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Zhou Y, Chai X, Guo T, Pu Y, Zeng M, Zhong A, Yang G, Cai J. A Prediction Model of the Incidence of Nonalcoholic Fatty Liver Disease With Visceral Fatty Obesity: A General Population-Based Study. Front Public Health 2022; 10:895045. [PMID: 35812496 PMCID: PMC9259946 DOI: 10.3389/fpubh.2022.895045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
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 This study is a historical cohort study that included 1,516 individuals with visceral obesity. All individuals were randomly divided into training group and validation group at 70% (n = 1,061) and 30% (n = 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.
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Affiliation(s)
- Yang Zhou
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China
- Trauma Center, Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiangping Chai
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China
- Trauma Center, Second Xiangya Hospital, Central South University, Changsha, China
| | - Tuo Guo
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China
- Trauma Center, Second Xiangya Hospital, Central South University, Changsha, China
| | - Yuting Pu
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China
- Trauma Center, Second Xiangya Hospital, Central South University, Changsha, China
| | - Mengping Zeng
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China
- Trauma Center, Second Xiangya Hospital, Central South University, Changsha, China
| | - Aifang Zhong
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China
- Trauma Center, Second Xiangya Hospital, Central South University, Changsha, China
| | - Guifang Yang
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China
- Trauma Center, Second Xiangya Hospital, Central South University, Changsha, China
| | - Jiajia Cai
- Outpatient Office, Second Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Jiajia Cai
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Suri A, Song E, van Nispen J, Voigt M, Armstrong A, Murali V, Jain A. Advances in the Epidemiology, Diagnosis, and Management of Pediatric Fatty Liver Disease. Clin Ther 2021; 43:438-454. [PMID: 33597074 DOI: 10.1016/j.clinthera.2021.01.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/28/2020] [Accepted: 01/04/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Nonalcoholic fatty liver (NAFL) is a major contributor to pediatric liver disease. This review evaluated the current literature on prevalence, screening, diagnosis, and management of NAFL in children and explored recent advances in the field of pediatric NAFL. METHODS A PubMed search was performed for manuscripts describing disease burden, diagnosis, and management strategies in pediatric NAFL published within the past 15 years. Systematic reviews, clinical practice guidelines, randomized controlled trials, and cohort and case-control studies were reviewed for the purpose of this article. FINDINGS The prevalence of NAFL in children is increasing. It is a leading cause of liver-related morbidity and mortality in children. Screening and diagnosis of NAFL in children are a challenge. Lifestyle changes and exercise are the cornerstones of the management of NAFL. IMPLICATIONS Further research is needed to develop better screening and diagnostic tools for pediatric NAFL, including noninvasive diagnostics. NAFL therapeutics is another area of much-needed, ongoing research.
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Affiliation(s)
- Anandini Suri
- Department of Pediatrics, School of Medicine, St. Louis University, St. Louis, Missouri, USA.
| | - Eric Song
- Department of Pediatrics, School of Medicine, St. Louis University, St. Louis, Missouri, USA
| | - Johan van Nispen
- Department of Pediatrics, School of Medicine, St. Louis University, St. Louis, Missouri, USA
| | - Marcus Voigt
- Department of Pediatrics, School of Medicine, St. Louis University, St. Louis, Missouri, USA
| | - Austin Armstrong
- Department of Pediatrics, School of Medicine, St. Louis University, St. Louis, Missouri, USA
| | - Vidul Murali
- Department of Pediatrics, School of Medicine, St. Louis University, St. Louis, Missouri, USA
| | - Ajay Jain
- Department of Pediatrics, School of Medicine, St. Louis University, St. Louis, Missouri, USA
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