Next Article in Journal
Melanoma Brain Metastasis: Mechanisms, Models, and Medicine
Next Article in Special Issue
Role of n-3 Polyunsaturated Fatty Acids in Ameliorating the Obesity-Induced Metabolic Syndrome in Animal Models and Humans
Previous Article in Journal
Restoration of Immune Responsiveness to Glioma by Vaccination of Mice with Established Brain Gliomas with a Semi-Allogeneic Vaccine
Previous Article in Special Issue
Vitamin C and Heart Health: A Review Based on Findings from Epidemiologic Studies
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Advances in Integrating Traditional and Omic Biomarkers When Analyzing the Effects of the Mediterranean Diet Intervention in Cardiovascular Prevention

Cardiovascular Risk and Nutrition Research (REGICOR Group), Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), 08003 Barcelona, Spain
CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), 28029 Madrid, Spain
Department of Clinical Sciences, Lund University, 22100 Lund, Sweden
Department of Internal Medicine, Skåne University Hospital, 22241 Lund, Sweden
Department of Nutrition and Food Sciences, University of Navarra, 31009 Pamplona, Spain
Department of Preventive Medicine and Public Health, University of Navarra, 31009 Pamplona, Spain
INSERM U1048, Institute of Metabolic and Cardiovascular Diseases (I2MC), Rangueil Hospital, 31442 Toulouse, France
Department of Preventive Medicine and Public Health, University of Valencia, 46010 Valencia, Spain
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2016, 17(9), 1469;
Received: 19 June 2016 / Revised: 8 August 2016 / Accepted: 26 August 2016 / Published: 2 September 2016
(This article belongs to the Special Issue Advances in Nutritional Epidemiology)


Intervention with Mediterranean diet (MedDiet) has provided a high level of evidence in primary prevention of cardiovascular events. Besides enhancing protection from classical risk factors, an improvement has also been described in a number of non-classical ones. Benefits have been reported on biomarkers of oxidation, inflammation, cellular adhesion, adipokine production, and pro-thrombotic state. Although the benefits of the MedDiet have been attributed to its richness in antioxidants, the mechanisms by which it exercises its beneficial effects are not well known. It is thought that the integration of omics including genomics, transcriptomics, epigenomics, and metabolomics, into studies analyzing nutrition and cardiovascular diseases will provide new clues regarding these mechanisms. However, omics integration is still in its infancy. Currently, some single-omics analyses have provided valuable data, mostly in the field of genomics. Thus, several gene-diet interactions in determining both intermediate (plasma lipids, etc.) and final cardiovascular phenotypes (stroke, myocardial infarction, etc.) have been reported. However, few studies have analyzed changes in gene expression and, moreover very few have focused on epigenomic or metabolomic biomarkers related to the MedDiet. Nevertheless, these preliminary results can help to better understand the inter-individual differences in cardiovascular risk and dietary response for further applications in personalized nutrition.

