Gene Diet Interactions

Most of the emphasis on gene-environment interactions continues to be placed on gene-diet interactions. Unlike studies focusing on tobacco smoking, alcohol and physical activity, studies of gene-diet interactions include both observational and intervention studies. The major concern with extracting information about dietary intake from observational studies continues to be the use of dietary intake assessments that do not accurately reflect true intake of the individual. Improvements in data collection are needed to obtain better and more objective measures of nutrient intakes from large observational studies. Conversely, the major concern about intervention studies has been, and continues to be, the very small number of subjects involved in each study (Ordovas and Corella, 2004; Corella and Ordovas, 2005). For the purpose of this chapter, we have grouped studies according to their experimental design and include postprandial studies that were not accompanied by dietary changes within observational studies. Previous reports have extensively reviewed this topic (Ordovas and Corella, 2004; Corella and Ordovas, 2005) and in this work we will focus only on recent publications.

Observational studies reporting gene-diet interactions in the fasting or postprandial states overwhelmingly focused on the traditional lipid candidate genes (Santos et al., 2006; Corella et al., 2007b; Robitaille et al., 2007b; Li et al., 2007; Morcillo et al., 2007; Shen et al., 2007; Sofi et al., 2007; Scacchi et al., 2007) (Table 1.3). The average number of subjects included in observational gene-diet interaction studies is much lower than those reported for interactions with alcohol, smoking and physical activity and these numbers have not changed much in recent years. The main outcomes examined in gene-diet interactions have included primarily plasma lipids, but also body mass index (BMI), inflammatory markers and other measures of the metabolic syndrome. Moreover, each report is limited to one single locus and, even those that examine multiple loci (Santos et al., 2006) do not attempt to examine more complex but more realistic situations involving gene-gene-nutrient interactions.

It is important to underscore the consolidation of certain candidate genes on their role in lipid metabolism and modulation by dietary factors. This is the case of the APOA5 gene (Corella et al., 2007b; Hubacek et al., 2007; Moreno-Luna et al., 2007), as well as new findings involving the long-known but cryptic APOA2, suggesting potential roles of this apolipoprotein on dietary intake, body mass index and postprandial lipemia (Corella et al., 2007a; Delgado-Lista et al., 2007).

Although most of the gene-diet interactions have focused on dietary fats, other habits such as coffee drinking have been studied in a recent case-control study (Cornelis et al., 2007a) that examined the interaction between the adenosine A2A receptor (ADORA2A) and the CYP1A2 genes and caffeine intake (see Table 1.2) and reported a modulation of caffeine intake due to genetic variability at the ADORA2A gene. Several other phenotypes have been investigated in relation to gene-diet interactions beyond dietary fats. One of the most solidly established is the 5',10'-methylenetetrahydrofolate reductase (MTHFR) gene which has been comprehensively reviewed (Cummings and Kavlock, 2004; Friso and Choi, 2005). Another less explored example is the one provided by investigators in the UK and New Zealand who tested whether children's intellectual development was influenced by both genetics and early nutrition - i.e. breastfeeding versus formula feeding (Caspi et al., 2007). These researchers showed that the association between breastfeeding and IQ is moderated by a genetic variant in FADS2, a gene involved in the genetic control of fatty acid pathways. Their finding shows that environmental exposures can be used to uncover novel candidate genes in complex phenotypes. It also shows that genes interact with the early environment to shape the complex phenotypes such as IQ.

Findings from observational studies are informative and are of great potential value, but their clinical validity must be confirmed by intervention studies. Ideally, studies designed to test gene-diet interactions should involve prior selection of participants based on the genotype of interest. Most of the currently reported intervention studies are still

TABLE 1.3 Gene-diet interactions (observational studies)

Gene

Population

Main trait

Outcome

Reference z

SLC6A14, CART, GHSR, GAD2, GHRL, MKKS, LEPROTL1, PCSK1, UCP2, UCP3, FOXC2, PPARGC1A, PPARG2, PPARG3, SREBF1, WAC, HSD11B1, LIPC, IGF2, KCNJ11, ENPP1, ADIPOQ, CD36, IL6, TNFA, SERPINE1

APOA1/C3/A4/A5

43 SNPs

549 obese women Obesity

APOA5

CPT1

APOA1 (G-75 > A and C83 > T), APOC3 (C-482 > T and C3238 > G), APOA4 (Thr347> Ser and Gln360His) and APOA5 (T-1131 > C, Ser19 > Trp and Val153 > Met)

