Are Phosphatidylcholine and Lysophosphatidylcholine Body Levels Potentially Reliable Biomarkers in Obesity? A Review of Human Studies.
Paula Emília Nunes Ribeiro BellotMelissa Nunes MoiaBruna Zavarize ReisLucia de Fatima Campos PedrosaLjubica TasicFernando BarbosaKarine Cavalcanti Maurício de Sena EvangelistaPublished in: Molecular nutrition & food research (2023)
Phosphatidylcholines (PCs) are the major components of biological membranes in animals and are a class of phospholipids that incorporate choline as a headgroup. Lysophosphatidylcholines (LPCs) are a class of lipid biomolecules derived from the cleavage of PCs, and are the main components of oxidized low-density lipoproteins (oxLDLs) that are involved in the pathogenesis of atherosclerosis. Since obesity is associated with a state of chronic low-grade inflammation, one can anticipate that the lipidomic profile changes in this context and both PCs and LPCs are gaining attention as hypothetically reliable biomarkers of obesity. Thus, a literature search is performed on PubMed, Latin American and Caribbean Health Science Literature (LILACS), and Excerpta Medica DataBASE (Embase) to obtain the findings of population studies to clarify this hypothesis. The search strategy resulted in a total of 2403 reports and 21 studies were included according to the eligibility criteria. Controversial data on the associations of PCs and LPCs with body mass index (BMI) and body fat parameters have been identified. There is an inverse relationship between BMI and most species of PCs, and a majority of studies exhibited negative associations between BMI and LPCs. Other findings regarding the differences between PCs and LPCs in obesity are presented, and the associated uncertainties are discussed in detail.
Keyphrases
- weight gain
- body mass index
- insulin resistance
- metabolic syndrome
- weight loss
- low grade
- high fat diet induced
- type diabetes
- case control
- systematic review
- public health
- healthcare
- endothelial cells
- oxidative stress
- high grade
- cardiovascular disease
- mental health
- skeletal muscle
- physical activity
- big data
- fatty acid
- risk assessment
- deep learning
- transcription factor
- adverse drug
- genetic diversity
- pluripotent stem cells