Risk Prediction for Non-alcoholic Fatty Liver Disease Based on Biochemical and Dietary Variables in a Chinese Han Population

Development and validation of a nomogram for nonalcoholic fatty liver disease in Western Xinjiang, China

OBJECTIVE

The aim of this study was to establish a simple, nonalcoholic fatty liver disease (NAFLD) screening model using readily available variables to identify high-risk individuals in Western Xinjiang, China.

METHODS

A total of 40 033 patients from the National Health Examination were divided into a training group (70%) and a validation group (30%). Univariate regression and least absolute shrinkage and selection operator models optimized feature selection, while a multivariate logistic regression analysis constructed the prediction model. The model's performance was evaluated using the area under the receiver operating characteristic curve, and its clinical utility was assessed through decision curve analysis.

RESULTS

The nomogram assessed NAFLD risk based on factors such as sex, age, diastolic blood pressure, waist circumference, BMI, fasting plasma glucose, alanine aminotransferase, platelet count, total cholesterol, triglycerides, low-density lipoprotein-cholesterol, and high-density lipoprotein-cholesterol. The area under the receiver operating characteristic curves were 0.829 for men and 0.859 for women in the development group, and 0.817 for men and 0.865 for women in the validation group. The decision curve analysis confirmed the nomogram's clinical usefulness, with consistent findings in the validation set.

CONCLUSION

A user-friendly nomogram prediction model for NAFLD risk was successfully developed and validated for Western Xinjiang, China.

Use of Machine Learning to Predict Onset of NAFLD in an All-Comers Cohort-Development and Validation in 2 Large Asian Cohorts

Background and Aims

Nonalcoholic fatty liver disease (NAFLD) is one of the most common liver diseases. There are no universally accepted models that accurately predict time to onset of NAFLD. Machine learning (ML) models may allow prediction of such time-to-event (ie, survival) outcomes. This study aims to develop and independently validate ML-derived models to allow personalized prediction of time to onset of NAFLD in individuals who have no NAFLD at baseline.

Methods

The development dataset comprised 25,599 individuals from a South Korean NAFLD registry. A random 70:30 split divided it into training and internal validation sets. ML survival models (random survival forest, extra survival trees) were fitted, with time to NAFLD diagnosis in months as the target variable and routine anthropometric and laboratory parameters as predictors. The independent validation dataset comprised 16,173 individuals from a Chinese open dataset. Models were evaluated using the concordance index (c-index) and Brier score on both the internal and independent validation sets.

Results

The datasets (development vs independent validation) had 1,331,107 vs 543,874 person months of follow-up, NAFLD incidence of 25.7% (6584 individuals) vs 14.4% (2322 individuals), and median time to NAFLD onset of 60 (interquartile range 38-75) vs 24 (interquartile range 13-37) months, respectively. The ML models achieved a good c-index of >0.7 in the validation cohort-random survival forest 0.751 (95% confidence interval 0.742-0.759), extra survival trees 0.752 (95% confidence interval 0.744-0.762).

Conclusion

ML models can predict time-to-onset of NAFLD based on routine patient data. They can be used by clinicians to deliver personalized predictions to patients, which may facilitate patient counseling and clinical decision making on interval imaging timing.

Nomogram for predicting the risk of nonalcoholic fatty liver disease in older adults in Qingdao, China: A cross-sectional study

BACKGROUND AND OBJECTIVES

To explore the risk factors for non-alcoholic fatty liver disease (NAFLD) and to establish a non-invasive tool for the screening of NAFLD in an older adult population.

METHODS AND STUDY DESIGN

A total of 131,161 participants were included in this cross-sectional study. Participants were randomly divided into training and validation sets (7:3). The least absolute shrinkage and selection operator method was used to screen risk factors. Multivariate logistic regression was employed to develop a nomogram, which was made available online. Receiver operating characteristic curve analysis, calibration plots, and decision curve analysis were used to validate the discrimination, calibration, and clinical practicability of the nomogram. Sex and age subgroup analyses were conducted to further validate the reliability of the model.

RESULTS

Nine variables were identified for inclusion in the nomogram (age, sex, waist circumference, body mass index, exercise frequency, systolic blood pressure, fasting plasma glucose, alanine aminotransferase, and low-density lipoprotein cholesterol). The area under the receiver operating characteristic curve values were 0.793 and 0.790 for the training set and the validation set, respectively. The calibration plots and decision curve analyses showed good calibration and clinical utility. Subgroup analyses demonstrated consistent discriminatory ability in different sex and age subgroups.

