
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-23-11374
00073
10.1097/MD.0000000000039437
3
4500
Research Article
Observational Study
Clinical model to predict the risk of nonalcoholic fatty liver disease: A secondary analysis of data from a cross-sectional study
Yang Bo M Med 18275616479@163.com
a
https://orcid.org/0009-0004-1245-8702
Zhong Xiang MB a*
a Department of Gastroenterology and Hepatology, Guizhou Aerospace Hospital, Zunyi, China.
* Correspondence: Xiang Zhong, Department of Gastroenterology and Hepatology, Guizhou Aerospace Hospital, Zunyi 563000, China (e-mail: 35159336@qq.com).
06 9 2024
06 9 2024
103 36 e3943716 12 2023
09 7 2024
02 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

This study aimed to develop and validate a clinical model for predicting the risk of nonalcoholic fatty liver disease (NAFLD) by using data from a cross-sectional study. This investigation utilized data from the Dryad database and employed multivariable logistic regression analysis, restricted cubic spline, and nomogram analysis to achieve comprehensive insights. The discrimination and calibration of the nomogram were evaluated using the receiver operating characteristic curve and calibration plot. A total of 1072 patients were included in the study, including 456 with non-NAFLD and 616 with NAFLD. Significant differences were observed in terms of sex, body mass index (BMI), tobacco, hypertension, diabetes, alanine aminotransferase (ALT), aspartate aminotransferase (AST), ALT/AST ratio, uric acid (UA), fasting blood glucose (FBG), triglyceride (TG), high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, systolic blood pressure, and diastolic blood pressure (P < .05 for all comparisons). Multivariable logistic regression analysis indicated that sex, BMI, diabetes, ALT/AST ratio, UA, FBG, and TG were associated with an increased risk of NAFLD. Restricted cubic spline indicated a nonlinear relationship between the risk of NAFLD and variables including ALT/AST ratio, FPG, TG, and UA (P for nonlinearity < .01). The variables in the nomogram included BMI, diabetes, ALT/AST ratio, UA, FBG, and TG. The value of area under the curve was 0.790, indicating that the nomogram prediction model exhibited significant discriminatory accuracy. A reliable clinical model for predicting the risk of NAFLD was developed using readily available clinical data. The model can assist clinicians in identifying individuals with an increased risk of NAFLD, enabling early interventions for preventing and managing this prevalent liver disease.

Dryad database
nomogram
nonalcoholic fatty liver disease
predictive model
risk factor
OPEN-ACCESSTRUE
==== Body
pmc1. Introduction

Nonalcoholic fatty liver disease (NAFLD) is a progressive condition characterized by the excessive accumulation of fat in the liver, occurring without substantial alcohol intake.[1] Clinically, NAFLD may present with elevated liver enzymes, hepatomegaly, or nonspecific symptoms such as fatigue and abdominal discomfort. NAFLD stands as the most prevalent liver disease globally, affecting approximately 25% of the global population.[2]

Internationally, NAFLD has been identified as a growing health concern, particularly in developed countries. Widespread changes in diet and lifestyle, along with the prevalence of obesity, have contributed to a surge in NAFLD cases.[3] According to the American Gastroenterological Association, NAFLD is predicted to become the leading cause of liver transplantation by 2030 in the USA.[4] As for China, major urbanization, Westernized diets, and sedentary lifestyles have played a role in the increasing rates of NAFLD. A recent study indicated that China had a higher NAFLD incidence than non-China regions, highlighting the magnitude of this public health issue.[5] NAFLD may progress to cirrhosis and increase the likelihood of hepatocellular cancer, and gut microbiota dysbiosis plays a critical role in this process by disrupting the gut–liver axis and immune system function.[6,7] The pathogenesis of NAFLD is multifaceted, initiated by several risk factors encompassing obesity, type 2 diabetes, dyslipidemia, and endothelial dysfunction.[8] Patients with NAFLD have an increased mortality risk from liver disease, cardiovascular disease, and cancer.[9]

