Filter

Refine by collections

  • Obesity (1)

Refine by date

—
  • Print
  • Save

Search Results

You are looking at 1 - 1 of 1 items for :

  • Author: Shaoping Li x
Clear All Modify Search
Lei Gao Department of Geriatrics, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

Search for other papers by Lei Gao in
Google Scholar
PubMed Close
,
Wenxia Cui Department of Geriatrics, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

Search for other papers by Wenxia Cui in
Google Scholar
PubMed Close
,
Dinghuang Mu Department of Geriatrics, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China

Search for other papers by Dinghuang Mu in
Google Scholar
PubMed Close
,
Shaoping Li Department of Health Management Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

Search for other papers by Shaoping Li in
Google Scholar
PubMed Close
,
Nan Li Department of Geriatrics, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

Search for other papers by Nan Li in
Google Scholar
PubMed Close
,
Weihong Zhou Department of Health Management Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

Search for other papers by Weihong Zhou in
Google Scholar
PubMed Close
, and
Yun Hu Department of Geriatrics, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

Search for other papers by Yun Hu in
Google Scholar
PubMed Close

Objective

To create a nomogram-based model to estimate the Chinese population's 5-year risk of metabolic dysfunction-associated steatotic liver disease (MASLD).

Methods

We randomly divided 7582 participants into two groups in a 7:3 ratio: one group was assigned to work with the training set, which consisted of 5307 cases, and the other group was assigned to validate the model using 2275 cases. The least absolute shrinkage and selection operator model was employed to ascertain the variables with the highest correlation among all potential variables. A logistic model was constructed by incorporating these selected variables, which were subsequently visualized using a nomogram. The discriminatory ability, calibration, and clinical utility of the model were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

During the 5-year follow-up, 1034 (13.64%) total participants were newly diagnosed with MASLD. Using eight variables (gender, body mass index, waist, hemoglobin, alanine aminotransferase, uric acid, triglycerides, and high-density lipoprotein), we built a 5-year MASLD risk prediction model. The nomogram showed an area under the ROC of 0.795 (95% CI: 0.779–0.811) in the training set and 0.785 (95% CI: 0.760–0.810) in the validation set. The calibration curves revealed a 5-year period of agreement between the observed and predicted MASLD risks. DCA curves illustrated the practicality of this nomogram over threshold probability profiles ranging from 5% to 50%.

Conclusion

We created and tested a nomogram to forecast the risk of MASLD prevalence over the next 5 years.

Article Type:
Research Article
Collections:
Obesity
DOI:
https://doi.org/10.1530/EC-24-0186
Volume/Issue:
Volume 13: Issue 8
Open access