Interaction between SIDT2 and ABCA1 Variants with Nutrients on HDL-c Levels in Mexican Adults.
Guadalupe León-ReyesAnna D Argoty-PantojaBerenice Rivera-ParedezAlberto Hidalgo-BravoYvonne Nicole FloresJorge SalmerónRafael Velázquez-CruzPublished in: Nutrients (2023)
Previous studies have reported that the SIDT2 and ABCA1 genes are involved in lipid metabolism. We aimed to analyze the association-the gene x gene interaction between rs17120425 and rs1784042 on SIDT2 and rs9282541 on ABCA1 and their diet interaction on the HDL-c serum levels-in a cohort of 1982 Mexican adults from the Health Workers Cohort Study. Demographic and clinical data were collected through a structured questionnaire and standardized procedures. Genotyping was performed using a predesigned TaqMan assay. The associations and interactions of interest were estimated using linear and logistic regression. Carriers of the rs17120425-A and rs1784042-A alleles had slightly higher blood HDL-c levels compared to the non-carriers. In contrast, rs9282541-A was associated with low blood HDL-c levels (OR = 1.34, p = 0.013). The rs1784042 x rs9282541 interaction was associated with high blood HDL-c levels ( p = 3.4 × 10 -4 ). Premenopausal women who carried at least one rs17120425-A allele and consumed high dietary fat, protein, monounsaturated, or polyunsaturated fatty acids levels had higher HDL-c levels than the non-carriers. These results support the association between the genetic variants on SIDT2 and ABCA1 with HDL-c levels and suggest gene-gene and gene-diet interactions over HDL-c concentrations in Mexican adults. Our findings could be a platform for developing clinical and dietary strategies for improving the health of the Mexican population.
Keyphrases
- genome wide
- copy number
- genome wide identification
- healthcare
- public health
- high throughput
- weight loss
- physical activity
- pregnant women
- transcription factor
- machine learning
- type diabetes
- fatty acid
- single cell
- postmenopausal women
- heavy metals
- metabolic syndrome
- insulin resistance
- social media
- artificial intelligence
- data analysis
- pregnancy outcomes