Despite consistent advances in elucidating the pathogenesis of NAFLD, identifying potential therapeutic targets, and progressing drug development, substantial challenges remain unaddressed. To date, no pharmaceutical agent has been approved to treat this condition.[10] In terms of therapeutic approach, the first-line treatment for NAFLD primarily focuses on lifestyle modifications,[11] including adopting a healthy diet, engaging in regular physical activity, and losing weight. The use of insulin-sensitizing drugs can be considered in the presence of diabetes or metabolic syndrome.[12] Identifying those who are at risk of progressing with NAFLD is crucial. Liver biopsy, an invasive method, is often considered the gold standard for diagnosing NAFLD.[13] However, liver biopsy is an invasive procedure associated with a risk of complications such as pain and bleeding.[14,15] Further, it may not accurately represent the extent of liver disease due to the possibility of sampling error.[16] Imaging methods have become increasingly accepted as noninvasive alternatives to liver biopsy in clinical practice. Ultrasonography is a widely recognized imaging method utilized for diagnosing hepatic steatosis, with acceptable sensitivity and specificity in detecting moderate-to-severe hepatic steatosis.[17,18] Its main limitation is not related to cost, but rather, its effectiveness is highly dependent on the operator’s skills, making it less suitable for monitoring patients with NAFLD after therapeutic interventions due to its limited capacity to accurately identify moderate steatosis and its qualitative nature without specialized picture post-processing.[19]

Therefore, identifying patients with an increased risk of NAFLD is crucial to optimize their management. The invasive nature of liver biopsy and the operator-dependent nature of abdominal ultrasonography render them less suitable for diagnosis and disease monitoring in clinical practice, as well as unsuitable for population-level screening. Several clinical features, such as diabetes, hypertension, body mass index (BMI), age, and gender, have been used to predict NAFLD, but they are inaccurate when used alone.[20,21] Hence, the development of simple, readily accessible, and validated noninvasive tests is vital and highly sought-after in the clinical practice of individuals with NAFLD. The NAFLD dataset was obtained from the Dryad database, comprising clinical and routine blood-based variables. This study aimed to develop a simple-to-use nomogram to predict the risk of NAFLD to help provide early evidence for the condition and effectively prevent it.

2. Materials and methods

2.1. Study design and participants

The Dryad database, which is funded by the National Science Foundation, serves as a repository for high-quality research data. Its primary objective is to facilitate academic exchange by protecting and promoting the reuse of research data in scientific publications. The Dryad Digital Repository website was utilized to obtain the data for this investigation (https://Datadryad.org). This website provides open access to the raw data of published papers, allowing for their unrestricted reuse in secondary analysis. In accordance with the Dryad Terms of Service, the specific Dryad data package (data from nonalcoholic fatty liver disease, https://doi.org/10.5061/dryad.7d7wm3809) was reference in this study. The raw data utilized in this study were publicly provided by Yan et al in 2023.[22]

2.2. Data collection

Variables were extracted from the aforementioned database as follows: sex, age, BMI, tobacco use, hypertension, diabetes, platelet, alanine aminotransferase (ALT), aspartate aminotransferase (AST), ALT/AST ratio, uric acid (UA), fasting blood glucose (FBG), total cholesterol, triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Sex was recorded as female or male. The age was categorized as 40 to 49, 50 to 59, 60 to 69, and 70 to 79. BMI was aggregated into normal, overweight, and obese. Hypertension was classified into no and yes. Diabetes was coded as no or yes. The ALT/AST ratio was calculated by dividing ALT by AST.

2.3. Statistical analysis

Continuous variables are denoted as mean ± standard deviation, with the distinction between the non-NAFLD group and the NAFLD group being evaluated via independent-sample t test. Categorical variables are noted as numbers and percentages, with chi-square test being employed for their analysis. The restricted cubic spline (RCS) function serves as a vital instrument for delineating dose–response relationships between continuous exposure and outcomes. Through the employment of univariate and multivariable models, logistic regression analysis was implemented to discern the relationship between clinical variables and NAFLD risk factors. Univariate analysis was performed for every incorporated component. Subsequently, all indicators from this analysis were incorporated into a multivariate logistic regression analysis employing forward stepwise logistic regression with an inclusion and exclusion threshold of 0.1. The logistic regression models employed 95% confidence intervals (CI) for the odds ratio. A nomogram was constructed to estimate the risk model of NAFLD on the basis of multivariate logistic regression. The receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration plots were used to evaluate the calibration and discrimination of the model. R statistical software (version 4.2.3, StataCorp LLC, College Station, TX) was used for all statistical analyses. A P value of <.05 (two-sided) was regarded as statistically significant.

