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Interaction between the genetic risk score and dietary protein intake on cardiometabolic traits in Southeast Asian

  • Alsulami, Sooad1, 2
  • Aji, A. S.3, 4
  • Ariyasra, U.3
  • Sari, S. R.3
  • Tasrif, N.5
  • Yani, F. F.5
  • Lovegrove, J. A.2, 6
  • Sudji, I. R.5
  • Lipoeto, N. I.5
  • Vimaleswaran, K. S.2, 6
  • 1 King Abdulaziz University, Jeddah, Saudi Arabia , Jeddah (Saudi Arabia)
  • 2 University of Reading, Whiteknights, Reading, RG6 6AP, UK , Reading (United Kingdom)
  • 3 Andalas University, Padang, West Sumatra, Indonesia , Padang (Indonesia)
  • 4 Universitas Alma Ata, Yogyakarta, Indonesia , Yogyakarta (Indonesia)
  • 5 Andalas University, Padang, Indonesia , Padang (Indonesia)
  • 6 University of Reading, Reading, UK , Reading (United Kingdom)
Published Article
Genes & Nutrition
Publication Date
Oct 12, 2020
DOI: 10.1186/s12263-020-00678-w
Springer Nature


BackgroundCardiometabolic diseases are complex traits which are influenced by several single nucleotide polymorphisms (SNPs). Thus, analysing the combined effects of multiple gene variants might provide a better understanding of disease risk than using a single gene variant approach. Furthermore, studies have found that the effect of SNPs on cardiometabolic traits can be influenced by lifestyle factors, highlighting the importance of analysing gene-lifestyle interactions.AimsIn the present study, we investigated the association of 15 gene variants with cardiometabolic traits and examined whether these associations were modified by lifestyle factors such as dietary intake and physical activity.MethodsThe study included 110 Minangkabau women [aged 25–60 years and body mass index (BMI) 25.13 ± 4.2 kg/m2] from Padang, Indonesia. All participants underwent a physical examination followed by anthropometric, biochemical and dietary assessments and genetic tests. A genetic risk score (GRS) was developed based on 15 cardiometabolic disease-related SNPs. The effect of GRS on cardiometabolic traits was analysed using general linear models. GRS-lifestyle interactions on continuous outcomes were tested by including the interaction term (e.g. lifestyle factor*GRS) in the regression model. Models were adjusted for age, BMI and location (rural or urban), wherever appropriate.ResultsThere was a significant association between GRS and BMI, where individuals carrying 6 or more risk alleles had higher BMI compared to those carrying 5 or less risk alleles (P = 0.018). Furthermore, there were significant interactions of GRS with protein intake on waist circumference (WC) and triglyceride concentrations (Pinteraction = 0.002 and 0.003, respectively). Among women who had a lower protein intake (13.51 ± 1.18% of the total daily energy intake), carriers of six or more risk alleles had significantly lower WC and triglyceride concentrations compared with carriers of five or less risk alleles (P = 0.0118 and 0.002, respectively).ConclusionsOur study confirmed the association of GRS with higher BMI and further showed a significant effect of the GRS on WC and triglyceride levels through the influence of a low-protein diet. These findings suggest that following a lower protein diet, particularly in genetically predisposed individuals, might be an effective approach for addressing cardiometabolic diseases among Southeast Asian women.

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