Current Nutrition Reports

, Volume 1, Issue 3, pp 123–131 | Cite as

Gene–Diet Interactions on Lipid Levels: Current Knowledge in the Era of Genome-Wide Association Studies



Plasma lipids are important risk factors for cardiovascular disease. Both genetic factors and diet are known to regulate lipid levels, and there has been a longstanding interest in how genes may interact with diet to modulate changes in lipid levels. Genome-wide association studies have recently identified the genes most strongly associated with variation in lipids within a population. In this paper, the current knowledge on gene–diet interactions to regulate lipid levels is discussed in light of these studies. Future genome-wide studies are required that specifically identify genes that are important modulators of lipid levels in response to dietary change. Some methodologic challenges inherent in these studies are discussed.


Genome-wide association studies GWAS Gene–diet interactions Lipids Lipid metabolism Variation Cholesterol Triglyceride Dietary intervention 


An adverse lipid profile of high triglycerides, total and low-density lipoprotein (LDL) cholesterol, and low high density (HDL) cholesterol has been associated with atherosclerosis and cardiovascular disease [1, 2, 3]. At a population level, diet is an important determinant of the lipid profile, but a great degree of interindividual variation has been consistently noted, particularly in the lipid responses to dietary interventions. This variation has been attributed to differences in genetic background, in addition to age, gender, and ethnicity. In the past, a number of likely candidate genes have been explored, but no genes have been identified that can consistently account for a large number of these interindividual differences. In recent years, genome-wide association studies (GWAS) have shed light on genetic mechanisms underlying the regulation of lipid levels; however, it is becoming apparent that further research is required, with studies more specifically designed to assess diet–gene interactions in a more comprehensive way.

Dietary Modulation of Lipids

Population-level effects of diet composition on lipid profile are well-established. Reductions in total fat, saturated fatty acids, and trans-fatty acids are all effective in reducing total cholesterol, LDL cholesterol, and triglycerides [4, 5, 6]. However, a reduction in total dietary fat intake achieved by replacement with carbohydrates has been associated with an unfavorable reduction in HDL cholesterol and increased triglycerides [5, 7]. Conversely, replacement of trans- or saturated fatty acids with monounsaturated fatty acids has been found to maintain HDL cholesterol [8]. Increased dietary intake of n-3 polyunsaturated fatty acids has been associated with lower triglycerides [9]. In addition to changes in dietary composition, a reduction in total energy leading to weight loss is associated with reductions in cholesterol and triglycerides, and an increase in HDL cholesterol associated with weight loss [10, 11].

While the effects of diet composition on the lipid profile are well-understood, the mechanisms by which diet can alter lipid levels are not fully elucidated. Thus far, mechanistic studies have shown that reductions in dietary saturated and trans-fatty acids involve changes in the activity of several receptors and enzymes, in particular LDL receptor, apolipoproteins B and E, and cholesteryl ester transfer protein (Fig. 1) [12, 13]. Changes in the activity of these enzymes and receptors result in a change in circulating very low-density lipoprotein (VLDL), and a favorable redistribution of cholesterol between plasma and peripheral tissues (Fig. 1) [13]. The reduction in total cholesterol that results from an exchange of saturated with monounsaturated fatty acids appears to be a consequence of increased LDL clearance, most likely involving the LDL receptor [14], but also associated with reduced cholesteryl ester transfer protein activity [15]. The reduction in triglyceride associated with an increased n-3 polyunsaturated fatty acid intake is via decreased production and possibly increased clearance of triglycerides regulated by several proposed mechanisms, particularly involving the desaturase enzymes encoded by fatty acid desaturase genes (Fig. 1) [16]. Hypertriglyceridaemia associated with increased dietary carbohydrate is a result of increased production and/or decreased clearance of triglyceride into peripheral tissues, particularly involving the enzyme lipoprotein lipase [17], as well as pathways regulating de novo lipogenesis (Fig. 1). Hypertriglyceridemia appears to be associated with a reduction in HDL cholesterol, possibly via cholesterol and triglyceride transfer mechanisms and subsequent catabolism of lipoprotein particles. This involves the enzymes hepatic lipase and cholesteryl ester transfer protein (Fig. 1) [18]. Acutely, energy restriction has been shown to inhibit cholesterol synthesis [19], but improvements are also seen as a longer-term consequence of weight loss, with reductions in cholesterol and triglycerides and an increase in HDL cholesterol [11, 20, 21]. The mechanisms behind sustained improvements in lipid profile are less clear and likely to involve additional factors associated with the weight loss, such as insulin sensitivity [20] and blood supply [22].
Fig. 1

