PanNASH covers commented articles ans publications to acquire sufficient information, understanding, and skills to cope with NASH/NAFLD healthcare demands.
The spectrum of non-alcoholic fatty liver disease (NAFLD) is divisible into three stages: 1) NAFL, 2) non-alcoholic steatohepatitis (NASH), NAFLD’s progressive and inflammatory subtype, and 3) NAFLD or NASH with fibrosis, whose stages range from F0-4.
Hepatic microvascular dysfunction is central to the development and progression of nonalcoholic steatohepatitis (NASH), the inflammatory subtype of nonalcoholic fatty liver disease (NAFLD).
Along with its association with metabolic syndrome, NAFLD is closely linked with mixed dyslipidaemia (reduced high-density lipoprotein cholesterol (HDL-C) and increased very low-density lipoprotein triglycerides (VLDL-TG)).
Up to 30% of patients with non-alcoholic fatty liver disease (NAFLD) progress to non-alcoholic steatohepatitis (NASH), significantly increasing their risk of fibrosis and associated adverse outcomes. As such, ensuring the early identification of NAFLD and NASH in at-risk patients is a clinical priority.
Changing the terminology from NAFLD, non-alcoholic fatty liver disease, to MAFLD, metabolic dysfunction-associated fatty liver disease, has been an ongoing debate. MAFLD is based upon an individual having hepatic steatosis, as well as either type 2 diabetes mellitus, obesity (or being overweight) or if at a normal weight, two or more metabolic risk abnormalities.
Patients with non-alcoholic fatty liver disease (NAFLD) are at risk of progressing to inflammatory non-alcoholic steatohepatitis (NASH), a NAFLD subtype associated with fibrosis, cirrhosis, and hepatocellular carcinoma.
NASHmap, a non-invasive machine learning model, is revolutionizing NASH identification. Utilizing 14 clinical variables, it offers a promising alternative to invasive liver biopsies. With an 81% positive predictive value, NASHmap can potentially improve clinical practice and specialist referrals for high-risk patients.
Proteo-transcriptomic analyses offer a promising approach for precise NASH risk assessment. A predictive model based on protein levels and clinical variables demonstrates 79% accuracy. This innovative method holds potential for developing blood-based diagnostic tools. Further testing in diverse populations is necessary to validate its efficacy.
Seeking the perfect NASH biomarker may be unrealistic; versatility is key. A diverse biomarker pool adapts to varied clinical contexts and settings. FIB-4, a cost-effective marker, can be supplemented by complex multimarkers for NASH therapeutic trials. Embracing diagnostic imperfections is crucial for flexible biomarker algorithms.
NAFLD's prevalence in IBD patients raises concerns for cardiovascular risk. The study found doubled atherosclerotic cardiovascular disease risk in IBD patients with NAFLD. Transient elastography may predict cardiovascular risk in IBD patients. Targeted risk assessments are crucial for IBD patients with NAFLD.
The rising healthcare costs of NASH highlight the need for early intervention. Higher FIB-4 scores correlate with increased healthcare expenses. Early-stage intervention can reduce the burden on patients and the healthcare system.
Heterogeneity in cut-off points for non-invasive fibrosis tests impacts NAFLD risk stratification. Standardized guidelines could improve consistency and accuracy in identifying patients requiring specialist care.