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Preliminary Results of CitraVes™ Effects on Low Density Lipoprotein Cholesterol and Waist Circumference in Healthy Subjects after 12 Weeks: A Pilot Open-Label Study.

ABSTRACT: Appropriate monitoring and control of modifiable risk factors, such as the level of low-density lipoprotein cholesterol (LDL-C) and other types of dyslipidemia, have an important role in the prevention of cardiovascular diseases (CVD). Recently, various nutraceuticals with lipid-lowering effects have gained attention. In addition to the plant-derived bioactive compounds, recent studies suggested that plant cells are able to release small lipoproteic structures named extracellular vesicles (EVs). The interaction between EVs and mammalian cells could lead to beneficial effects through anti-inflammatory and antioxidant activities. The present study aimed to assess the safety of the new patented plant-based product citraVes™, containing extracellular vesicles (EVs) from Citrus limon (L.) Osbeck juice, and to investigate its ability to modulate different CV risk factors in healthy subjects. A cohort of 20 healthy volunteers was recruited in a prospective open-label study. All participants received the supplement in a spray-dried formulation at a stable dose of 1000 mg/day for 3 months. Anthropometric and hematobiochemical parameters were analyzed at the baseline and after the follow-up period of 1 and 3 months. We observed that the supplement has an effect on two key factors of cardiometabolic risk in healthy subjects. A significant change in waist circumference was found in women after 4 (85.4 [79.9, 91.0] cm, p < 0.005) and 12 (85.0 [80.0, 90.0] cm, p < 0.0005) weeks, when compared to the baseline value (87.6 [81.7, 93.6] cm). No difference was found in men (baseline: 100.3 [95.4, 105.2] cm; 4 weeks: 102.0 [95.7, 108.3] cm; 12 weeks: 100.0 [95.3, 104.7] cm). The level of LDL-C was significantly lower at 12 weeks versus 4 weeks (p = 0.0064). Our study evaluated, for the first time, the effects of a natural product containing plant-derived EVs on modifiable risk factors in healthy volunteers. The results support the use of EV extracts to manage cardiometabolic risk factors successfully.

SUBMITTER: Raimondo S

PROVIDER: S-EPMC8145538 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAbdominal obesity predict metabolic syndrome parameters at low levels of waist circumference (WC) in Africans. At the same time, the African lipid profile phenotype of low high-density lipoprotein (HDL) cholesterol without concomitant elevated triglyceride levels renders high triglyceride levels detrimental to cardiometabolic health unsuitable for identifying cardiometabolic risk in black African populations.ObjectivesWe aimed to identify simple clinical measures for cardiometabolic risk based on WC and HDL in an adult Kenyan population in order to determine which of the two predictors had the strongest impact.MethodsWe used linear regression analyses to assess the association between the two exposure variables WC and HDL with cardiometabolic risk factors including ultrasound-derived visceral (VAT) and subcutaneous adipose tissue (SAT) accumulation, fasting and 2-h venous glucose, fasting insulin, fasting lipid profile, and blood pressure in adult Kenyans (n = 1 370), and a sub-population with hyperglycaemia (diabetes and pre-diabetes) (n = 196). The same analyses were performed with an interaction between WC and HDL to address potential effect modification. Ultrasound-based, semi-quantitative hepatic steatosis assessment was used as a high-risk measure of cardiometabolic disease.ResultsMean age was 38.2 (SD 10.7) (range 17-68) years, mean body mass index was 22.3 (SD 4.5) (range 13.0-44.8) kg/m2, and 57.8% were women. Cardiometabolic risk was found in the association between both WC and HDL and all outcome variables (p<0.05) except for HDL and SAT, fasting and 2-h venous glucose. Additive cardiometabolic risk (WC and HDL interaction) was found for SAT, low-density lipoprotein cholesterol, and triglycerides. No differences in the association between WC and HDL and the outcome variables were found when comparing the full study population and the hyperglycaemia sub-population. Increase in WC and HDL were both associated with hepatic steatosis (OR 1.09, p<0.001, and OR 0.46, p = 0.031, respectively).ConclusionIn adult Kenyans, increasing WC identified more cardiometabolic risk factors compared to HDL.

Project description:BackgroundWaist circumference is becoming recognized as a useful predictor of health risks in clinical research. However, clinical datasets tend to lack this measurement and self-reported values tend to be inaccurate. Predicting waist circumference from standard physical features could be a viable method for generating this information when it is missing or mitigating the impact of inaccurate self-reports. This study determined the degree to which the XGBoost advanced machine learning algorithm could build models that predict waist circumference from height, weight, calculated Body Mass Index, age, race/ethnicity and sex, whether they perform better than current models based on linear regression, and the relative importance of each feature in this prediction.MethodsWe trained tree-based models (via XGBoost gradient boosting) and linear models (via regression) to predict waist circumference from height, weight, Body Mass Index, age, race/ethnicity and sex (n = 60,740 participants). We created 10 iterations of each model, each using 90% of the dataset for training and the remaining 10% for testing performance (this group was different for each iteration). We calculated model performance and feature importance as an average across 10 iterations. We then externally validated the ensembled version of the top model.ResultsThe XGBoost model predicted waist circumference with a mean bias ± standard deviation of 0.0 ± 0.04 cm and a root mean squared error of 4.7 ± 0.05 cm, with performance varying slightly by sex and race/ethnicity. The XGBoost model showed varying degrees of improvement over linear regression models. The top 3 predictors were Body Mass Index, weight and race (Asian). External validation found that on average this model overestimated waist circumference by 4.65 cm in the United Kingdom population (mainly due to overprediction in females) and underestimated waist circumference by 1.7 cm in the Chinese population. The respective root mean squared errors were 7.7 cm and 7.1 cm.ConclusionsXGBoost-based models accurately predict waist circumference from standard physical features. Waist circumference prediction using this approach would be valuable for epidemiological research and beyond.

Dataset Information

Preliminary Results of CitraVes™ Effects on Low Density Lipoprotein Cholesterol and Waist Circumference in Healthy Subjects after 12 Weeks: A Pilot Open-Label Study.

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