Clinical spectrum transition and prediction model of nonalcoholic fatty liver disease in children with obesity

Zhou, Xuelian; Lin, Xiufu; Chen, Jingnan; Pu, Jiaqi; Wu, Wei; Wu, Zhaoyuan; H, Lin; Huang, Ke; Zhang, Li; Dai, Yang-Li; Ni, Yan; Dong, Guanping; Fu, Junfen

doi:10.3389/fendo.2022.986841

Cited by 8 publications

(9 citation statements)

References 58 publications

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“…These sex and race differences in weight change are concordant with previous epidemiological studies that studied sex‐ and race‐based differences in gradual weight gain. These overall trends in weight gain match other analyses of both NHANES data and other cohorts of the United States population 14–22 …”

Section: Discussionsupporting

confidence: 80%

“…These overall trends in weight gain match other analyses of both NHANES data and other cohorts of the United States population. 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 …”

Section: Discussionmentioning

confidence: 99%

“…These overall trends in weight gain match other analyses of both NHANES data and other cohorts of the United States population. [14][15][16][17][18][19][20][21][22] Many past obesity studies have observed age-related weight gain in population cohorts. [23][24][25][26] These studies showed significant weight gain in the populations (greater than 3 kg per decade) and were found to have significant racial and sex-based disparities.…”

Section: Model Construction and Statistical Analysismentioning

confidence: 99%

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Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017–2020

Huang

2023

Obesity Science & Practice

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ImportanceThe prevalence of obesity among United States adults has increased from 34.9% in 2013–2014 to 42.8% in 2017–2018. Developing methods to model the increase of obesity over‐time is a necessity to know how to accurately quantify its cost and to develop solutions to combat this national public health emergency.MethodsA cross‐sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017–2020) was conducted in individuals who completed the weight questionnaire and had accurate data for both weight at the time of survey and weight 10 years ago. To model the dynamics of obesity, a Markov transition state matrix was created, which allowed for the analysis of weight transitions over time. Bootstrap simulation was incorporated to account for uncertainty and generate multiple simulated datasets, providing a more robust estimation of the prevalence and trends in obesity within the cohort.ResultsOf the 6146 individuals who met the inclusion criteria, 3024 (49%) individuals were male and 3122 (51%) were female. There were 2252 (37%) White individuals, 1257 (20%) Hispanic individuals, 1636 (37%) Black individuals, and 739 (12%) Asian individuals. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight. A total of 2411 (39%) individuals lost weight, and 3735 (61%) individuals gained weight. 87 (1%) individuals were underweight (BMI <18.5), 2058 (33%) were normal weight (18.5 ≤ BMI <25), 1376 (22%) were overweight (25 ≤ BMI <30) and 2625 (43%) were in the obese category (BMI >30).ConclusionUnited States adults are at risk of transitioning from normal weight to the overweight or obese category. Markov modeling combined with bootstrap simulations can accurately model long‐term weight status.

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Section: Discussionsupporting

confidence: 80%

“…These overall trends in weight gain match other analyses of both NHANES data and other cohorts of the United States population. 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 …”

Section: Discussionmentioning

confidence: 99%

Section: Model Construction and Statistical Analysismentioning

confidence: 99%

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Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017–2020

Huang

2023

Obesity Science & Practice

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“…The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020 [1][2][3][4][5][6] . Thus, new methods for modeling this increase in obesity overtime is necessary to best combat this public health emergency [7][8][9][10] . Multiple studies have identi ed major risk factors for obesity, including demographic factors (race, sex, age), lifestyle factors (exercise, sleep), and clinical comorbidities (hypertension, diabetes.…”

Section: Introductionmentioning

confidence: 99%

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

Huang¹,

Huang

2023

Preprint

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Importance: The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection. Methods: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017–2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling. Results: Of the 6,146 patients that met the inclusion criteria, 3,024 (49%) of patients were male and 3,122 (51%) of patients were female. There were 2,252 (37%) White patients, 1,257 (20%) Hispanic patients, 1,636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2,411 (39%) patients lost weight, and 3,735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2,058 (33%) were normal weight (18.5 ≤ BMI < 25), 1,376 (22%) were overweight (25 ≤ BMI < 30) and 2,625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight. Conclusions: United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions.

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“…Mass screening of health screening populations via ultrasound is not only expensive but also consumes a significant amount of medical resources. Therefore, more researchers have begun developing NAFLD risk prediction models using existing clinical data through machine learning and artificial intelligence [13][14][15][16][17]. Risk prediction models for NAFLD are also available and demonstrate good predictive value, but most are built based on retrospective studies.…”

Section: Introductionmentioning

confidence: 99%

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

Huang,

Jin,

Mao

2023

J Med Internet Res

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Background Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events. Objective This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk. Methods The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)–recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets. Results The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model. Conclusions Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events.

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Clinical spectrum transition and prediction model of nonalcoholic fatty liver disease in children with obesity

Cited by 8 publications

References 58 publications

Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017–2020

Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017–2020

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

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