Overview of lipid metabolic pathways, the role of key genes identified in genome-wide association studies (GWAS), and the effects of dietary changes. Gastrointestinal adenosine trisphosphate (ATP)-binding cassette subfamily G members 5 and 8 (ABCG5/8) transporters regulate selective sterol absorption. Dietary fats are packaged into chylomicrons for transport to peripheral tissues, where uptake of triglycerides is mediated by lipoprotein lipase (LPL). Angiopoietin-like 3 (ANGPTL3) contributes to the regulation of LPL. Chylomicron remnant uptake into the liver is regulated by apolipoprotein E (APOE). Triglycerides are synthesized in the liver, involving fatty acid (FA) desaturase (FADS1-2–3) enzymes and influenced by glycolytic flux and glucokinase regulatory protein (GCKR). Cholesterol is synthesized in the liver, regulated by 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR). Very low-density lipoproteins (VLDL) are assembled in the liver from apolipoprotein B (APOB), cholesterol, and triglyceride. Sortilin-1 (SORT1), carbohydrate response element binding protein (encoded by MLXIPL), and Tribbles homolog 1 (TRIB1) all contribute to the regulation of hepatic VLDL assembly and efflux. VLDL are processed by LPL, releasing triglyceride into peripheral tissues and resulting in low-density lipoprotein (LDL). High-density lipoproteins (HDL) are formed from apolipoproteins (APOA1) from the liver and phospholipids and cholesterol acquired from peripheral tissues by cholesterol efflux mediated by ATP-binding cassette A1 (ABCA1). Protein phosphatase 1, regulatory (inhibitor) subunit 3B (PPP1R3B) and polypeptide N-acetylgalactosaminyltransferase 2 (GALNT2) contribute to the regulation of HDL levels. Free cholesterol is esterified by the enzyme lecithin-cholesterol acyltransferase (LCAT), which is sequestered into the core of the particle. Continual remodeling of LDL and HDL particles, which involves triglyceride, phospholipid, and cholesteryl ester transfer, occurs via the enzymes cholesteryl ester transfer protein (CETP) and phospholipid transfer protein (PLTP). VLDL and LDL laden with cholesteryl esters are taken up via the LDL receptor (LDLR) into the liver. Proprotein convertase subtilisin/kexin type 9 (PCSK9) regulates degradation of LDLR. Triglycerides are removed and hydrolyzed from TG-rich HDL by hepatic lipase (encoded by LIPC). Endothelial lipase (LIPG) also contributes to HDL remodeling via phospholipase activity. Genes identified in GWAS are in bold and italics. Genes that have been implicated in the regulation of a pathway, but the mechanism has not been elucidated, are indicated in gray [2, 24, 37, 59]. The impact of diet, including changes in fat and carbohydrate content, FA composition, and energy intake, on the regulation of lipid metabolic pathways and the genes that regulate them is indicated. MUFA, monounsaturated fatty acid; SFA, saturated fatty acid; TFA, trans-fatty acid

However, population-level effects of diets on lipid levels conceal significant heterogeneity at an individual level [5]. This is often attributed (in part) to genetic variation [23, 24]. Previous genetic studies, in particular studies of monogenic lipid disorders, have helped characterize specific lipid metabolic pathways that may be modulated by diet [2, 24, 25]. Further advances in genetic analysis and in particular gene–diet interactions will help further elucidate these pathways and identify novel pathways.

Genetic Modulation of Lipids

Heritability estimates for variation in lipid levels within a population range from 30 % to 60 % [26]. In addition to the effect on the variation in lipids within a population, it is also assumed that genetic factors contribute to interindividual differences in response to dietary changes that cannot be explained by sex, age, and ethnicity [3, 23]. Over the past few decades, studies have been conducted to investigate candidate genes within specific lipid metabolic pathways (Fig. 1) in order to identify whether mutation or variation in these genes can explain the differences in lipid levels. Reviews of these candidate gene studies can be found elsewhere [2, 24]. In summary, gene variants have been identified in apolipoproteins, and genes involved in cholesterol synthesis and efflux, lipolysis, lipogenesis, and triglyceride and cholesterol transfer (Fig. 1). The genes that have most consistently been found to account for some of the variation in population lipid levels are APOE, CETP, and APOA5 [24].

