👤 Julia Hernandez-Baixauli

In recent years many women have looked for alternative therapies to address menopause. Hesperidin, phytosterols and curcumin are bioactive compounds that can ameliorate some cardiovascular risk factors associated with menopause, although there are no data concerning the effects of their combined supplementation. We used ovariectomized (OVX) rats, a postmenopausal model with oestrogen deficiency, to evaluate whether supplementation with a multi-ingredient (MI) including hesperidin, phytosterols and curcumin for 57 days would display beneficial effects against fat mass accretion and metabolic disturbances associated with menopause. Twenty OVX rats were orally supplemented with either MI (OVX-MI) or vehicle (OVX). Furthermore, 10 OVX rats orally received the vehicle along with subcutaneous injections of 17β-oestradiol biweekly (OVX-E2), whereas 10 rats were sham operated and received oral and injected vehicles (control group; SH). MI supplementation partly counteracted the fat mass accretion observed in OVX animals, which was evidenced by decreased total fat mass, adiposity index, the weight of retroperitoneal, inguinal and mesenteric white adipose tissue (MWAT) depots and MWAT adipocyte hypertrophy. These effects were accompanied by a significant decrease in the circulating levels of leptin and the mRNA levels of the fatty acid uptake-related genes 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