CONCLUSIONS

This study established and validated a new nomogram model for evaluating the risk of NAFLD among older adults. The nomogram had good discriminatory performance and is a non-invasive and convenient tool for the screening of NAFLD in older adults.

Identifying metabolic dysfunction-associated steatotic liver disease in patients with hypertension and pre-hypertension: An interpretable machine learning approach

Objective

Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most prevalent liver diseases and is associated with pre-hypertension and hypertension. Our research aims to develop interpretable machine learning (ML) models to accurately identify MASLD in hypertensive and pre-hypertensive populations.

Methods

The dataset for 4722 hypertensive and pre-hypertensive patients is from subjects in the NAGALA study. Six ML models, including the decision tree, K-nearest neighbor, gradient boosting, naive Bayes, support vector machine, and random forest (RF) models, were used in this study. The optimal model was constructed according to the performances of models evaluated by K-fold cross-validation (k = 5), the area under the receiver operating characteristic curve (AUC), average precision (AP), accuracy, sensitivity, specificity, and F1. Shapley additive explanation (SHAP) values were employed for both global and local interpretation of the model results.

Results

The prevalence of MASLD in hypertensive and pre-hypertensive patients was 44.3% (362 cases) and 28.3% (1107 cases), respectively. The RF model outperformed the other five models with an AUC of 0.889, AP of 0.800, accuracy of 0.819, sensitivity of 0.816, specificity of 0.821, and F1 of 0.729. According to the SHAP analysis, the top five important features were alanine aminotransferase, body mass index, waist circumference, high-density lipoprotein cholesterol, and total cholesterol. Further analysis of the feature selection in the RF model revealed that incorporating all features leads to optimal model performance.

Conclusions

ML algorithms, especially RF algorithm, improve the accuracy of MASLD identification, and the global and local interpretation of the RF model results enables us to intuitively understand how various features affect the chances of MASLD in patients with hypertension and pre-hypertension.

Liver Fat Scores for Noninvasive Diagnosis and Monitoring of Nonalcoholic Fatty Liver Disease in Epidemiological and Clinical Studies

Nonalcoholic fatty liver disease (NAFLD) is strongly associated with the metabolic syndrome and type 2 diabetes and independently contributes to long-term complications. Being often asymptomatic but reversible, it would require population-wide screening, but direct diagnostics are either too invasive (liver biopsy), costly (MRI) or depending on the examiner's expertise (ultrasonography). Hepatosteatosis is usually accommodated by features of the metabolic syndrome (e.g. obesity, disturbances in triglyceride and glucose metabolism), and signs of hepatocellular damage, all of which are reflected by biomarkers, which poorly predict NAFLD as single item, but provide a cheap diagnostic alternative when integrated into composite liver fat indices. Fatty liver index, NAFLD LFS, and hepatic steatosis index are common and accurate indices for NAFLD prediction, but show limited accuracy for liver fat quantification. Other indices are rarely used. Hepatic fibrosis scores are commonly used in clinical practice, but their mandatory reflection of fibrotic reorganization, hepatic injury or systemic sequelae reduces sensitivity for the diagnosis of simple steatosis. Diet-induced liver fat changes are poorly reflected by liver fat indices, depending on the intervention and its specific impact of weight loss on NAFLD. This limited validity in longitudinal settings stimulates research for new equations. Adipokines, hepatokines, markers of cellular integrity, genetic variants but also simple and inexpensive routine parameters might be potential components. Currently, liver fat indices lack precision for NAFLD prediction or monitoring in individual patients, but in large cohorts they may substitute nonexistent imaging data and serve as a compound biomarker of metabolic syndrome and its cardiometabolic sequelae.

Diagnostic value of triglyceride-glucose index and related parameters in metabolism-associated fatty liver disease in a Chinese population: a cross-sectional study

OBJECTIVE

Our study aimed to explore the diagnostic value of triglyceride-glucose (TyG) and its related parameters in metabolism-associated fatty liver disease (MAFLD).

DESIGN

A cross-sectional study of residents who attended medical checkups at the First Hospital of Nanping City, Fujian Medical University, between 2015 and 2017.

SETTING

One participation centre.

PARTICIPANTS

2605 subjects met the inclusion-exclusion criteria and were grouped according to whether they had MAFLD.