However, there are fewer data relating to the genetic influence on lipid changes in response to diet, and no genes have consistently been shown to account for a large proportion of the interindividual variation in lipid responses to dietary change. The APOE polymorphism, which has often been described as a genetic modifier of dietary lipid responses [27], has shown inconsistent results between studies. In a systematic review of studies that involved responses to a modification of dietary saturated fat, only 11 of the 46 intervention studies showed evidence of modification in response by APOE genotype [23]. In the same systematic review, variations in other genes previously found to be moderators of lipid responses to dietary change, including APOB, APO A-I, C-III, and A-IV gene cluster, LDL receptor and lipoprotein lipase, were also found to have inconsistent results. The majority of studies that found significant associations were small (n < 50) studies and therefore possibly a result of false-positive chance [23].

GWAS Identified the Genes That Are Most Important in Variation of Lipids Within a Population

In the preceding 5 years, a number of genome-wide association studies (GWAS) have been conducted to identify several loci robustly associated with variation in lipid levels in (mostly white European) populations [28, 29, 30, 31, 32]. Most recently, a large meta-analysis of more than 100,000 individuals [33•] reported on 95 loci that were associated with lipid traits. The 25 loci most strongly associated with lipid traits from this study are shown in Table 1. Of these loci, 16 were located in or near genes whose proteins played a clear and defined role in lipid metabolic pathways (Fig. 1). These included pathways of cholesterol and triglyceride transfer, apolipoprotein formation, cholesterol synthesis and efflux, lipogenesis, and lipolysis, and in many cases, these genes had already been examined through a candidate gene approach. A further six loci identified in these GWAS, which had previously been unknown or thought to be unrelated to lipid metabolism, have since been shown to play novel roles in lipid metabolism (Table 1). Follow-up mechanistic studies on mice overexpressing the SORT1 gene have established the product of this gene to play a novel regulatory role in reducing hepatic triglyceride and VLDL production and efflux [34•]. Although the exact mechanism remains unclear, this is one of the first examples of genes identified in GWAS leading to identification of novel lipid metabolic pathways [34•]. GCKR, which encodes glucokinase regulatory protein, was shown in mechanistic studies to increase hepatic glycolytic flux and glucose uptake, and was subsequently predicted to increase plasma triglyceride by a mechanism of increased malonyl CoA-activation of de novo lipogenesis and inhibition of fatty acid oxidation [35, 36]. In mice, TRIB1 expression was confirmed to be associated with a reduction in triglyceride and total cholesterol via a reduction in VLDL production, possibly mediated by regulation of lipogenic genes [37]. Functional roles for GALNT2 and PPP1R3B proteins were also shown in murine models [33•]. Overexpression of both GALNT2 and PPP1R3B was associated with reduced HDL cholesterol, although the mechanism by which they act remains unknown [33•]. The functions of the remaining 3 loci in Table 1 and many of the other 70 loci reported in this meta-analysis [33•] remain unknown.
Table 1

Top 25 genes most strongly associated with lipid traits in GWAS



Lipid metabolic pathways


P for lead traitc



Cholesteryl ester/triglyceride exchange


P < 10−380



Major lipoprotein component of HDL, promotes cholesterol efflux


P < 10−240



Hepatic VLDL efflux


P < 10−170



Major lipoprotein in chylomicrons and important in clearance of TG-rich lipoproteins


P < 10−147



Improved glycemic regulation at the expense of high hepatic TG production [36]


P < 10−133



Cell surface receptor for apo B, which regulates endocytosis of cholesterol-rich LDL


P < 10−117



Endothelial protein that promotes uptake of cholesterol-rich lipoproteins into peripheral tissues


P < 10−115



Major lipoprotein of LDL, docks to LDLR for cholesterol delivery to peripheral tissues


P < 10−114



Hepatic protein that hydrolyzes triglyceride and catalyzes receptor-mediated lipoprotein uptake


P < 10−96



Transcription factor that binds carbohydrate response elements to increase transcription of genes involved in glycolysis, lipogenesis, and VLDL secretion


P < 10−58



Scaffold protein with high hepatic expression. Affects lipogenic gene expression, hepatic lipogenesis, VLDL formation, and plasma lipids [37]


P < 10−55



Endothelial lipase that hydrolyzes HDL


P < 10−49



Selective sterol absorption in GIT and hepatic sterol excretion


P < 10−47



Rate-limiting enzyme in cholesterol synthesis pathway


P < 10−47



Hepatic secretory factor that inhibits LPL and endothelial lipase


P < 10−43



Scaffold protein (role in lipid metabolism unknown)


