Association between High-Density Lipoprotein Cholesterol to Apolipoprotein A-I Ratio and Nonalcoholic Fatty Liver Disease: A Cross-Sectional Study

Estimated small dense low-density lipoprotein cholesterol and nonalcoholic fatty liver disease in nonobese populations

AIMS/INTRODUCTION

To explore the association between estimated small dense low-density lipoprotein cholesterol (sdLDL-C) and the risk of incident nonalcoholic fatty liver disease (NAFLD) in nonobese populations.

MATERIALS AND METHODS

This study included participants who underwent health checkups in 2014 and were followed up until 2019. We carried out Cox proportional hazards regression analyses to evaluate the association of estimated sdLDL-C with NAFLD. Discordance analyses were carried out to estimate the relative NAFLD risk in estimated sdLDL-C versus low-density lipoprotein cholesterol (LDL-C) discordant/concordant groups. Estimated sdLDL-C was calculated by equations based on LDL-C and triglycerides. The diagnosis of NAFLD was based on the presence of abdominal ultrasonography after excluding other causes of chronic liver disease.

RESULTS

Over a mean follow-up period of 26,694 person-years, 844 incident NAFLD cases were recorded. Compared with the first quartile of estimated sdLDL-C, the fourth quartile was associated with a 2.933-fold increased risk of NAFLD (95% confidence interval 2.095-4.107). With the increase in estimated sdLDL-C, the risk of NAFLD gradually increased both in participants within the normal range of LDL-C (hazard ratio 2.854, 95% confidence interval 1.650-5.617) and beyond the normal range of LDL-C (hazard ratio 2.636, 95% confidence interval 1.263-5.502). In addition, the inconsistent high estimated sdLDL-C/low LDL-C group was associated with an increased risk of NAFLD, but not the low estimated sdLDL-C/high LDL-C group.

CONCLUSIONS

Estimated sdLDL-C was positively associated with the risk of incident NAFLD in a nonobese population, independent of LDL-C.

A simpler definition of MAFLD precisely predicts incident metabolic diseases: a 7-year cohort study

BACKGROUND

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a novel definition proposed in 2020 with a relatively complex set of criteria. Thus, simplified criteria that are more applicable are required. This study aimed to develop a simplified set of criteria for identifying MAFLD and predicting MAFLD-related metabolic diseases.

METHODS

We developed a simplified set of metabolic syndrome-based criteria for MAFLD, and compared the performance of the simplified criteria with that of the original criteria in predicting MAFLD-related metabolic diseases in a 7-year follow-up.

RESULTS

In the 7-year cohort, a total of 13,786 participants, including 3372 (24.5%) with fatty liver, were enrolled at baseline. Of the 3372 participants with fatty liver, 3199 (94.7%) met the MAFLD-original criteria, 2733 (81.0%) met the simplified criteria, and 164 (4.9%) were metabolic healthy and met neither of the criteria. During 13,612 person-years of follow-up, 431 (16.0%) fatty liver individuals newly developed T2DM, with an incidence rate of 31.7 per 1000 person-years. Participants who met the simplified criteria had a higher risk of incident T2DM than those who met the original criteria. Similar results were observed for incident hypertension, and incident carotid atherosclerotic plaque.

CONCLUSION

The MAFLD-simplified criteria are an optimized risk stratification tool for predicting metabolic diseases in fatty liver individuals.

Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population

Purpose

This study aimed to identify independent risk factors for carotid atherosclerosis (CAS) and construct and validate a CAS risk prediction model based on the Chinese population.

Methods

This retrospective study included 4,570 Chinese adults who underwent health checkups (including carotid ultrasound) at the Zhenhai Lianhua Hospital, Ningbo, China, in 2020. All the participants were randomly assigned to the training and validation sets at a ratio of 7:3. Independent risk factors associated with CAS were identified using multivariate logistic regression analysis. The least absolute shrinkage and selection operator combined with 10-fold cross-validation were screened for characteristic variables, and nomograms were plotted to demonstrate the risk prediction model. C-index and receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the risk model’s discrimination, calibration, and clinical applicability.

Results

Age, body mass index, diastolic blood pressure, white blood cell count, mean platelet volume, alanine transaminase, aspartate transaminase, and gamma-glutamyl transferase were identified as independent risk factors for CAS. In the training, internal validation, and external validation sets, the risk model showed good discriminatory power with C-indices of 0.961 (0.953–0.969), 0.953 (0.939–0.967), and 0.930 (0.920–0.940), respectively, and excellent calibration. The results of DCA showed that the prediction model could be beneficial when the risk threshold probabilities were 1–100% in all sets. Finally, a network computer (dynamic nomogram) was developed to facilitate the physicians’ clinical operations. The website is https://nbuhgq.shinyapps.io/DynNomapp/.

Conclusion

The development of risk models contributes to the early identification and prevention of CAS, which is important for preventing and reducing adverse cardiovascular and cerebrovascular events.