RESULTS

The TyG index and its associated parameters are positively associated with the risk of developing MAFLD (p<0.001). Restriction cube spline analysis showed a significant dose-response relationship between the TyG index and MAFLD. The risk of developing MAFLD increases significantly with a higher TyG index. After adjusting for confounders, this relationship remains (OR: 4.89, 95% CI 3.98 to 6.00). The areas under the receiver operating characteristic curves of the TyG index for MAFLD detection were 0.793 (0.774 to 0.812). The areas under the curve (AUC) of TyG-related parameters were improved, among which TyG-waist circumference (TyG-WC) showed the largest AUC for MAFLD detection (0.873, 95% CI 0.860 to 0.887). In addition, the best cut-off value of the TyG-WC was 716.743, with a sensitivity and specificity of 88.7% and 71.4%, respectively.

CONCLUSION

The TyG index effectively identifies MAFLD, and the TyG-related parameters improved the identification and diagnosis of MAFLD, suggesting that TyG-related parameters, especially TyG-WC, may be a useful marker for diagnosing MAFLD.

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

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.

Association Between Serum Uric Acid and Non-Alcoholic Fatty Liver Disease: An Updated Systematic Review and Meta-Analysis

Objective

Recent epidemiological evidence shows that there is an association between serum uric acid (SUA) levels and nonalcoholic fatty liver disease (NAFLD). The purpose of this meta-analysis is to summarize all available evidence and assess the associations between SUA levels and NAFLD.

Methods

Using two databases, Web of Science and PubMed, observational studies were applied from the establishment of the databases to June 2022. We used a random effect model to construct the pooled odds ratio (OR) and 95% confidence interval (CI) to appraise the association between SUA levels and NAFLD. The Begg's test was conducted to appraise publication bias.

Results

A total of 50 studies were included, involving 2,079,710 participants (719,013 NAFLD patients). The prevalence and incidence rates (95% CIs) of NAFLD in the patients with hyperuricemia were 65% (57-73%) and 31% (20-41%), respectively. Compared to participants with lower levels of SUA, the pooled OR (95% CI) of NAFLD in those with higher levels of SUA was 1.88 (95% CI: 1.76-2.00). In the subgroup analyses, we found that SUA levels were positively associated with NAFLD in all subgroups, according to study design, study quality, sample size, sex, comparison, age, or country.

Conclusion

This meta-analysis shows that increased SUA levels are positively associated with NAFLD. The results suggested that reducing SUA levels can be a potential strategy for the prevention of NAFLD.

Registration Number

PROSPERO-CRD42022358431.

Development and validation of an online dynamic nomogram based on the atherogenic index of plasma to screen nonalcoholic fatty liver disease

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD), a common liver disease worldwide, can be reversed early in life with lifestyle and medical interventions. This study aimed to develop a noninvasive tool to screen NAFLD accurately.

METHODS

Risk factors for NAFLD were identified using multivariate logistic regression analysis, and an online NAFLD screening nomogram was developed. The nomogram was compared with reported models (fatty liver index (FLI), atherogenic index of plasma (AIP), and hepatic steatosis index (HSI)). Nomogram performance was evaluated through internal and external validation (National Health and Nutrition Examination Survey (NHANES) database).

RESULTS

The nomogram was developed based on six variables. The diagnostic performance of the present nomogram for NAFLD (area under the receiver operator characteristic curve (AUROC): 0.863, 0.864, and 0.833, respectively) was superior to that of the HSI (AUROC: 0.835, 0.833, and 0.810, respectively) and AIP (AUROC: 0.782, 0.773, and 0.728, respectively) in the training, validation, and NHANES sets. Decision curve analysis and clinical impact curve analysis presented good clinical utility.

CONCLUSION

This study establishes a new online dynamic nomogram with excellent diagnostic and clinical performance. It has the potential to be a noninvasive and convenient method for screening individuals at high risk for NAFLD.

Evaluated Glomerular Filtration Rate Is Associated With Non-alcoholic Fatty Liver Disease: A 5-Year Longitudinal Cohort Study in Chinese Non-obese People

Objective

Evidence regarding the association between evaluated glomerular filtration rate (eGFR) and non-alcoholic fatty liver disease (NAFLD) is still limited. On that account, the purpose of our research is to survey the link of evaluated eGFR on NAFLD.