3. Results

3.1. Characteristics of the research population

Out of the original 1592 participants in the research, 520 people who use alcohol were eliminated. A total of 1072 patients were included in the study, including 456 with non-NAFLD and 616 with NAFLD (Fig. 1). The clinicopathological characteristics of patients with and without NAFLD are summarized in Table 1. Significant differences were found in terms of sex, BMI, tobacco, hypertension, diabetes, ALT, AST, ALT/AST, UA, FBG, TG, HDL-C, LDL-C, SBP, and DBP (P < .05 for all comparisons). Regarding gender, more male patients were diagnosed with NAFLD than non-NAFLD (65.7% vs 51.8%). Participants with NAFLD had a higher BMI, tobacco use, hypertension, and diabetes. Serum levels of ALT, AST, UA, FBG, TG, SBP, and DBP were significantly higher in participants with NAFLD than in those without NAFLD.

Table 1 Comparison of the clinical characteristics between NAFLD and non-NAFLD patients.

Characteristics	Non-NAFLD (N = 456)	NAFLD (N = 616)	P	
Sex			<.001	
 Female	220 (48.2%)	211 (34.3%)		
 Male	236 (51.8%)	405 (65.7%)		
Age				
 40–49	100 (21.9%)	121 (19.6%)	.217	
 50–59	187 (41%)	283 (45.9%)		
 60–69	107 (23.5%)	148 (24%)		
 70–79	62 (13.6%)	64 (10.4%)		
BMI			<.001	
 Normal	263 (57.7%)	139 (22.6%)		
 Overweight	169 (37.1%)	335 (54.4%)		
 Obese	24 (5.3%)	142 (23.1%)		
Tobacco			.005	
 No	403 (88.4%)	505 (82%)		
 Yes	53 (11.6%)	111 (18%)		
Hypertension			<.001	
 No	252 (55.3%)	221 (35.9%)		
 Yes	204 (44.7%)	395 (64.1%)		
Diabetes			<.001	
 No	369 (80.9%)	368 (59.7%)		
 Yes	87 (19.1%)	248 (40.3%)		
PLT, 10*9/L	210.0 ± 52.5	213.1 ± 54.2	.361	
ALT, U/L	19.9 ± 13.9	29.2 ± 22.1	<.001	
AST, U/L	20.9 ± 9.5	23.6 ± 10.2	<.001	
ALT/AST ratio	0.9 ± 0.3	1.2 ± 0.4	<.001	
UA, µmol/L	325.6 ± 85.4	370.8 ± 95.5	<.001	
FBG, mmol/L	5.1 ± 1.4	5.8 ± 1.8	<.001	
TC, mmol/L	4.4 ± 1.0	4.5 ± 1.2	.207	
TG, mmol/L	1.3 ± 1.0	2.1 ± 1.8	<.001	
HDL-C, mmol/L	1.2 ± 0.4	1.1 ± 0.3	<.001	
LDL-C, mmol/L	2.7 ± 0.9	2.7 ± 0.9	.645	
SBP, mm Hg	128.7 ± 15.7	131.9 ± 15.9	<.001	
DBP, mm Hg	78.9 ± 11.1	81.9 ± 10.7	<.001	
ALT = alanine aminotransferase, AST = aspartate aminotransferase, BMI = body mass index, DBP = diastolic blood pressure, FBG = fasting blood glucose, HDL-C = high-density lipoprotein cholesterol, LDL-C = low-density lipoprotein cholesterol, NAFLD = nonalcoholic fatty liver disease, PLT = platelet, SBP = systolic blood pressure, TC = total cholesterol, TG = triglyceride, UA = uric acid.

Figure 1. Flow diagram of inclusion and exclusion criteria for the collection of patients with NAFLD in the Dryad database.

3.2. Univariate and multivariate analysis for risk factors of NAFLD

The correlations between potential risk variables and NAFLD were examined using a univariate analysis. The results of univariate analysis revealed that sex, BMI, tobacco, hypertension, diabetes, ALT, AST, ALT/AST ratio, UA, FBG, TG, HDL-C, SBP, and DBP were substantially correlated with NAFLD. However, no correlation was seen between age, platelet, total cholesterol, and LDL-C levels and the presence of NAFLD. In the multivariate logistic model, variables, such as sex (P = .002), BMI (P < .001), diabetes (P = .017), ALT/AST ratio (P = .003), UA (P = .005), FBG (P = .012), and TG (P < .001), were associated with an increased risk of NAFLD (Table 2).

Table 2 Risk factors for NAFLD identified by univariate logistic regression analysis and multivariate logistic regression analysis.

