Development and validation of a clinical and laboratory-based nomogram to predict nonalcoholic fatty liver disease

Abstract

Background and aim

Nonalcoholic fatty liver disease (NAFLD) is becoming the leading cause of chronic liver disease in China. The early identification and management of patients at risk are essential. We aimed to develop a novel clinical and laboratory-based nomogram (CLN) model to predict NAFLD with high accuracy.

Methods

We designed a retrospective cross-sectional study and enrolled 21,468 participants (16,468 testing and 5000 validation). Clinical information and laboratory/imaging results were retrieved. Significant variables independently associated with NAFLD were identified by a logistic regression model, and a NAFLD prediction CLN was constructed. The CLN was then compared with four existing NAFLD-related prediction models: the fatty liver index (FLI), the hepatic steatosis index (HSI), the visceral adiposity index (VAI) and the triglycerides and glucose (TyG) index. Area under the receiver operator characteristic curve (AUROC) and decision curve analysis (DCA) were performed.

Results

A total of 6261/16,468 (38.02%) and 1759/5000 (35.18%) participants in the testing and validation datasets, respectively, had ultrasound-proven NAFLD. Six variables were selected to build the CLN: body mass index (BMI), diastolic blood pressure (DBP), uric acid (UA), fasting blood glucose (FBG), triglyceride (TG), and alanine aminotransferase (ALT). The diagnostic accuracy of the CLN for NAFLD (AUROC 0.857, 95% CI 0.852–0.863) was significantly superior to that of the FLI (AUROC 0.849, 95% CI 0.843–0.855), the VAI (AUROC 0.752, 95% CI 0.745–0.760), the HSI (AUROC 0.828, 95% CI 0.822–0.834), and the TyG index (AUROC 0.774, 95% CI 0.767–0.781) (all p < 0.001). DCA confirmed the clinical utility of the CLN.

Conclusions

This physical examination and laboratory test-based, nonimage-assisted novel nomogram has better performance in predicting NAFLD than the FLI, the VAI, the HSI and the TyG index alone. This model can be used as a quick screening tool to assess NAFLD in the general population.

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Abbreviations

CLN:

Clinical and laboratory-based nomogram

BMI:

Body mass index

WC:

Waist circumference

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

HR:

Heart rate

Hb:

Hemoglobin

PLT:

Platelet count

WBC:

White blood cell count

NEU:

Neutrophil count

LYM:

Lymphocyte count

MNC:

Monocyte count

ALB:

Albumin

GLB:

Globulin

UA:

Uric acid

FBG:

Fasting blood glucose

TG:

Triglyceride

TC:

Total cholesterol

LDL-C:

Low-density lipoprotein cholesterol

HDL-C:

High-density lipoprotein cholesterol

AFU:

Alpha fucosidase

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

GGT:

Gamma-glutamyl transpeptidase

ChE:

Cholinesterase

ALP:

Alkaline phosphatase

CEA:

Carcinoembryonic antigen

AFP:

Alpha fetoprotein

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Acknowledgements

This work was supported by research grants from Zhejiang Provincial Medical & Hygienic Science and Technology Project of China (2018KY385). Zhejiang Provincial Natural Science Foundation of China (LY20H160023). The supporting institutions had no involvement in the study design; the collection, analysis and interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication.

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Authors

Contributions

CC and ZSS conceived and designed the research. YSF, TXF and LYX collected samples. ZL and YJ conducted the experiments. WWP and LJM analyzed the data. CC and LJM wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shusen Zheng.

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The authors declare that they have no competing interests.

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The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Zhejiang University School of Medicine.

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Informed consent was obtained from all individual participants included in the study.

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Cen, C., Wang, W., Yu, S. et al. Development and validation of a clinical and laboratory-based nomogram to predict nonalcoholic fatty liver disease. Hepatol Int (2020). https://doi.org/10.1007/s12072-020-10065-7

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Keywords

  • Nonimage assisted
  • Prediction model
  • Healthy population
  • Metabolic syndrome
  • Liver biopsy
  • Early diagnosis and prevention
  • Fatty liver index
  • Hepatic steatosis index
  • Triglycerides and glucose index
  • Visceral adiposity index