Impact of Lipid Genetic Risk Score and Saturated Fatty Acid Intake on Central Obesity in an Asian Indian Population

Abstract

Abnormalities in lipid metabolism have been linked to the development of obesity. We used a nutrigenetic approach to establish a link between lipids and obesity in Asian Indians, who are known to have a high prevalence of central obesity and dyslipidaemia. A sample of 497 Asian Indian individuals (260 with type 2 diabetes and 237 with normal glucose tolerance) (mean age: 44 ± 10 years) were randomly chosen from the Chennai Urban Rural Epidemiological Study (CURES). Dietary intake was assessed using a previously validated questionnaire. A genetic risk score (GRS) was constructed based on cholesteryl ester transfer protein (CETP) and lipoprotein lipase (LPL) genetic variants. There was a significant interaction between GRS and saturated fatty acid (SFA) intake on waist circumference (WC) (Pinteraction = 0.006). Individuals with a low SFA intake (≤23.2 g/day), despite carrying ≥ 2 risk alleles, had a smaller WC compared to individuals carrying < 2 risk alleles (Beta = −0.01 cm; p = 0.03). For those individuals carrying ≥ 2 risk alleles, a high SFA intake (>23.2 g/day) was significantly associated with a larger WC than a low SFA intake (≤23.2 g/day) (Beta = 0.02 cm, p = 0.02). There were no significant interactions between GRS and other dietary factors on any of the measured outcomes. We conclude that a diet low in SFA might help reduce the genetic risk of central obesity confirmed by CETP and LPL genetic variants. Conversely, a high SFA diet increases the genetic risk of central obesity in Asian Indians.

Gene-environment interactions and the effect on obesity risk in low and middle-income countries: a scoping review

“…Individuals with high GRS (≥ 2 risk alleles) and high SFA intake (>14 g/day) had a significantly higher WC ( P interaction = 0.02) compared to those with low SFA intake after adjustment for age and sex in the Ghanaian study. In contrast, in the Indian population, those with lower SFA intake (≤23.2 g/day) had a significantly smaller WC (β= -0.01cm, P =0.03) ( P interaction = 0.006) after adjustment for age, sex and 6 other potential confounders ( 30 , 32 ). However, some inconsistencies were also reported on the modifying effects of dietary fat intake on genetic susceptibility and obesity risk.…”

“…(n = 80) and Wuni et al. (n = 497) all reported significant associations between higher total fat intake and obesity traits, for those carrying risk alleles of obesity-related gene variants, or for those with a high GRS in Indonesian, Ghanaian and Indian populations ( 29 – 32 ). High SFA was also found to interact positively with WC in those with increased genetic susceptibility to obesity in the cross-sectional studies by Alsulami et al.…”

“…However, even with replication, many of the study designs shown in this review are susceptible to reverse causation, highlighting the need for more well-controlled long-term prospective longitudinal research looking at GxE on obesity. Using P -values without effect sizes or confidence intervals to report associations was also prevalent across studies ( 32 – 35 , 42 ), alongside small sample sizes and erroneous underpowered statistical analyses, resulting in concerns for selective reporting and publication bias.…”

Implementation of Nutrigenetics and Nutrigenomics Research and Training Activities for Developing Precision Nutrition Strategies in Malaysia

“…In addition, future considerations include the use of genetic risk scores rather than individual SNPs, large sample sizes, and strategies to improve follow-up. The inclusion of data on total energy and macronutrient intake, data on specific types of food or micronutrients, as well as accounting for various confounding factors in the statistical analysis, need to be taken into consideration [ 26 , 39 , 51 , 54 , 55 ]. To this end, training workshops will include ethics protocols, software for data collection and analysis, and research methods to enhance reproducibility (see Supplementary Table S1 ).…”

“…This will be achieved through collaboration of the partners and associate partners of the N 2 RTU with academics from other Malaysian institutions. Results of nutrigenetic studies by the GeNuIne Collaboration indicate that individuals with a genetic predisposition to cardiometabolic traits such as obesity, dyslipidaemia, and hyperglycaemia are responsive to particular nutrients in an ethnic-specific manner [ 26 , 39 , 44 , 55 , 57 , 58 ] but the translation of these findings into clinical practice requires the joint effort of different stakeholders, and the N 2 RTU is well placed to achieve this. Studies have also shown that baseline metabolic profiles can predict responses to nutritional interventions [ 59 , 60 ].…”