👤 Behzad Kiani

Lipoprotein(a) [Lp(a)] is a genetically determined, highly atherogenic lipoprotein that contributes to cardiovascular disease and calcific aortic valve stenosis. Increased Lp(a) levels warrant intensified management of cardiovascular risk factors. With targeted Lp(a)-lowering therapies in clinical development, identification of individuals with increased levels has increasing therapeutic implications. Guidelines differ, recommending testing in either high-risk groups or universally once in a lifetime, yet testing rates remain low. We performed a retrospective analysis of laboratory data from a large tertiary referral centre in Queensland, Australia, evaluating trends in Lp(a) testing between 1 January 2015 and 31 December 2024. Lp(a) testing increased markedly over the 10-year study period. In Queensland, annual test volumes rose from 652 in 2015 to 4,364 in 2024. Including interstate referrals, test numbers increased from 2,686 in 2015 to 23,135 in 2024. The steepest rise occurred in the final 2 years of observation. Despite these increases, testing rates relative to the screened population remained low, and testing generally occurred late in individuals in their 50s. Lp(a) testing has grown substantially in Queensland and Australia over the past decade, likely reflecting increased recognition of its causal role in cardiovascular disease, evolving guideline recommendations, test accessibility, and the emergence of novel therapies. However, overall testing remains limited. Broader implementation of guideline-based testing and greater clinician awareness will be critical to ensure timely identification of individuals who may benefit from available and emerging therapeutic strategies. Show less

During the periparturient period, dairy cows experience negative energy balance due to reduced feed intake, leading to adipose tissue breakdown, liver damage, and fat accumulation. This study examined the gut-liver-brain axis to explore the link between fatty liver disease, changes in hypothalamic appetite-related neurons, and microbiome shifts in dairy cows. Thirty cows were monitored, with daily DMI recordings and blood sampling. Postpartum brain, liver, and ileal contents were collected from 10 selected cows, divided into two groups: H-DMI (slight DMI decrease) and L-DMI (severe DMI decrease). The L-DMI group of cows exhibited higher plasma NEFA, BHBA, ALT, and AST levels, along with severe hepatic steatosis and lipid accumulation. Transcriptome sequencing of the hypothalamic arcuate nucleus (ARC) revealed decreased expression of Hypocretin Neuropeptide Precursor (HCRT), orexin-A (OX-A), Orexin Receptor Type 1 (OX1R), and Cannabinoid Receptor 1 (CB1) in the L-DMI group, while Pro-opiomelanocortin (POMC) and Melanocortin 4 Receptor (MC4R) expression increased. Metagenomic analysis of ileal contents showed reduced abundance of Ruminococcus spp. in the L-DMI group, which may be associated with fatty liver disease (FL). Integrated omics analysis showed that increased MC4R expression was correlated with the elevated abundance of bacteria such as Akkermansia glycaniphila, and reduced abundance of species such as Methanobrevubacter thaueri and Ruminococcus spp. Decreased HCRT expression was also linked to Akkermansia glycaniphila. In conclusion, these changes may affect DMI through the OX-A/POMC pathway, with neurological and gut microbiome alterations potentially leading to appetite suppression, negative energy balance, and the development of fatty liver disease. Show less

Non-alcoholic fatty liver disease (NAFLD) comprises a range of chronic liver diseases that result from the accumulation of excess triglycerides in the liver, and which, in its early phases, is categorized NAFLD, or hepato-steatosis with pure fatty liver. The mortality rate of non-alcoholic steatohepatitis (NASH) is more than NAFLD; therefore, diagnosing the disease in its early stages may decrease liver damage and increase the survival rate. In the current study, we screened the gene expression data of NAFLD patients and control samples from the public dataset GEO to detect DEGs. Then, the correlation betweenbetween the top selected DEGs and clinical data was evaluated. In the present study, two GEO datasets (GSE48452, GSE126848) were downloaded. The dysregulated expressed genes (DEGs) were identified by machine learning methods (Penalize regression models). Then, the shared DEGs between the two training datasets were validated using validation datasets. ROC-curve analysis was used to identify diagnostic markers. R software analyzed the interactions between DEGs, clinical data, and fatty liver. Ten novel genes, including ABCF1, SART3, APC5, NONO, KAT7, ZPR1, RABGAP1, SLC7A8, SPAG9, and KAT6A were found to have a differential expression between NAFLD and healthy individuals. Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagnostic markers. These key genes may be predictive markers for the development of fatty liver. It is recommended that these key genes are assessed further as possible predictive markers during the development of fatty liver. Show less