Characteristic	Univariate analysis	Multivariate analysis	
Odds ratio (95% CI)	P value	Odds ratio (95% CI)	P value	
Sex					
 Female	Reference		Reference		
 Male	1.789 (1.397–2.293)	<.001	0.568 (0.397–0.811)	.002	
Age					
 40–49	Reference				
 50–59	1.251 (0.906–1.727)	.175			
 60–69	1.143 (0.795–1.644)	.471			
 70–79	0.853 (0.550–1.322)	.477			
BMI					
 Normal	Reference		Reference		
 Overweight	3.751 (2.845–4.945)	<.001	2.781 (2.038–3.797)	<.001	
 Obese	11.195 (6.935–18.072)	<.001	5.089 (3.015–8.591)	<.001	
Tobacco					
 No	Reference		Reference		
 Yes	1.671 (1.175–2.378)	.004	1.405 (0.919–2.148)	.117	
Hypertension					
 No	Reference		Reference		
 Yes	2.208 (1.724–2.827)	<.001	1.337 (0.967–1.847)	.079	
Diabetes					
 No	Reference		Reference		
 Yes	2.858 (2.152–3.796)	<.001	1.563 (1.084–2.254)	.017	
PLT	1.001 (0.999–1.003)	.361			
ALT	1.045 (1.033–1.057)	<.001	0.992 (0.965–1.019)	.555	
AST	1.037 (1.020–1.054)	<.001	1.015 (0.985–1.047)	.330	
ALT/AST ratio	6.536 (4.404–9.700)	<.001	3.657 (1.534–8.718)	.003	
UA	1.006 (1.004–1.007)	<.001	1.003 (1.001–1.005)	.005	
FBG	1.443 (1.286–1.618)	<.001	1.162 (1.033–1.307)	.012	
TC	1.073 (0.959–1.200)	.217			
TG	1.947 (1.659–2.284)	<.001	1.335 (1.131–1.574)	<.001	
HDL-C	0.186 (0.122–0.281)	<.001	0.720 (0.429–1.207)	.212	
LDL-C	0.969 (0.845–1.110)	.645			
SBP	1.013 (1.005–1.021)	.001	0.992 (0.980–1.003)	.150	
DBP	1.026 (1.014–1.038)	<.001	1.011 (0.995–1.028)	.168	
ALT = alanine aminotransferase, AST = aspartate aminotransferase, BMI = body mass index, DBP = diastolic blood pressure, FBG = fasting blood glucose, HDL-C = high-density lipoprotein cholesterol, LDL-C = low-density lipoprotein cholesterol, NAFLD = nonalcoholic fatty liver disease, PLT = platelet, SBP = systolic blood pressure, TC = total cholesterol, TG = triglyceride, UA = uric acid.

3.3. Dose–response relationship between continuous variables and risk of NAFLD

RCS indicated a nonlinear relationship between the risk of NAFLD and variables, including the ALT/AST ratio, FPG, TG, and UA (P for nonlinearity < .01, Fig. 2). A drastic increase in NAFLD risk was observed with an ALT/AST ratio >0.7. Similarly, an UA level of <360 μmol/L had a minimal impact on the risk of NAFLD. However, when the UA level exceeded 360 μmol/L, the risk of NAFLD significantly increased with increasing UA levels.

Figure 2. Dose–response relationship between continuous variables and risk of NAFLD. (A) Dose–response relationship between ALT/AST ratio and risk of NAFLD. (B) Dose–response relationship between FBG and risk of NAFLD. (C) Dose–response relationship between TG and risk of NAFLD. (D) Dose–response relationship between UA and risk of NAFLD.

3.4. Construction and validation of the nomogram

On the basis of the multivariate logistic model, the predictive accuracy of each predictor alone was assessed separately with ROC analysis. As shown in Figure 3, TG achieved the highest AUC of 0.710 (95% CI: 0.680–0.741), and it was significantly better than other predictors. On the basis of the 6 aforementioned independent indicators, a nomogram model was constructed to estimate the risk of NAFLD (Fig. 4). The individual points for each measured variable can be determined by aligning them vertically with the top reference point line. The total points can then be calculated by summing all the individual points. Subsequently, the risk of NAFLD can be assessed by aligning the total points vertically with the risk line. Meanwhile, the ROC curve was used to evaluate the discriminatory performance of the nomogram. The AUC value was 0.790, indicating that the nomogram prediction model exhibited significant discriminatory accuracy. Furthermore, the calibration curves were close to 45°, indicating strong agreement between the projected outcomes and the actual measurements (Fig. 5A and B).

Figure 3. Evaluation of the predictive accuracy of each indicator by using ROC analysis.

