID: 0000-0002-6209-0078
AD  - Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic 
      Diseases, Shenzhen Second People's Hospital, the First Affiliated Hospital of 
      Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, 
      China.
FAU - Lv, Lingbo
AU  - Lv L
AUID- ORCID: 0000-0002-6693-4733
AD  - Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic 
      Diseases, Shenzhen Second People's Hospital, the First Affiliated Hospital of 
      Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, 
      China.
FAU - Zhou, Liming
AU  - Zhou L
AUID- ORCID: 0000-0002-7416-2913
AD  - Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic 
      Diseases, Shenzhen Second People's Hospital, the First Affiliated Hospital of 
      Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, 
      China.
FAU - Guo, Tiecheng
AU  - Guo T
AUID- ORCID: 0000-0002-6235-3122
AD  - Chiwan Community Health Service Centre, Shenzhen, China.
FAU - Yan, Dewen
AU  - Yan D
AUID- ORCID: 0000-0003-4998-2853
AD  - Department of Endocrinology, Shenzhen Clinical Research Center for Metabolic 
      Diseases, Shenzhen Second People's Hospital, the First Affiliated Hospital of 
      Shenzhen University, Health Science Center of Shenzhen University, Shenzhen, 
      China.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Ann Med
JT  - Annals of medicine
JID - 8906388
SB  - IM
MH  - Humans
MH  - *Diabetes Mellitus, Type 2
MH  - Heart
MH  - *Heart Failure
MH  - Area Under Curve
MH  - Body Mass Index
MH  - Retrospective Studies
PMC - PMC10798288
OTO - NOTNLM
OT  - Diabetic cardiomyopathy
OT  - clinical predictive model
OT  - diastolic cardiac dysfunction
OT  - type 2 diabetes
COIS- No potential conflict of interest was reported by the author(s).
EDAT- 2023/03/14 06:00
MHDA- 2023/03/15 06:00
PMCR- 2023/03/13
CRDT- 2023/03/13 03:22
PHST- 2023/03/13 03:22 [entrez]
PHST- 2023/03/14 06:00 [pubmed]
PHST- 2023/03/15 06:00 [medline]
PHST- 2023/03/13 00:00 [pmc-release]
AID - 2180154 [pii]
AID - 10.1080/07853890.2023.2180154 [doi]
PST - ppublish
SO  - Ann Med. 2023 Dec;55(1):766-777. doi: 10.1080/07853890.2023.2180154.