Metabolism: clinical and experimental最新文献

Hepatocellular carcinoma (HCC) is a relevant complication occurring in individuals with advanced Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD). Recent epidemiological data suggest an alarming increase in the HCC burden worldwide, with a relevant proportion attributable to MASLD (up to 38 %), either in cirrhotic or non-cirrhotic livers. In view of the changing landscape of metabolic syndrome as "silent pandemic", this narrative review aims to provide an updated picture of the burden of HCC in individuals with MASLD. In the complex pathophysiological pathways linking insulin resistance to MASLD and cardiometabolic syndrome, metabolic inflammation appears a relevant driver of systemic as well as organ-specific complications. Novel insights from the field of immunology, gut-derived liver damage, and association with extra-hepatic cancers will be discussed. Finally, strategies for risk-based HCC surveillance (circulating biomarkers, prognostic models and polygenic risk scores) will be provided and the potential impact of novel drug targeting fibrosing Metabolic dysfunction-Associated Steatohepatitis (MASH) on incident HCC will be discussed.

Background: There are no known non-invasive tests (NITs) designed for accurately detecting metabolic dysfunction-associated steatohepatitis (MASH) with liver fibrosis stages F2-F3, excluding cirrhosis-the FDA-defined range for prescribing Resmetirom and other drugs in clinical trials. We aimed to validate and re-optimize known NITs, and most importantly to develop new machine learning (ML)-based NITs to accurately detect MASH F2-F3.

Methods: Clinical and metabolomic data were collected from 443 patients across three countries and two clinic types (metabolic surgery, gastroenterology/hepatology) covering the entire spectrum of biopsy-proven MASLD, including cirrhosis and healthy controls. Three novel types of ML models were developed using a categorical gradient boosting machine pipeline. These were compared with 24 biomarker, imaging, and algorithm-based NITs with both known cutoffs for MASH F2-F4 and re-optimized cutoffs for MASH F2-F3.

Results: NFS at a - 1.455 cutoff attained an AUC of 0.59, the highest sensitivity (90.9 %, 95 % CI 84.3-95.4), and NPV of 87.2 %. FIB4 risk stratification followed by elastography (8 kPa) had the best specificity (86.9 %) and PPV (63.3 %), with an AUC of 0.57. NFS followed by elastography improved the PPV to 65.3 % and AUC to 0.62. Re-optimized FibroScan-AST (FAST) at a 0.22 cutoff had the highest PPV (69.1 %). ML models using aminotransferases, metabolic syndrome components, BMI, and 3-ureidopropionate achieved an AUC of 0.89, which further increased to 0.91 following hyperparameter optimization and the addition of alpha-ketoglutarate. These new ML models outperformed all other NITs and displayed accuracy, sensitivity, specificity, PPV, and NPV up to 91.2 %, 85.3 %, 97.0 %, 92.4 %, and 90.7 % respectively. The models were reproduced and validated in a secondary sensitivity analysis, that used one of the cohorts as feature selection/training, and the rest as independent validation, likewise outperforming all other NITs.

Conclusions: We report for the first time the diagnostic characteristics of non-invasive, metabolomics-based biomarker models to detect MASH with fibrosis F2-F3 required for Resmetirom treatment and inclusion in ongoing phase-III trials. These models may be used alone or in combination with other NITs to accurately determine treatment eligibility.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a systemic disease characterized by insulin resistance and lipotoxicity. Its association with type 2 diabetes, cardiovascular disease, liver cirrhosis, and hepatocellular carcinoma are well described. However, the association of MASLD and extra-hepatic cancers has received significantly less attention. This narrative review will summarize the conflicting evidence regarding the association between MASLD and cancers of the urinary system, including renal cell carcinoma, urothelial carcinoma, and prostate adenocarcinoma. It will explore potential mechanisms that could be responsible for a higher risk of urinary system cancers in patients with MASLD. We hope that our comprehensive assessment of the literature will help the readers to better interpret the available evidence.

PCSK9 is a serine protease that regulates plasma levels of low-density lipoprotein (LDL) and cholesterol by mediating the endolysosomal degradation of LDL receptor (LDLR) in the liver. When PCSK9 functions unchecked, it leads to increased degradation of LDLR, resulting in elevated circulatory levels of LDL and cholesterol. This dysregulation contributes to lipid and cholesterol metabolism abnormalities, foam cell formation, and the development of various diseases, including cardiovascular disease (CVD), viral infections, cancer, and sepsis. Emerging clinical and experimental evidence highlights an imperative role for PCSK9 in metabolic anomalies such as hypercholesterolemia and hyperlipidemia, as well as inflammation, and disturbances in mitochondrial homeostasis. Moreover, metabolic hormones - including insulin, glucagon, adipokines, natriuretic peptides, and sex steroids - regulate the expression and circulatory levels of PCSK9, thus influencing cardiovascular and metabolic functions. In this comprehensive review, we aim to elucidate the regulatory role of PCSK9 in lipid and cholesterol metabolism, pathophysiology of diseases such as CVD, infections, cancer, and sepsis, as well as its pharmaceutical and non-pharmaceutical targeting for therapeutic management of these conditions.

Background & aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a spectrum of pathologies ranging from simple steatosis to steatohepatitis, fibrosis and cirrhosis. Patients with metabolic associated steatohepatitis (MASH) with and without fibrosis are at greatest risk of liver and cardiovascular complications. To identify such at-risk MASLD patients, physicians are still reliant on invasive liver biopsies. This study aimed to identify circulating lipidomic signatures to better identify patients with MASH in a multi-ethnic Asian cohort.

Approach & results: A lipidomic approach was used to quantify a total of 481 serum lipids from 151 Singaporean patients paired with protocolized liver biopsies. Lipidomic signatures as diagnostic biomarkers for MASLD, MASH and advanced fibrosis were identified. 210 lipids showed significant differences for varying histological subtypes of MASLD. Majority of these lipids were associated with liver steatosis (198/210). We identified a panel of 13 lipids associated with lobular inflammation, ballooning and significant fibrosis. More specifically, dihexosylceramides were novel markers for significant fibrosis. Using the serum lipidome alone, we could stratify patients with MASLD (AUROC 0.863), as well as those with at-risk MASH (AUROC 0.912) and advanced fibrosis (AUROC 0.95). The lipidomic at-risk MASH predictor, using 14 markers, was independently validated (n = 105) with AUROC 0.76.

Conclusions: The dynamic shift in blood lipid profile was associated with progressive histological stages of MASLD, providing surrogate markers for distinguishing stages of MASLD as well as identifying novel pathways in the pathogenesis.