Figure 4. Nomogram for predicting NAFLD.

Figure 5. Validation of nomogram for predicting risk in patients with NAFLD. (A) Discrimination plot. (B) Calibration plot of nomogram for risk in patients with NAFLD.

4. Discussion

In this study, a novel clinical model to predict the risk of NAFLD was developed. The increasing prevalence of NAFLD worldwide has prompted the need for reliable risk prediction models that can aid in the early identification and management of this condition. This model incorporates a range of clinical and laboratory parameters to identify individuals at high risk of developing NAFLD, thus allowing for early intervention and personalized management strategies.

The research findings indicated that male patients and individuals with elevated BMI; diabetes; and high ALT/AST ratio, UA, FBG, and TG may be at an increased risk of developing NAFLD. Taken individually, clinical factors, such as BMI, diabetes, ALT/AST ratio, UA, FBG, and TG, do not possess sufficient predictive value for NAFLD to be considered clinically useful. Thus, a nomogram model with high predictive accuracy was developed to overcome these limitations. The inclusion of diverse parameters, such as BMI, diabetes, ALT/AST ratio, UA, FBG, and TG, in the nomogram model reflects the multifactorial nature of NAFLD pathogenesis and risk prediction. The nomogram demonstrated a predictive accuracy of 0.790 for assessing the risk of NAFLD. The variables included in the nomogram of this study are easily obtainable, all of which are commonly collected parameters in medical facilities. In this study, logistic models were used to evaluate the risk factors of NAFLD, and RCS was used to clarify the nonlinear relationship. The results showed that the ALT/AST ratio, TG, FPG, and UA were nonlinearly correlated with NAFLD. Specifically, a nomogram was developed to identify patients at an increased risk of developing NAFLD, enabling early and targeted preventive interventions. The results of this research may be used to target particular preventative efforts toward those at the highest risk. The ability of the model to identify NAFLD was validated by the calibration findings.

Obesity is associated with both the onset and progression of NAFLD.[23] An observational study demonstrated a significant association between BMI and the occurrence of NAFLD, identifying BMI as a critical predictive risk factor for NAFLD.[24] Excessive accumulation of adipose tissue in obesity leads to an increased flux of free fatty acids to the liver. These free fatty acids can be converted into toxic lipid metabolites within the hepatocytes, fostering liver injury and the progression to fibrosis.[25] Moreover, adipose tissue, especially visceral fat, secretes various pro-inflammatory cytokines, including tumor necrosis factor-alpha and interleukin-6, which can exert direct pro-inflammatory effects on the liver, further exacerbating liver pathology.[26] The relationship between obesity and NAFLD is multifactorial, involving insulin resistance, oxidative stress, dysregulated lipid metabolism, and the activation of pro-inflammatory pathways.[27] Aligned with prior studies, the findings of the present study suggest that BMI is an independent risk factor for NAFLD. The inclusion of BMI in the predictive model for NAFLD provided a reliable and accurate parameter.

NAFLD and diabetes are closely related, with extensive research demonstrating that the presence of diabetes considerable increases the risk of developing NAFLD. In patients with diabetes, the prevalence of NAFLD can be as high as 70%, indicating a substantial overlap between these 2 conditions.[28] The pathophysiological mechanisms linking NAFLD and diabetes are complex and multifactorial, involving insulin resistance as a key component. Insulin resistance promotes increased hepatic fat accumulation, which is a hallmark of NAFLD, and exacerbates peripheral glucose intolerance, which is a defining feature of diabetes.[29] Furthermore, NAFLD is not only considered a complication of diabetes but also contributes to the progression of insulin resistance and the worsening of glycemic control, thereby perpetuating a vicious cycle between the 2 diseases.[30] The present study revealed that diabetes is a risk factor for the onset of NAFLD. Consequently, the consensus on the necessity of screening and monitoring NAFLD in patients with diabetes is growing. Effective management of diabetes, including maintaining glycemic control and mitigating insulin resistance, is crucial for the prevention and treatment of NAFLD in this patient population.[31]

The relationship between NAFLD and serum levels of ALT and AST is well-documented and clinically significant.[32] Elevated ALT and AST levels are frequently observed in individuals with NAFLD, serving as noninvasive biochemical markers indicative of liver cell injury and hepatocellular inflammation.[33] Notably, while mild to moderate elevations in these enzymes are common in NAFLD, normal ALT and AST levels do not preclude the presence of the disease, indicating that a comprehensive clinical assessment is necessary for an accurate diagnosis. In this study, the higher AST/ALT ratio was associated with an increased risk of NAFLD. This result aligns with a previous research finding, contributing to supporting the association between elevated ALT/AST ratio and NAFLD.[34] The mechanistic implications of this correlation underscore the potential of the ALT/AST ratio as a clinically relevant tool for risk stratification and monitoring disease progression in individuals with NAFLD.