P < 10−38



Catalyzes cholesterol efflux from peripheral tissues and HDL formation


P < 10−33



Conversion from cholesterol to cholesteryl ester for formation of HDL


P < 10−33



Regulates LDLR activity


P < 10−28



Receptor protein involved in engulfment of apoptotic cells (role in lipid metabolism unknown)


P < 10−28



Protein of high hepatic expression that can reduce HDL levels [33•]


P < 10−25



Fatty acid biosynthesis


P < 10−24



Plasma protein (role in lipid metabolism unknown)


P < 10−24



Phospholipid transfer protein; plasma protein that transfers phospholipids from triglyceride-rich lipoproteins to HDL


P < 10−22



GalNAc-transferase involved in o-linked oligosaccharide biosynthesis, which can regulate HDL levels [33•]


P < 10−21

aGenes in which there is existing evidence for gene–diet interactions on lipid levels are indicated in bold

bWhen the mechanism by which the gene regulates lipid traits is known, prior to GWAS, it is marked as known; if the gene was not previously implicated in the regulation of lipid traits prior to GWAS and a pathway of regulation has been targeted by subsequent analysis/experiments, it is marked as novel; if the gene was not previously implicated in the regulation of lipid traits and a mechanism has not yet been identified, it is marked as unknown

cP value for the association with the lead trait from the meta-analysis by Teslovich et al. [33•]

GIT gastrointestinal tract; GWAS genome-wide association study; HDL high-density lipoprotein; HDLC HDL cholesterol; LDL low-density lipoprotein; LDLC LDL cholesterol; LDLR LDL receptor; TC total cholesterol; TG triglyceride; VLDL very low-density lipoprotein

Loci identified in GWAS now account for about 25 % to 30 % of genetic variance in each trait (or 10 %–13 % of total variance in the trait) [33•]. There are likely to be more SNPs with small effect sizes that have not yet been identified with current GWAS methods [33•]. Rare SNPs of larger effect size will also not have been captured by GWAS and will contribute to the estimate of genetic variance [38]. Variations in loci do not occur in isolation, and combinations of polymorphisms are likely to act together to modulate lipid levels. It has been demonstrated that SNPs identified in GWAS have a cumulative effect on lipid traits, such that a linear increase in the number of risk alleles for a given trait corresponded with an increasingly more adverse lipid profile [39•, 40, 41]. However, this simple additive approach does not explore the potentially multiplicative effects of two or more SNPs. A simple example is seen for the APOE polymorphism, which has been identified in GWAS to be associated with all lipid traits [29, 30, 31, 33•], but with an effect size of 0.18 mM per risk allele for LDL cholesterol [33•]. However, two SNPs define the common ApoE, and when combined, the difference between individuals of the lowest risk isoform compared with those of the highest risk isoform was greater than 1 mM for LDL cholesterol [42]. Variance in a number of SNPs, in a number of genes, and of a number of different lipid metabolic pathways is likely to have complex and interacting effects. To fully explore these interactions in a comprehensive and systematic way will require novel data analytical methods and possibly increased computing power.

GWAS do not take into account environmental modifiers (eg, diet) in the identification of genes associated with a given trait. A number of different methods for analyzing the data are beginning to emerge to address this. Manning and colleagues [43] have proposed a joint meta-analytical method for simultaneously testing gene and gene–environment interactions in cross-sectional data in order to identify genetic loci that are impacted upon by a given environmental exposure on a trait of interest. These analyses could be conducted in studies in which data on dietary exposures are available in very large cohorts, along with GWAS data. However, this method is limited to testing a single dietary exposure at a time, and it remains to be seen how effective this approach will be in identifying gene–diet interactions that modify lipid levels. In a separate approach, Igl and colleagues [44•] performed a GWAS with a lifestyle-adjusted model that included certain dietary covariates (eg, game meat, non-game meat, fish and milk products) in their models to identify SNPs associated with lipid levels. This approach identified SNPs that were sensitive to the inclusion of environmental factors into the model (gene + diet) but did not assess how diet could interact with genes (gene × diet) to modify blood lipids.

