Fatty Liver as Potential Biomarker of Atherosclerotic Damage in Familial Combined Hyperlipidemia

Altered lipid metabolism and the development of metabolic-associated fatty liver disease

PURPOSE OF REVIEW

An increasing amount of research has underscored the significant role of lipoproteins in the pathogenesis of metabolic-associated fatty liver disease (MAFLD). This comprehensive review examines the intricate relationship between lipoprotein abnormalities and the development of MAFLD.

RECENT FINDINGS

Atherogenic dyslipidemia seen in insulin resistance states play a significant role in initiating and exacerbating hepatic lipid accumulation. There are also specific genetic factors ( PNPLA3 , TM6SF2 , MBOAT7 , HSD17B13 , GCKR- P446L) and transcription factors (SREBP-2, FXR, and LXR9) that increase susceptibility to both lipoprotein disorders and MAFLD. Most monogenic primary lipid disorders do not cause hepatic steatosis unless accompanied by metabolic stress. Hepatic steatosis occurs in the presence of secondary systemic metabolic stress in conjunction with predisposing environmental factors that lead to insulin resistance. Identifying specific aberrant lipoprotein metabolic factors promoting hepatic fat accumulation and subsequently exacerbating steatohepatitis will shed light on potential targets for therapeutic interventions.

SUMMARY

The clinical implications of interconnection between genetic factors and an insulin resistant environment that predisposes MAFLD is many fold. Potential therapeutic strategies in preventing or mitigating MAFLD progression include lifestyle modifications, pharmacological interventions, and emerging therapies targeting aberrant lipoprotein metabolism.

Exploring the Multifaceted Landscape of MASLD: A Comprehensive Synthesis of Recent Studies, from Pathophysiology to Organoids and Beyond

Non-alcoholic fatty liver disease (NAFLD) is a widespread contributor to chronic liver disease globally. A recent consensus on renaming liver disease was established, and metabolic dysfunction-associated steatotic liver disease, MASLD, was chosen as the replacement for NAFLD. The disease's range extends from the less severe MASLD, previously known as non-alcoholic fatty liver (NAFL), to the more intense metabolic dysfunction-associated steatohepatitis (MASH), previously known as non-alcoholic steatohepatitis (NASH), characterized by inflammation and apoptosis. This research project endeavors to comprehensively synthesize the most recent studies on MASLD, encompassing a wide spectrum of topics such as pathophysiology, risk factors, dietary influences, lifestyle management, genetics, epigenetics, therapeutic approaches, and the prospective trajectory of MASLD, particularly exploring its connection with organoids.

Whole grain germinated brown rice intake modulates the gut microbiota and alleviates hypertriglyceridemia and hypercholesterolemia in high fat diet-fed mice

Hyperlipidemia is a common clinical disorder of lipid metabolism in modern society and is considered to be one of the major risk factors leading to cardiovascular-related diseases. Germinated brown rice (GBR) is a typical whole grain food. The lipid-lowering effect of GBR has received increasing attention, but its mechanism of action is not fully understood. The gut microbiota has been proposed as a novel target for the treatment of hyperlipidemia. The aim of this study was to investigate the effects of GBR on the gut microbiota and lipid metabolism in high-fat diet (HFD)-fed C57BL/6J mice. The effect of GBR on hyperlipidemia was evaluated by measuring blood lipid levels and by pathological examination. The gut microbiota was detected by 16S rRNA sequencing, and the protein and mRNA expression levels involved in cholesterol metabolism were detected by western blotting and RT-qPCR to find potential correlations. The results showed that GBR supplementation could effectively reduce the levels of TC, TG, LDL-C and HDL-C in the serum and alleviate the excessive accumulation of fat droplets caused by HFD. Moreover, GBR intervention improved HFD-fed gut microbiota disorder via increasing the diversity of the gut microbiota, reducing the Firmicutes/Bacteroidetes ratio, and improving gut barrier damage. In addition, GBR could inhibit endogenous cholesterol synthesis and promote cholesterol transport and excretion. These findings suggest that GBR may be a competitive candidate for the development of functional foods to prevent abnormal lipid metabolism.

