👤 Daniela Crescenti

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the two major neurodegenerative diseases causing dementia. Due to similar clinical phenotypes, differential diagnosis is challenging without specific biomarkers. Beta-site Amyloid Precursor Protein cleaving enzyme 1 (BACE1) is a β-secretase pivotal in AD pathogenesis. In AD and mild cognitive impairment subjects, BACE1 activity is increased in brain/cerebrospinal fluid, and plasma levels appear to reflect those in the brain. In this study, we aim to evaluate serum BACE1 activity in FTD, since, to date, there is no evidence about its role. The serum of 30 FTD patients and 30 controls was analyzed to evaluate (i) BACE1 activity, using a fluorescent assay, and (ii) Glial Fibrillary Acid Protein (GFAP) and Neurofilament Light chain (NfL) levels, using a Simoa kit. As expected, a significant increase in GFAP and NfL levels was observed in FTD patients compared to controls. Serum BACE1 activity was not altered in FTD patients. A significant increase in serum BACE1 activity was shown in AD vs. FTD and controls. Our results support the hypothesis that serum BACE1 activity is a potential biomarker for the differential diagnosis between AD and FTD. Show less

Hypertriglyceridemia (HTG) is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). One of the multiple origins of HTG alteration is impaired lipoprotein lipase (LPL) activity, which is an emerging target for HTG treatment. We hypothesised that early, even mild, alterations in LPL activity might result in an identifiable metabolomic signature. The aim of the present study was to assess whether a metabolic signature of altered LPL activity in a preclinical model can be identified in humans. A preclinical LPL-dependent model of HTG was developed using a single intraperitoneal injection of poloxamer 407 (P407) in male Wistar rats. A rat metabolomics signature was identified, which led to a predictive model developed using machine learning techniques. The predictive model was applied to 140 humans classified according to clinical guidelines as (1) normal, less than 1.7 mmol/L; (2) risk of HTG, above 1.7 mmol/L. Injection of P407 in rats induced HTG by effectively inhibiting plasma LPL activity. Significantly responsive metabolites (i.e. specific triacylglycerols, diacylglycerols, phosphatidylcholines, cholesterol esters and lysophospholipids) were used to generate a predictive model. Healthy human volunteers with the impaired predictive LPL signature had statistically higher levels of TG, TC, LDL and APOB than those without the impaired LPL signature. The application of predictive metabolomic models based on mechanistic preclinical research may be considered as a strategy to stratify subjects with HTG of different origins. This approach may be of interest for precision medicine and nutritional approaches. Show less