Recent studies have elucidated a compelling correlation between NAFLD and elevated UC levels, suggesting a shared pathophysiological underpinning.[35–37] The interplay between NAFLD and hyperuricemia is postulated to be multifactorial, involving insulin resistance, oxidative stress, and the renin–angiotensin system, which are pathways known to influence both conditions.[38] Thus, the present research, in line with previous studies, supports the notion of a positive relationship between NAFLD and UC. This notion reinforces the need for further clinical studies to explore UA as a potential therapeutic target in NAFLD management. In addition, NAFLD and dyslipidemia are intricately connected, with particular attention given to the role of TG in the pathogenesis and progression of NAFLD. The association between NAFLD and TG can be attributed to insulin resistance, which is a cause and consequence of NAFLD, promoting the hepatic uptake of free fatty acids and their conversion into TG.[39]

In recent years, the epidemiological burden of liver diseases associated with metabolic disorders has been steadily increasing worldwide across all age groups.[40,41] The definition of NAFLD has evolved from the traditional term “NAFLD” to “Metabolic Dysfunction-Associated Fatty Liver Disease” and further to “Metabolic Dysfunction-Associated Steatohepatitis”. The definition of Metabolic Dysfunction-Associated Fatty Liver Disease requires evidence of metabolic dysregulation such as obesity, type 2 diabetes, or other metabolic imbalances.[42] Metabolic Dysfunction-Associated Steatohepatitis, a more specific stage of liver disease, considers severe disease states closely related to metabolic dysfunction.[43] This evolution in terminology reflects a deeper understanding of NAFLD, acknowledging its close association with metabolic syndromes, including diabetes, obesity, and dyslipidemia.[44] These updates have broadened the diagnostic scope, highlighting the multifactorial nature of the disease and its position within the spectrum of systemic metabolic disorders. Future research should focus on developing and validating new noninvasive diagnostic tools to accurately differentiate between the stages of the disease and tailor interventions to individual metabolic profiles.

This study has several limitations. First, its retrospective, single-center, cross-sectional design inherently carries a degree of selection bias. Second, the number of patients in the present study is insufficient. Therefore, future studies should further verify the results by expanding the sample size. Third, correlations among dietary habits, physical activity, and genetic factors were not determined due to raw data limitations. Thus, prospective basic and clinical studies are required to confirm these causal relationships. Finally, the raw data did not include liver fat content classified as mild, moderate, or severe hepatic steatosis. Therefore, multicenter, long-term follow-up studies are required for further external validation in the future.

In conclusion, this investigation delineated several significant risk factors for NAFLD: BMI, diabetes, ALT/AST ratio, UA, FBG, and TG: which collectively underscore the complexity of this condition. By integrating these variables, a robust predictive model that quantitatively estimates the likelihood of NAFLD was constructed, with a high degree of accuracy. However, while the model demonstrates promising utility in a clinical setting, its applicability is restricted by the inherent limitations of the retrospective design and single-center data source. Therefore, prospective multicenter studies are needed to validate and potentially enhance the predictive power of the model. Such efforts may facilitate early intervention strategies and personalized management approaches tailored to individual risk profiles, ultimately contributing to the reduction of NAFLD burden worldwide.

Acknowledgments

The authors would like to thank the investigators of Tongji Medical College of Huazhong University of Science and Technology for sharing their data.

Author contributions

Conceptualization: Xiang Zhong.

Data curation: Xiang Zhong.

Formal analysis: Xiang Zhong.

Funding acquisition: Xiang Zhong.

Investigation: Xiang Zhong.

Methodology: Xiang Zhong.

Project administration: Bo Yang.

Resources: Bo Yang.

Software: Bo Yang.

Supervision: Bo Yang.

Validation: Bo Yang.

Visualization: Bo Yang.

Writing – original draft: Bo Yang.

Writing – review & editing: Bo Yang.