Methods

This study is a retrospective cohort study. Which consecutively and non-selectively collected a total of 16,138 non-obese participants in a Chinese hospital from January 2010 to December 2014. We then used the Cox proportional-hazards regression model to explore the relationship between baseline eGFR and NAFLD risk. A Cox proportional hazards regression with cubic spline functions and smooth curve fitting (the cubic spline smoothing) was used to identify the non-linear relationship between eGFR and NAFLD. Additionally, we also performed a series of sensitivity analyses and subgroup analyses. Data had been uploaded to the DATADRYAD website.

Results

The mean age of the included individuals was 43.21 ± 14.95 years old, and 8,467 (52.47%) were male. The mean baseline eGFR was 98.83 ± 22.80 mL/min per 1.73m2. During a median follow-up time of 35.8 months, 2,317 (14.36%) people experienced NAFLD. After adjusting covariates, the results showed that eGFR was negatively associated with incident NAFLD (HR = 0.983, 95%CI: 0.980, 0.985). There was also a non-linear relationship between eGFR and NAFLD, and the inflection point of eGFR was 103.489 mL/min per 1.73 m2. The effect sizes (HR) on the left and right sides of the inflection point were 0.988 (0.984, 0.991) and 0.971 (0.963, 0.979), respectively. And the sensitive analysis demonstrated the robustness of our results. Subgroup analysis showed that eGFR was more strongly associated with incident NAFLD in diastolic blood pressure (DBP) &lt; 90 mmHg, fasting plasma glucose (FPG) ≤ 6.1 mmol/L, high-density lipoprotein cholesterol (HDL-c) &lt; 1 mmol/L, and alanine aminotransferase (ALT) ≥ 40 U/L participants. In contrast, the weaker association was probed in those with DBP ≥ 90 mmHg, ALT &lt; 40 U/L, FPG &gt; 6.1 mmol/L, and HDL-c ≥ 1 mmol/L.

Conclusion

This study demonstrates a negative and non-linear association between eGFR and incident NAFLD in the Chinese non-obese population. eGFR is strongly related to NAFLD when eGFR is above 103 mL/min per 1.73 m2. From a therapeutic perspective, it makes sense to maintain eGFR levels within the inflection point to 130 mL/min/1.73 m2.

Macro- and micronutrients in metabolic (dysfunction) associated fatty liver disease: association between advanced fibrosis and high dietary intake of cholesterol/saturated fatty acids

BACKGROUND AND AIM

There is still no approved pharmacotherapy for metabolic (dysfunction) associated fatty liver disease (MAFLD). Although dietary and lifestyle modifications for weight loss remain the mainstay for disease management, the association between macro- and micronutrients and fibrosis stage in patients with MAFLD remains unclear. This study was undertaken to address this issue.

METHODS

This is a retrospective analysis of prospectively collected data from patients in whom MAFLD was diagnosed with vibration controlled transient elastography. Steatosis was defined by a controlled attenuation parameter ≥238 dB/m, whereas a liver stiffness measurement ≥11 kPa was considered to indicate advanced fibrosis.

RESULTS

The study sample consisted of 106 patients with MAFLD (mean age 49 ± 10 years, 52 men and 54 women). Among the different micro- and macronutrients tested, only the dietary intake of cholesterol and saturated fatty acid (SFA) was independently associated with the presence of advanced fibrosis.

CONCLUSION

Our findings suggest that cholesterol/SFA-restricted dietary regimens - as exemplified by the traditional Mediterranean diet - may reduce the risk of advanced fibrosis in patients with MAFLD.

A correlation study of the relationships between nonalcoholic fatty liver disease and serum triglyceride concentration after an oral fat tolerance test

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) has become one of the most common chronic liver diseases worldwide. Triglyceride (TG) accumulation is central to NAFLD development. People now spend most of their day in the postprandial state, and the measurement of postprandial blood lipid concentration can make up for the lack of simple detection of fasting blood lipids. Postprandial triglyceride (PTG) is commonly used as a surrogate for postprandial blood lipid concentrations, and many studies have shown that PTG is a risk factor for NAFLD. The aim of the present study was to investigate the relationship between PTG concentration during oral fat tolerance testing (OFTT) and NAFLD.

METHODS

A total of 472 Chinese adults, aged 25 to 65 years, were enrolled in the study. All the participants underwent OFTT. The serum concentrations of TG and other lipids were measured, and their relationships with NAFLD were analyzed.

