Intersection of the nutrition science and clinical axes defining the response phenotype

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Only three of the 51 studies summarized in the AHRQ review reported any attempt to genotype the subjects for any polymorphisms known to be involved in cholesterol metabolism (these three studies identified subjects with familial hypercholesterolemia). Two of the 51 studies investigated soy protein efficacy in type II diabetic subjects. The criteria for study subject selection in the rest of the 51 studies were fairly broad and included men and pre- and post-menopausal women of various ages and body mass index. Baseline clinical measurements were made in most, but not all, cases after a defined dietary run-in period. The run-in and intervention diets were not standardized across the studies and varied significantly (26 studies used habitual diets, 13 studies specified diets and nine, two and one study used the NCEP Step I, Step II and Therapeutic Lifestyle Changes (TLC) diets, respectively). In effect, the AHRQ endeavored to draw general conclusions on the ability of soy protein to lower serum cholesterol as a function of (variable soy protein) x (variable background diet) x (variable subject genetics). Even a potent cholesterol-lowering drug would be challenged in such a scenario and it should not be surprising that the cholesterol-lowering activity of soy protein regresses to some low mean effect with a small signal to noise ratio. When well defined and carefully characterized soy proteins (or peptides) finally become available, the research community will be in a unique position to ask the nutrige-nomic question: what is the response phenotype for soy protein's effect on serum cholesterol? In the interim, the molecular pharmacology literature is quite instructive in terms of candidate genes and single nucleotide poymorphisms (SNPs) that may be involved in defining the response phe-notype.

Polymorphisms in regulatory genes in the cholesterol synthesis pathway (e.g. 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoA reductase) or absorption and transport pathways (e.g. Niemann-Pick C1-like protein 1 precursor (NPClLl)) have been shown to affect an individual's response to different cholesterol-lowering drugs such as pravastatin and ezetimibe (Chasman et al., 2004; Simon et al., 2005). In addition, a polymorphism in the sterol receptor element binding protein 1c (SREBP-1c gene) has recently been found to be associated with an increased cholesterol-absorption inhibition in response to ezetimibe in humans (Berthold et al., 2007). Polymorphisms in other genes involved in cholesterol absorption, namely, the half transporters ABCG5 and ABCG8, have also been shown to affect cholesterol absorption and synthesis in women during weight loss (Santosa et al., 2007). The molecular precedents have clearly been set; thus, it would not be surprising if individuals with these types of genetic variations also exhibited differential responses to soy protein as well.

The cholesterol-lowering response to soy protein has been shown to be influenced by apolipoprotein E genotype in familial hypercholesterolemic (FH) subjects; specifically, E3/E3 and E3/E4 subjects experience significantly larger decreases in total cholesterol than E3/E2 subjects (Gaddi et al., 1991). This observation is consistent with an ability of soy protein to depress the low level of hepatic LDL receptor activity in individuals having the more efficient LDL receptor binding E3 and E4 alleles. However, not all studies that have segregated cholesterol-lowering data on the basis of apoE alleles have shown this difference for soy protein (Vigna et al., 2000). This could indicate that the LDL receptor gene defect in the FH subjects followed in the Gaddi study could involve an epistatic effect on the apoE genotype. These data also indicate that the apoE genotype could be a 'response modifier' rather than a direct participant in the mechanism whereby soy protein affects cholesterol homeostasis. The literature on the genetics of response modifying apolipoproteins and their impact on dietary interventions is rich and well documented (Ordovas, 2006).

As discussed earlier in this chapter, isolated soy isoflavones do not appear to exert a significant LDL cholesterol-lowering effect except in subjects who metabolize daidzein to equol (Clerici et al., 2007). In a post-hoc analysis of data from a soy dietary intervention study, Meyer et al. (2004) noted that the cholesterol 'responders' in their soy intervention study were also equol producers. The equol producing phenotype appears to be under genetic control, and the specific intestinal bacteria responsible for the dehy-droxylation and reduction of daidzein to equol have not been identified (Frankenfeld et al., 2004). Setchell et al. (2002) proposed 'bacterio-typing' people according to their ability to produce equol prior to randomization into dietary intervention studies that use isoflavone containing ingredients, since the production of equol may well be a confounding variable in such studies. Another response modifier in the equol story may be polymorphisms of estrogen receptor i (ERi) recognizing that equol is an estrogenic compound. For instance, pre-menopausal and post-menopausal women on exogenous estrogen carrying the (G/A) genotype of the ERi 1082G>A polymorphism have significantly lower total and LDL cholesterol than their counterparts with the (G/G) genotype (Almeida et al., 2005) and post-menopausal women bearing the A/A genotype of the ERi splice variant polymorphism (ERi (cx) Tsp509I) show significantly higher HDL cholesterol levels after isoflavone consumption than their G/A or G/G counterparts (Hall et al., 2006). Thus, it is important to consider the microbiome in host lipid metabolism. This is underscored in a recent report from Martin et al. (2007) who demonstrated that the specific gut microbial populations in mice significantly influenced bile acid metabolism and activity and that substitution of a conventional mouse microbiome with a human infant microbiome altered the bile acid metabolism such that there was a significant reduction in plasma lipids and increased hepatic lipid concentrations.

In conclusion, genetic factors are clearly involved in determining the 'response phenotype' to soy protein in clinical studies. Future studies using defined soy proteins would benefit greatly from using genetically characterized study populations in standardized interventional trials

FIGURE 2.6 The pre-emptive nutrition model in practice with soy protein.

(Figure 2.6). In addition, care should be taken to identify environmental factors that can influence the 'response phenotype' through gene-environment interactions (such as smoking, alcohol consumption and physical activity). The latter is described in detail in Chapter 1.

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