Diet–Gene Interactions to Modify Lipids for Individual Genes Identified in GWAS

The effect of a number of genes that were identified in GWAS to modify lipid responses to diets has been studied previously from a candidate gene approach. These include CETP, APOA1, APOE, LDLR, LPL, APOB, LIPC, LIPG, ABCG5/8, and LCAT (Table 1, Fig. 1), and these findings have been comprehensively reviewed elsewhere [23, 24]. Although there is some evidence of gene–diet interactions, there is considerable heterogeneity in response, and it is noted that these gene–diet interaction studies were generally of short duration, with a small sample size, and from studies not designed for this purpose. Therefore, these findings need to be replicated in further studies, including well-designed, adequately powered randomized controlled trials. However, for some of the top genes identified in GWAS, in which the roles in lipid metabolism have been clearly established (Table 1, Fig. 1), there have been surprisingly few reports of gene–diet interactions. For example, despite the clearly defined role that HMGCR plays in the regulation of cholesterol synthesis, there has been a scarcity of reports on variation in this gene interacting with dietary factors to modulate lipid levels. In one cross-sectional analysis of interactions between dietary fat and a variant of the HMGCR gene, carriers of the variant appeared to have higher serum triglyceride levels to greater saturated fat intake but no effect on other lipids [45]. There has also been a scarcity of reports on effects of dietary interactions with ANGPTL3, PLTP, PCSK9, and ABCA1 (Table 1).

Of the genes identified in GWAS that had not been implicated previously in the regulation of triglyceride and cholesterol levels (Table 1), some preliminary studies into possible gene–diet interactions have been conducted. In observational, cross-sectional studies, variants in the FADS genes (which encode desaturase enzymes in long-chain fatty acid biosynthesis) have been reported to interact with different levels of dietary n-3 and n-6 polyunsaturated fatty acids to alter serum cholesterol levels. In people who consumed a high proportion of n-3 polyunsaturated fatty acids (compared with the population median), the variant allele was associated with lower serum total and non-HDL cholesterol, translating to a protective effect [46, 47]. However, as these findings were cross-sectional, the impact of these gene variants on lipid concentrations over time or in response to changes in diet remains to be determined. Also, in a cross-sectional analysis in a small cohort (n = 379), there was no interaction observed between variants of the GCKR gene and the level of polyunsaturated, saturated, or monounsaturated fat in the diet on plasma triglycerides [48]. In an intervention trial, variation in the GCKR gene was shown to modify triglyceride responses to an intensive lifestyle intervention of increased physical activity, weight loss, and a low-fat diet such that there was a greater reduction in carriers of the variant [49]. However, it cannot be determined from this study whether diet, physical activity, or the effects of weight loss (or all factors) are important determinants. In acute postprandial studies, individuals who carry the GCKR risk variant were found to have an elevated postprandial triglyceride response to a high-fat meal (in combination with another variant in the apolipoprotein A-V [APOA5] gene) [50, 51], indicating a probable cumulative effect of high-fat diets to increase triglyceride levels in individuals with variants in these genes.

For many of the novel loci recently identified to be associated with lipids, the gene within the loci underlying the mechanistic effect may not have been identified, and the mechanism underlying the regulation of lipid levels is unknown (Table 1). These loci have not yet been studied in terms of gene–diet interactions to determine the modulation of effect on lipid levels.

There are some notable examples of genes that have been identified through candidate gene studies as important moderators of diet to modulate lipid levels, but that were not identified in GWAS. These include the transcription factors peroxisome proliferator-activated receptor (PPAR) alpha and gamma, which regulate the expression of several genes involved in lipid metabolism [13]. Variants in the PPAR alpha gene have previously been shown to interact with polyunsaturated fatty acid intake to enhance improvements in total and LDL cholesterol [52] and triglycerides [53] among people with a higher proportional intake of n-3 and n-6 polyunsaturated fatty acids. A PPAR gamma SNP was also found to enhance the reduction in serum triglyceride in response to a dietary intervention to increase n-3 polyunsaturated fat intake [54]. It is likely that these genes become important modifiers of lipid levels in response to particular dietary exposures, and were therefore not identified in GWAS that did not take into account diet. More examples of such genes are likely to be discovered when dietary factors are included.

The proportion of variance in lipid responses to dietary change that can be explained by variation in a single gene is not likely to be greater than 10 % [23]. We have examined the potential cumulative effect of SNPs identified in GWAS to modify lipid responses to a dietary intervention of reduced dietary saturated fat. We showed that a cumulative genetic predisposition score composed from simple addition of these SNPs did not modify the response to changes in dietary saturated fat [39•]. We had insufficient power to explore the effects in individual SNPs, but a preliminary analysis revealed no obvious modifying effect of any of the individual SNPs [39•]. A similar approach was used to examine the combined effects of nine common genes on the change in lipid responses to statin therapy [55•]. In this analysis, the combined genetic score appeared to modify the response to statin therapy such that the response was greater—the higher the genetic predisposition to low HDL cholesterol and high LDL cholesterol—in women only [55•]. Although this study was not diet related, it illustrates that the modifying effect of the interaction between genes and environmental modifiers will depend on the mechanism by which the environmental factor acts to alter plasma lipids. Because different dietary factors act to regulate lipid levels via different metabolic pathways, the effect of SNPs on the change in lipids is likely to be complex, with different combinations of SNPs responding differently to various dietary exposures. New analysis and modeling techniques are required to comprehensively investigate interactions of multiple SNPs and multiple dietary components.