RESULTS

Of the 472 participants, 155 were diagnosed with NAFLD. The fasting and postprandial TG concentrations of the participants with NAFLD were higher than those of healthy participants (P < 0.05). The TG concentrations of the healthy participants peaked 4 h postprandially, whereas those of the participants with NAFLD peaked 6 h postprandially and reached higher peak values. Postprandial TG concentration was significantly associated with a higher risk of NAFLD.

CONCLUSIONS

High PTG is positively related to a higher risk of NAFLD, and the PTG concentrations of patients with NAFLD are higher than in healthy individuals, with a delayed peak. Therefore, 4-h PTG may represent a potential marker of NAFLD.

TRIAL REGISTRATION

ChiCTR1800019514 .

Composite BMI and Waist-to-Height Ratio Index for Risk Assessment of Non-alcoholic Fatty Liver Disease in Adult Populations

Background: As obesity becomes more prevalent, non-alcoholic fatty liver disease (NAFLD) is also becoming a major worldwide health problem and the most common cause of chronic liver disease. A new obesity classification method based on a composite index which includes both the body mass index (BMI) and the waist-to-height ratio (WHtR) was recently proposed. However, the usefulness of this approach to assess the risk of NAFLD is unclear. Methods: This is a cross-sectional study of 1,276 adult individuals in Dalian, China. The Mann Whitney U test, χ2 test and t-test were used to compare differences between groups. Binary logistic regression analysis was used to identify independent risk factors. Based on BMI and WHtR tertiles, individuals were divided into five new groups. Spearman correlation and receiver operating characteristic curve (ROC) analyses were performed to compare the NAFLD risk factors among groups based on BMI alone, WHtR alone, or the combination of both indexes. Results: BMI, waistline circumference (WC), WHtR, alanine aminotransferase (ALT), weight, triglycerides (TG), γ-glutamyl transpeptidase (GGT), serum uric acid (SUA), red blood cell (RBC) counts, hemoglobin levels (HGB), fasting blood glucose (FBG) and aspartate aminotransferase (AST) levels were identified as high risk factors for NAFLD (all AUC > 0.7). Logistic regression analysis suggested that BMI and WHtR were independent predictors of the appearance of NAFLD (the ORs for BMI and WHtR were 1.595 and 4.060E-11, respectively; all P < 0.001). The combination of BMI and WHtR tertiles significantly improved the correlation coefficient and Area under the receiver operating characteristic curve (AUC) for NAFLD risk factors in subjects classified as overweight or obese when compared with either BMI or WHtR alone. Conclusions: BMI, WC, WHtR, ALT, weight, TG, GGT, SUA, RBC, HGB, FBG, AST were high risk factors for NAFLD. The composite BMI and WHtR index improved body fat classification and the ability to detect individuals with NAFLD risk, offering a more precise method for the early identification of high- and low-risk NAFLD patients.

A Nomogram Model Based on Noninvasive Bioindicators to Predict 3-Year Risk of Nonalcoholic Fatty Liver in Nonobese Mainland Chinese: A Prospective Cohort Study

The purpose of this study is to establish and validate an accurate and personalized nonalcoholic fatty liver disease (NAFLD) prediction model based on the nonobese population in China. This study is a secondary analysis of a prospective study. We included 6,155 nonobese adults without NAFLD at baseline, with a median follow-up of 2.3 years. Univariate and multivariate Cox regression analyses were used to determine independent predictors. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize the selection of variables. Based on the results of multivariate analysis, a prediction model was established. Harrell's consistency index (C-index) and area under the curve (AUC) were used to determine the discrimination of the proposed model. The goodness of fit of the calibration model was tested, and the clinical application value of the model was evaluated by decision curve analysis (DCA). The participants were randomly divided into a training cohort (n = 4,605) and a validation cohort (n = 1,550). Finally, seven of the variables (HDL-c, BMI, GGT, ALT, TB, DBIL, and TG) were included in the prediction model. In the training cohort, the C-index and AUC value of this prediction model were 0.832 (95% confidence interval (CI), 0.820-0.844) and 0.861 (95% CI, 0.849-0.873), respectively. In the validation cohort, the C-index and AUC values of this prediction model were 0.829 (95% CI, 0.806-0.852) and 0.859 (95% CI, 0.841-0.877), respectively. The calibration plots demonstrated good agreement between the estimated probability and the actual observation. DCA demonstrated a clinically effective predictive model. Our nomogram can be used as a simple, reasonable, economical, and widely used tool to predict the 3-year risk of NAFLD in nonobese populations in China, which is helpful for timely intervention and reducing the incidence of NAFLD.