Abbreviations:

ALT alanine aminotransferase

AST aspartate aminotransferase

AUC area under the curve

BMI body mass index

CI confidence intervals

DBP diastolic blood pressure

FBG fasting blood glucose

HDL-C high-density lipoprotein cholesterol

LDL-C low-density lipoprotein cholesterol

NAFLD nonalcoholic fatty liver disease

RCS restricted cubic spline

ROC receiver operating characteristic

SBP systolic blood pressure

TG triglyceride

UA uric acid

Ethics approval and consent was obtained from Dryad database.

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Yang B, Zhong X. Clinical model to predict the risk of nonalcoholic fatty liver disease: A secondary analysis of data from a cross-sectional study. Medicine 2024;103:36(e39437).
==== Refs
References

[1] Powell EE Wong VW Rinella M . Non-alcoholic fatty liver disease. Lancet. 2021;397 :2212–24.33894145
[2] Cotter TG Rinella M . Nonalcoholic fatty liver disease 2020: the state of the disease. Gastroenterology. 2020;158 :1851–64.32061595
[3] Fan JG Kim SU Wong VW . New trends on obesity and NAFLD in Asia. J Hepatol. 2017;67 :862–73.28642059
[4] Loomba R Lim JK Patton H El-Serag HB . AGA clinical practice update on screening and surveillance for hepatocellular carcinoma in patients with nonalcoholic fatty liver disease: expert review. Gastroenterology. 2020;158 :1822–30.32006545
[5] Le MH Le DM Baez TC . Global incidence of non-alcoholic fatty liver disease: A systematic review and meta-analysis of 63 studies and 1,201,807 persons. J Hepatol. 2023;79 :287–95.37040843
[6] Chen K Ma J Jia X Ai W Ma Z Pan Q . Advancing the understanding of NAFLD to hepatocellular carcinoma development: from experimental models to humans. Biochim Biophys Acta Rev Cancer. 2019;1871 :117–25.30528647
[7] Abenavoli L Montori M Svegliati Baroni G . Perspective on the role of gut microbiome in the treatment of hepatocellular carcinoma with immune checkpoint inhibitors. Medicina (Kaunas). 2023;59 :1427.37629716
[8] Manne V Handa P Kowdley KV . Pathophysiology of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. Clin Liver Dis. 2018;22 :23–37.29128059
[9] Ekstedt M Hagstrom H Nasr P . Fibrosis stage is the strongest predictor for disease-specific mortality in NAFLD after up to 33 years of follow-up. Hepatology. 2015;61 :1547–54.25125077
[10] Friedman SL Neuschwander-Tetri BA Rinella M Sanyal AJ . Mechanisms of NAFLD development and therapeutic strategies. Nat Med. 2018;24 :908–22.29967350
[11] Booth FW Roberts CK Laye MJ . Lack of exercise is a major cause of chronic diseases. Compr Physiol. 2012;2 :1143–211.23798298
[12] Shyangdan D Clar C Ghouri N . Insulin sensitisers in the treatment of non-alcoholic fatty liver disease: a systematic review. Health Technol Assess. 2011;15 :1–110.
[13] Kechagias S Ekstedt M Simonsson C Nasr P . Non-invasive diagnosis and staging of non-alcoholic fatty liver disease. Hormones (Athens). 2022;21 :349–68.35661987
[14] Bravo AA Sheth SG Chopra S . Liver biopsy. N Engl J Med. 2001;344 :495–500.11172192
[15] van der Poorten D Kwok A Lam T . Twenty-year audit of percutaneous liver biopsy in a major Australian teaching hospital. Intern Med J. 2006;36 :692–9.17040353
[16] Gawrieh S Knoedler DM Saeian K Wallace JR Komorowski RA . Effects of interventions on intra- and interobserver agreement on interpretation of nonalcoholic fatty liver disease histology. Ann Diagn Pathol. 2011;15 :19–24.21106424
[17] Tarantino G Finelli C . What about non-alcoholic fatty liver disease as a new criterion to define metabolic syndrome? World J Gastroenterol. 2013;19 :3375–84.23801829
[18] Lee SS Park SH . Radiologic evaluation of nonalcoholic fatty liver disease. World J Gastroenterol. 2014;20 :7392–402.24966609
[19] Lee SS Park SH Kim HJ . Non-invasive assessment of hepatic steatosis: prospective comparison of the accuracy of imaging examinations. J Hepatol. 2010;52 :579–85.20185194
[20] Campos GM Bambha K Vittinghoff E . A clinical scoring system for predicting nonalcoholic steatohepatitis in morbidly obese patients. Hepatology. 2008;47 :1916–23.18433022
