A Prediction Model of the Incidence of Nonalcoholic Fatty Liver Disease With Visceral Fatty Obesity
A General Population-Based Study
Bibliographic Data
| ID | 22078735 |
|---|---|
| Authors | Yang Zhou (0000-0002-4537-9857, Central South University), Xiangping Chai (0000-0002-3575-2646, Central South University), Tuo Guo (Central South University), Yuting Pu (0000-0001-9467-7503, Central South University), Mengping Zeng (Central South University), Aifang Zhong (0000-0002-8274-7313, Central South University), Guifang Yang (0000-0003-4809-0175, Central South University), Jiajia Cai (0000-0001-6224-4391, Central South University, corresponding author) |
| Year | 2022 |
| Volume | 10 |
| Pages | 895045-895045 |
| Publication date | 2022-06-23 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.895045 |
| PMID | 35812496 |
| OpenAlex | W4283317957 |
| Language | EN |
| References cited | 37 |
Objective: This study aimed to distinguish the risk variables of nonalcoholic fatty liver disease (NAFLD) and to construct a prediction model of NAFLD in visceral fat obesity in Japanese adults. Methods: = 455), respectively. The LASSO method and multivariate regression analysis were performed for selecting risk factors in the training group. Then, overlapping features were selected to screen the effective and suitable risk variables for NAFLD with visceral fatty obesity, and a nomogram incorporating the selected risk factors in the training group was constructed. Then, we used the C-index, calibration plot, decision curve analysis, and cumulative hazard analysis to test the discrimination, calibration, and clinical meaning of the nomogram. At last, internal validation was used in the validation group. Results: We contract a nomogram and validated it using easily available and cost-effective parameters to predict the incidence of NAFLD in participants with visceral fatty obesity, including ALT, HbA1c, body weight, FPG, and TG. In training cohort, the area under the ROC was 0.863, with 95% CI: 0.84-0.885. In validation cohort, C-index was 0.887, with 95%CI: 0.857-0.888. The decision curve analysis showed that the model's prediction is more effective. Decision curve analysis of the training cohort and validation cohort showed that the predictive model was more effective in predicting the risk of NAFLD in Japanese patients with visceral fatty obesity. To help researchers and clinicians better use the nomogram, our online version can be accessed at https://xy2yyjzyxk.shinyapps.io/NAFLD/. Conclusions: Most patients with visceral fatty obesity have a risk of NALFD, but some will not develop into it. The presented nomogram can accurately identify these patients at high risk
Cohort · Cohort study · Confidence interval · Disease · Environmental health · Fatty liver · Hazard ratio · Nomogram · Nonalcoholic fatty liver disease · Population · Receiver operating characteristic · Bariatric Surgery and Outcomes · Diabetes, Cardiovascular Risks, and Lipoproteins · Liver Disease Diagnosis and Treatment · Mathematics · Medicine · Internal Medicine
| Highly cited | No |
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