133 men

Lipids

1073 men and 1207 women

CPT1B c.282-18C > T 252 men and 99 Obesity and p.E531K variants women

The most remarkable interaction found in this study refers to the combination of the hepatic lipase gene polymorphism -514 C > T and fiber intake

APOA4 and APOA5 variants may play an important role in the individual sensitivity of lipid parameters to dietary composition in men

The APOA5-1131T> C modulates the effect of fat intake on BMI and obesity risk in both men and women

The findings suggest that indices of obesity might be modulated by an interaction between CPT1 variants and fat intake

Santos et al., 2006

Hubacek et al., 2007

Corella et al., 2007b

Robitaille et al., 2007b

CETP

TaqlB

780 diabetic men

FABP2

Ala54Thr

1226 men and women z d H

IL1B

-1473G > C, -511G > A, -31T > C, 3966C > T, 6054G > A

540 men and 580 women

LPA 93C > T and LPA 260 men and 387 121G > A women

PPARG

Prol2Ala

1394 men and women

HDLC These data suggested an interaction between the CETP TaqlB polymorphism and intake of dietary fat on plasma HDL concentration

Insulin An interaction existed between resistance the Ala54Thr polymorphism of FABP2 and the intake of dietary fats in a population with a high intake of monounsaturated fatty acids

Inflammation These results suggested that and the MetS ILlbeta genetic variants were associated with measures of chronic inflammation and the MetS risk and that genetic influences were more evident among subjects with low (n-3) PUFA intake

Lp(a) This study reported a significant interaction of daily fish intake and LPA 93C > T polymorphism in decreasing Lp(a) concentrations

T2DM This analysis suggests that the protective effect of the Ala allele against T2DM is present only in populations with a diet rich in lipids

Morcillo et al., 2007

Shen et al., 2007

Sofi et al., 2007

Scacchi et al., 2007

55 71

o cn

TABLE 1.3 (Continued)

Gene

Population

Main trait

Outcome

Reference z

APOA2

APOA2

APOA5

PPARA

APOB

SCARB1

Leu162Val and 140+5435T>C

514 men and 564 women

88 E3/E3 men

88 E3/E3 men

59 men

Fasting and PPL

m516C/T

c1119C>T

47 E3/E3 subjects PPL

59 men

The -265T > C polymorphism is consistently associated with food consumption and obesity, suggesting a new role for APOA2 in regulating dietary intake

This work shows that carriers of the minor C allele for APOA2 -265T/C (CC/TC) have a lower postprandial response compared with TT homozygotes

The minor 56G and -1131C alleles were associated with a higher postprandial response

These data suggest that PPARalpha variants may modulate CVD risk by influencing both fasting and postprandial lipid concentrations

The presence of the C/T genotype is associated with a higher PPL response

The c.1119C > T polymorphism is associated with a lower postprandial TG response in the smaller, partially catabolized lipoprotein fraction

Corella et al., 2007a

Delgado-Lista et al., 2007

Moreno-Luna et al., 2007

Tanaka et al., 2007b

Perez-Martinez et al., 2007b

Tanaka et al., 2007a

using retrospective and opportunistic analyses of subjects participating in dietary intervention studies designed for non-genetic purposes (Table 1.4) (Moreno-Luna et al., 2007; Perez-Martinez et al., 2007a, 2007b, 2007c; Fernandez de la Puebla et al., 2007; Tanaka et al., 2007a, 2007b; Delgado-Lista et al., 2007; Santosa et al., 2007; Hubacek et al., 2007; Nelson et al., 2007b; Corella et al., 2007a; Lai et al., 2007; Erkkila et al., 2007; Weiss et al., 2007; Helwig et al., 2007; Robitaille et al., 2007a, 2007b; Nieminen et al., 2007). As expected from their cost and complexity, the number of participants in these studies is very small and subject to errors and spurious associations and interactions. Similar to observational studies, most of the interventional reports focused on well-known candidate genes and none of the novel loci identified from genome-wide association studies has yet made their way to the literature. Although it is anticipated that reports about new genes will trickle into the literature in the near future.

Natural Weight Loss

Natural Weight Loss

I already know two things about you. You are an intelligent person who has a weighty problem. I know that you are intelligent because you are seeking help to solve your problem and that is always the second step to solving a problem. The first one is acknowledging that there is, in fact, a problem that needs to be solved.

Get My Free Ebook


Post a comment