Combinatorial Use of Machine Learning and Logistic Regression for Predicting Carotid Plaque Risk Among 5.4 Million Adults With Fatty Liver Disease Receiving Health Check-Ups: Population-Based Cross-Sectional Study

BACKGROUND

Carotid plaque can progress into stroke, myocardial infarction, etc, which are major global causes of death. Evidence shows a significant increase in carotid plaque incidence among patients with fatty liver disease. However, unlike the high detection rate of fatty liver disease, screening for carotid plaque in the asymptomatic population is not yet prevalent due to cost-effectiveness reasons, resulting in a large number of patients with undetected carotid plaques, especially among those with fatty liver disease.

OBJECTIVE

This study aimed to combine the advantages of machine learning (ML) and logistic regression to develop a straightforward prediction model among the population with fatty liver disease to identify individuals at risk of carotid plaque.

METHODS

Our study included 5,420,640 participants with fatty liver from Meinian Health Care Center. We used random forest, elastic net (EN), and extreme gradient boosting ML algorithms to select important features from potential predictors. Features acknowledged by all 3 models were enrolled in logistic regression analysis to develop a carotid plaque prediction model. Model performance was evaluated based on the area under the receiver operating characteristic curve, calibration curve, Brier score, and decision curve analysis both in a randomly split internal validation data set, and an external validation data set comprising 32,682 participants from MJ Health Check-up Center. Risk cutoff points for carotid plaque were determined based on the Youden index, predicted probability distribution, and prevalence rate of the internal validation data set to classify participants into high-, intermediate-, and low-risk groups. This risk classification was further validated in the external validation data set.

RESULTS

Among the participants, 26.23% (1,421,970/5,420,640) were diagnosed with carotid plaque in the development data set, and 21.64% (7074/32,682) were diagnosed in the external validation data set. A total of 6 features, including age, systolic blood pressure, low-density lipoprotein cholesterol (LDL-C), total cholesterol, fasting blood glucose, and hepatic steatosis index (HSI) were collectively selected by all 3 ML models out of 27 predictors. After eliminating the issue of collinearity between features, the logistic regression model established with the 5 independent predictors reached an area under the curve of 0.831 in the internal validation data set and 0.801 in the external validation data set, and showed good calibration capability graphically. Its predictive performance was comprehensively competitive compared with the single use of either logistic regression or ML algorithms. Optimal predicted probability cutoff points of 25% and 65% were determined for classifying individuals into low-, intermediate-, and high-risk categories for carotid plaque.

CONCLUSIONS

The combination of ML and logistic regression yielded a practical carotid plaque prediction model, and was of great public health implications in the early identification and risk assessment of carotid plaque among individuals with fatty liver.

Nonlinear correlation between fatty liver index and carotid intima media thickness among individuals undergoing health examination

BACKGROUND

Fatty liver index (FLI) is a predictor of non-alcohol fatty liver disease (NAFLD). This study aimed to assess the association between FLI and carotid intima media thickness (CIMT).

METHODS

In this cross-sectional study, we enrolled 277 individuals for health examination from the China-Japan Friendship Hospital. Blood sampling and ultrasound examinations were conducted. Multivariate logistic regression and restricted cubic spline analyses were performed to evaluate the association between FLI and CIMT.

RESULTS

Overall, 175 (63.2%) and 105 (37.9%) individuals had NAFLD and CIMT, respectively. The multivariate logistic regression analyses results showed that high FLI was independently associated with a high risk of increased CIMT, T2 vs. T1 (odds ratio [OR], 95% confidence interval [CI]): 2.41, 1.10-5.25, p = 0.027; T3 vs. T1 (OR, 95% CI): 1.58, 0.68-3.64, p = 0.285. The association between FLI and increased CIMT exhibited a J-shaped curve (nonlinear, p = 0.019). In the threshold analysis, the OR for developing increased CIMT was 1.031 (95% CI: 1.011-1.051, p = 0.0023) in participants with FLI < 64.247.

CONCLUSION

The relationship between FLI and increased CIMT in the health examination population is J-shaped, with an inflection point of 64.247.