Dietary fat and total energy intake modifies the association of genetic profile risk score on obesity: evidence from 48 170 UK Biobank participants

Celis‐Morales, Carlos; Lyall, Donald M.; Gray, Stuart R.; Steell, Lewis; Anderson, Jana; Iliodromiti, Stamatina; Welsh, Paul; Guo, Yibing; Petermann, Fanny; Mackay, Daniel; Bailey, Mark E.S.; Pell, Jill P.; Gill, Jason M.R.; Sattar, Naveed

doi:10.1038/ijo.2017.169

Cited by 39 publications

(22 citation statements)

References 33 publications

(51 reference statements)

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“…Additionally, a study in 1680 South Asians has shown a significant interaction of the risk allele 'T' of the TCF7L2 SNP rs12255372 with fat intake on high-density lipoprotein cholesterol (HDL-C) [70]. Studies on GRS-diet interactions on obesity traits have mainly focused on European populations [71][72][73]. In agreement with our study, data from UK Biobank [72] and two studies from the US [71] have reported significant interactions between the GRS and dietary intakes of total fat and SFA on WC; the GRS was associated with a higher WC among individuals with high intakes of total fat and SFA.…”

Section: Discussionmentioning

confidence: 99%

Interaction between Metabolic Genetic Risk Score and Dietary Fatty Acid Intake on Central Obesity in a Ghanaian Population

et al. 2020

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Obesity is a multifactorial condition arising from the interaction between genetic and lifestyle factors. We aimed to assess the impact of lifestyle and genetic factors on obesity-related traits in 302 healthy Ghanaian adults. Dietary intake and physical activity were assessed using a 3 day repeated 24 h dietary recall and global physical activity questionnaire, respectively. Twelve single nucleotide polymorphisms (SNPs) were used to construct 4-SNP, 8-SNP and 12-SNP genetic risk scores (GRSs). The 4-SNP GRS showed significant interactions with dietary fat intakes on waist circumference (WC) (Total fat, Pinteraction = 0.01; saturated fatty acids (SFA), Pinteraction = 0.02; polyunsaturated fatty acids (PUFA), Pinteraction = 0.01 and monounsaturated fatty acids (MUFA), Pinteraction = 0.01). Among individuals with higher intakes of total fat (>47 g/d), SFA (>14 g/d), PUFA (>16 g/d) and MUFA (>16 g/d), individuals with ≥3 risk alleles had a significantly higher WC compared to those with <3 risk alleles. This is the first study of its kind in this population, suggesting that a higher consumption of dietary fatty acid may have the potential to increase the genetic susceptibility of becoming centrally obese. These results support the general dietary recommendations to decrease the intakes of total fat and SFA, to reduce the risk of obesity, particularly in individuals with a higher genetic predisposition to central obesity.

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Section: Discussionmentioning

confidence: 99%

Interaction between Metabolic Genetic Risk Score and Dietary Fatty Acid Intake on Central Obesity in a Ghanaian Population

et al. 2020

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“…According to a 2-year weight loss intervention programme, carriers of the risk allele 'A' of the FTO SNP rs1558902 had a greater reduction in weight and regional fat compared to non-carriers when high protein diets were consumed, whereas an opposite genetic effect was found on changes in fat distribution in response to a low-protein intake [60]. However, studies investigating the joint effect of genetic variants have reported conflicting results [62][63][64], indicating that the influence of genetic predisposition on changes in body weight and WC does not seem to be modulated by protein intake. In contrast, the present study provides evidence for GRSprotein intake interactions on WC and triglyceride concentrations, and these interactions were independent of potential confounding effects.…”

Section: Discussionmentioning

confidence: 99%

“…In agreement with some studies [62,63], no interactions were detected between GRS and dietary intake of protein, fat and carbohydrate on BMI in the present study. However, a study in the European population (N = 48,170 adults) has shown that the joint effect of 93 obesity-related SNPs on BMI might be modulated by the intake of total energy, fat and saturated fat [64]. Furthermore, studies have shown that an obesogenic diet and physical inactivity with relatively high intake of sugar-sweetened beverages and prolonged television watching might exaggerate the effect of genetic factors on adiposity [18,68].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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Background Cardiometabolic 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. Aims In 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. Methods The 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. Results There 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). Conclusions Our 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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“…Diet (particularly fat and energy intake) has been shown to interact with genetic risk scores to alter the obesity risk. 79 Genes implemented in diet-gene interactions in obesity namely include FTO, MC4R, APOA2, PLIN and PPARG genes. FTO is regarded as the first identified obesity gene.…”

Section: Obesitymentioning

confidence: 99%

Nutrigenetics—Personalized Nutrition in the Genetic Age

Beckett¹,

Jones²,

Veysey³

et al. 2017

Exploratory Research and Hypothesis in Medicine

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Diet is an important modifiable determinant of disease, and it is becoming clear that diet and genetic risk factors are interactive in determining risk for diseases such as cardiovascular disease, diabetes and cancers. Advances in technology have improved our understanding of gene-nutrient interactions, and lead to the development of nutrigenetics, personalized nutrition based on genetics. While evidence is strong for some associations, others remain unclear. As such, the implementation of nutrigenetics remains controversial. While some argue it is not ready for clinical use, it has also been argued that nutrigenetics is unfairly held to a higher standard than traditional nutrition research. Despite the future promise of nutrigenetic testing for improving health outcomes, several barriers in science, technology, acceptance and ethics exist to its implementation. Gene-nutrient associations have been identified in a number of lifestyle-associated diseases, and better understanding of these relationships may lead to improved health outcomes. However, the success of nutrigenetics is not only dependent on the strength of the science, but in consumer acceptance and uptake. This narrative review provides an overview of the current landscape for nutrigenetics in relation to key disease states, and addresses the potential barriers to implementation.

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Dietary fat and total energy intake modifies the association of genetic profile risk score on obesity: evidence from 48 170 UK Biobank participants

Cited by 39 publications

References 33 publications

Interaction between Metabolic Genetic Risk Score and Dietary Fatty Acid Intake on Central Obesity in a Ghanaian Population

Interaction between Metabolic Genetic Risk Score and Dietary Fatty Acid Intake on Central Obesity in a Ghanaian Population

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

Nutrigenetics—Personalized Nutrition in the Genetic Age

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