[21] Palekar NA Naus R Larson SP Ward J Harrison SA . Clinical model for distinguishing nonalcoholic steatohepatitis from simple steatosis in patients with nonalcoholic fatty liver disease. Liver Int. 2006;26 :151–6.16448452
[22] Yan F Nie G Zhou N Zhang M Peng W . Association of fat-to-muscle ratio with non-alcoholic fatty liver disease: a single-centre retrospective study. BMJ Open. 2023;13 :e072489.
[23] Miyake T Kumagi T Hirooka M . Body mass index is the most useful predictive factor for the onset of nonalcoholic fatty liver disease: a community-based retrospective longitudinal cohort study. J Gastroenterol. 2013;48 :413–22.22933183
[24] Loomis AK Kabadi S Preiss D . Body mass index and risk of nonalcoholic fatty liver disease: two electronic health record prospective studies. J Clin Endocrinol Metab. 2016;101 :945–52.26672639
[25] Lomonaco R Sunny NE Bril F Cusi K . Nonalcoholic fatty liver disease: current issues and novel treatment approaches. Drugs. 2013;73 :1–14.23329465
[26] Hotamisligil GS . Inflammation and metabolic disorders. Nature. 2006;444 :860–7.17167474
[27] Fabbrini E Sullivan S Klein S . Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications. Hepatology. 2010;51 :679–89.20041406
[28] Younossi ZM Koenig AB Abdelatif D Fazel Y Henry L Wymer M . Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64 :73–84.26707365
[29] Cusi K . Role of insulin resistance and lipotoxicity in non-alcoholic steatohepatitis. Clin Liver Dis. 2009;13 :545–63.19818304
[30] Tilg H Moschen AR . Insulin resistance, inflammation, and non-alcoholic fatty liver disease. Trends Endocrinol Metab. 2008;19 :371–9.18929493
[31] Chalasani N Younossi Z Lavine JE . The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American Association for the Study of Liver Diseases. Hepatology. 2018;67 :328–57.28714183
[32] Liao Y Liu L Yang J . Analysis of clinical features and identification of risk factors in patients with non-alcoholic fatty liver disease based on FibroTouch. Sci Rep. 2023;13 :14812.37684380
[33] Clark JM Brancati FL Diehl AM . The prevalence and etiology of elevated aminotransferase levels in the United States. Am J Gastroenterol. 2003;98 :960–7.12809815
[34] Gawrieh S Wilson LA Cummings OW . Histologic findings of advanced fibrosis and cirrhosis in patients with nonalcoholic fatty liver disease who have normal aminotransferase levels. Am J Gastroenterol. 2019;114 :1626–35.31517638
[35] Liu Z Que S Zhou L Zheng S . Dose-response relationship of serum uric acid with metabolic syndrome and non-alcoholic fatty liver disease incidence: a meta-analysis of prospective studies. Sci Rep. 2015;5 :14325.26395162
[36] Xu C Yu C Xu L Miao M Li Y . High serum uric acid increases the risk for nonalcoholic fatty liver disease: a prospective observational study. PLoS One. 2010;5 :e11578.20644649
[37] Sirota JC McFann K Targher G Johnson RJ Chonchol M Jalal DI . Elevated serum uric acid levels are associated with non-alcoholic fatty liver disease independently of metabolic syndrome features in the United States: liver ultrasound data from the National Health and Nutrition Examination Survey. Metabolism. 2013;62 :392–9.23036645
[38] Lonardo A Ballestri S Marchesini G Angulo P Loria P . Nonalcoholic fatty liver disease: a precursor of the metabolic syndrome. Dig Liver Dis. 2015;47 :181–90.25739820
[39] Kawano Y Cohen DE . Mechanisms of hepatic triglyceride accumulation in non-alcoholic fatty liver disease. J Gastroenterol. 2013;48 :434–41.23397118
[40] Portincasa P Khalil M Mahdi L . Metabolic dysfunction-associated steatotic liver disease: from pathogenesis to current therapeutic options. Int J Mol Sci . 2024;25 :5640.38891828
[41] Benedict M Zhang X . Non-alcoholic fatty liver disease: an expanded review. World J Hepatol. 2017;9 :715–32.28652891
[42] Eslam M Sanyal AJ George J . MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology. 2020;158 :1999–2014.e1.32044314
[43] Rinella ME Lazarus JV Ratziu V . A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78 :1966–86.37363821
[44] Lin S Huang J Wang M . Comparison of MAFLD and NAFLD diagnostic criteria in real world. Liver Int. 2020;40 :2082–9.32478487
