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Phytosterols and Cardiovascular Risk Evaluated against the Background of Phytosterolemia Cases-A German Expert Panel Statement.

Eberhard WindlerFrank-Ulrich BeilHeiner K BertholdIoanna Gouni-BertholdUrsula KassnerGerald KloseStefan LorkowskiWinfried MärzKlaus G ParhoferJogchum PlatGünter SilbernagelElisabeth Steinhagen-ThiessenOliver WeingärtnerBirgit-Christiane ZyriaxDieter Lütjohann
Published in: Nutrients (2023)
Phytosterols (PSs) have been proposed as dietary means to lower plasma LDL-C. However, concerns are raised that PSs may exert atherogenic effects, which would offset this benefit. Phytosterolemia was thought to mimic increased plasma PSs observed after the consumption of PS-enriched foods. This expert statement examines the possibility of specific atherogenicity of PSs based on sterol metabolism, experimental, animal, and human data. Observational studies show no evidence that plasma PS concentrations would be associated with an increased risk of atherosclerosis or cardiovascular (CV) events. Since variants of the ABCG5/8 transporter affect the absorption of cholesterol and non-cholesterol sterols, Mendelian randomization studies examining the effects of ABCG5/8 polymorphisms cannot support or refute the potential atherogenic effects of PSs due to pleiotropy. In homozygous patients with phytosterolemia, total PS concentrations are ~4000% higher than under physiological conditions. The prevalence of atherosclerosis in these individuals is variable and may mainly relate to concomitant elevated LDL-C. Consuming PS-enriched foods increases PS concentrations by ~35%. Hence, PSs, on a molar basis, would need to have 20-40 times higher atherogenicity than cholesterol to offset their cholesterol reduction benefit. Based on their LDL-C lowering and absence of adverse safety signals, PSs offer a dietary approach to cholesterol management. However, their clinical benefits have not been established in long-term CV endpoint studies.
Keyphrases
  • low density lipoprotein
  • cardiovascular disease
  • endothelial cells
  • risk factors
  • type diabetes
  • electronic health record
  • machine learning
  • dna methylation
  • big data
  • case control
  • copy number
  • genome wide