Studying the effects of genes that modify lipid responses to diets in a controlled intervention setting, by its nature, means the cohort is limited to a small size, which can limit power to detect gene–diet interactions. The use of large, longitudinal, population-based studies may offer an alternative. Following GWAS that identified the SNPs most strongly associated with body mass index (BMI), a study was conducted that used the cumulative score of these SNPs to test gene–lifestyle interactions [56]. This study effectively demonstrated that physical activity (assessed by a self-administered questionnaire) attenuated the genetic predisposition to BMI in a cross-sectional, population-based sample of 20,430 individuals, and furthermore limited the change in BMI over time [56]. A similar approach could be used with lipid-associated genes identified in GWAS in which measures of dietary intake are also available. Dietary exposures such as total fat, saturated fat, and high carbohydrate, which are known to impact upon lipid levels, could be used to test the effect on the cross-sectional association between genetic predisposition and an unfavorable lipid profile, and the change in lipids over time.

GWAS on Interventions

The majority of GWAS to date have been conducted in cross-sectional data; it is therefore unsurprising that the loci identified as most important for variation of population lipid levels may have less of a moderating effect on changes in plasma lipids in response to diet [39•]. Some small GWAS have been performed on dynamic lipid responses to lipid-lowering drugs [55•, 57, 58]. A combined analysis of such studies has shown that there was no overlap between the SNPs associated with variation in the lipid levels of the cohort at baseline or after treatment compared with those that associated with the change in lipid levels [58]. This illustrates that the genes that are most important in modifying changes in lipids in response to lipid-lowering drugs are likely to be different from those that underlie differences in population lipid levels. Likewise, dietary-induced changes are likely to act via different mechanisms to pharmaceutical agents. Due to the nature of highly controlled dietary intervention trials, the size of the cohort is small in terms of genetic analyses. To combat this, it is likely that data from a number of similar studies would need to be combined to achieve the required power to assess diet–SNP interactions. Challenges to be encountered by this approach will be the heterogeneity in trial design with regard to changes in diet, participant characteristics, duration, and rigor.


GWAS have been central to recent advances in the understanding of genetic mechanisms underlying variation in blood lipid levels. However, as of yet, these studies have done little to enhance our understanding of gene–diet interactions and their modifying effect on blood lipids. Data analytical tools are being developed to incorporate dietary factors into GWAS studies in order to identify genes that interact with dietary factors to modify lipids. It is emerging that SNPs identified in cross-sectional GWAS may not be the most important modifiers of lipid changes in response to changes in diet or other environmental factors, and GWAS are now beginning to be conducted to identify the SNPs related to change in measured traits. These methods applied to large, controlled dietary intervention trials will help identify SNPs that interact with particular dietary exposures, although it is likely that data from many trials will need to be combined to obtain adequate power. The SNPs identified may differ, depending on the nature of the dietary exposure (eg, energy reduction, saturated fat reduction, increase in carbohydrate), and this in turn will increase our understanding of how these different diets alter blood lipids and the pathways involved. It is clear that advances will be required in the way data are analyzed in order to handle the vast amount of data that will take into account the complex interactions between different dietary exposures and different genes.



The authors wish to thank Dr. L. Hodson for discussions on lipid metabolism. Both authors are supported by the UK Medical Research Council (grant code U105960389).


Dr. Jebb has served on advisory boards for Tanita Ltd., PepsiCo, Nestle, Coca-Cola, Californian Almond, and Heinz; has been employed by the Medical Research Council; has received grant support from the NPRI, World Cancer Research Fund International, MRC Public Health Sciences Research Network, Tanita Ltd., Weight Watchers International, the Food Standards Agency, and the European Union; and has received payment for lectures given (including service on speakers’ bureaus) and preparation of nutrition-related articles for Rosemary Conley Enterprises.

Dr. Walker reported no potential conflicts of interest relevant to this article.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.MRC Human Nutrition Research, Elsie Widdowson LaboratoryCambridgeUK

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