Graphical Abstract

1. Introduction

Every day, the concept of “Precision Medicine” or “Personalized Medicine” is becoming more widely known and professionals from various fields are working hard to ensure that the promises that this new vision of medicine, which proposes customizing healthcare, with medical decisions, practices, and/or products tailored to the individual patient, are fulfilled. To do so, omics are essential. Although the concept of precision medicine has been more focused on curing, customizing prevention is also being proposed, given that it is known that, for the health care system, prevention and health promotion is far more cost effective than cure. The concept of “Precision Health” is, therefore, emerging in an attempt to build up a more global vision. Both within Precision Medicine and Precision Health, nutrition plays an essential role and, therefore, recently work has also started on the concept of “Precision Nutrition”, which specifically refers to integrating omics in nutrition so as to achieve a better prevention and/or treatment of disease based on a more customized diet. However, so that Precision Nutrition can become a reality, much more research into integrating the new omics into nutritional epidemiology needs to take place. This will allow us to gather new knowledge that we will gradually be able to apply in this new field. For some diseases, such as cardiovascular diseases (CVD), some important studies have already been undertaken that have allowed us to advance in this knowledge [1,2,3,4,5]. Nevertheless, these reports have to be accompanied by more so as to provide a high level of scientific evidence and to minimize biases. To do so, omics have to be integrated into the dietary controlled randomized trials that are undertaken in order to contribute new knowledge on the influence of diet on the risk of CVD.
These trials are relatively simple to undertake for what are called intermediate phenotypes of cardiovascular risk (plasma lipid concentrations, inflammation markers, blood pressure, etc.). However, for final phenotypes of CVD (CVD incidence, mortality, etc.), these trials are expensive and complex, given that they require a long latency period from when the intervention began to when the so-called hard events (myocardial infarction, stroke, etc.) take place. Fortunately, the results of the PREDIMED study (PREvención con DIeta MEDiterránea)—a randomized, controlled nutritional intervention trial [1]—aimed at assessing the influence of the Mediterranean Diet (MedDiet) on hard cardiovascular events, has helped not only to provide a higher level of evidence on the effects of the MedDiet on cardiovascular prevention, but has also encouraged various research groups throughout the world to begin or plan the design of large nutritional intervention studies focusing on hard cardiovascular events. Those studies that have been designed within the new omics era are now incorporating all these new technologies so as to provide better knowledge. The knowledge that omics can provide to nutritional studies of the MedDiet and CVD is twofold: on the one hand providing more information on the molecular mechanisms through which the MedDiet exercises its beneficial effects and, on the other hand providing information on omic biomarkers (genomic, epigenomic, metabolomic, etc.) for application in Precision Nutrition. In general, in nutritional epidemiology of CVD, three types of biomarkers are used: (1) Biomarkers of dietary exposure; (2) Biomarkers of nutritional status; and (3) Biomarkers of health/disease [6,7]. In this work, we will review present knowledge from randomized trials with the MedDiet, which provide the first scientific level of evidence, as well as the traditional biomarkers of health/disease status and will go deeper into new omics (genomics, transcriptomics, epigenomics, metabolomics) as they promise to revolutionize the identification of new biomarkers (of dietary exposure, of nutrition status, and of health/disease) in nutritional studies into CVD, to present the advances in integrating traditional and omic biomarkers when analyzing the effects of the MedDiet in CVD prevention.

2. Advances in Nutrition: Traditional Mediterranean Diet and Cardiovascular Risk

In clinical practice, main efforts are focused on blood pressure and lipid profile check-ups, management of diabetes and obesity, and promotion of smoking cessation. A healthy diet pattern, such as the traditional MedDiet, can be a useful and complementary tool, together with undertaking of physical exercise, to obtain better control of these risk factors. The PREDIMED study was the first primary prevention, randomized, controlled trial to test the long-term effects of the MedDiet on CVD incidence [1]. Participants were randomized into one of three diets: (1) MedDiet supplemented with extra-virgin olive oil (EVOO); (2) MedDiet supplemented with nuts; and (3) control diet (advice on a low-fat diet). The PREDIMED study ( has provided evidence, for the first time, of the primary prevention for cardiovascular events as a hard composite endpoint (myocardial infarction, stroke, and cardiovascular mortality) [1,2], stroke [1], atrial fibrillation [3], type-2 diabetes [4], and peripheral vascular disease [5], in high cardiovascular risk individuals. We will review present knowledge from randomized trials with the MedDiet, which provide the first scientific level of evidence, on traditional biomarkers of health/disease status and will go deeper into the new omics—genomics, transcriptomics, epigenomics, metabolomics, etc.—as they will be crucial in preventing and treating CVD [7].

3. Effects of the MedDiet on Traditional Biomarkers (Classical and Non-Classical Risk Factors)

A decrease in systolic and diastolic blood pressure in both PREDIMED MedDiet interventions was observed after a three-month intervention compared to each baseline [8]. Recently, we have reported that long-term intervention (4.8 years of median follow-up) with a non-energy restricted MedDiet (observed for both groups: one supplemented with EVOO and the other with nuts) did not significantly increase body-weight in comparison with the control low-fat group [9]. Moreover, intervention with MedDiet had a more favorable effect on the slight increase in waist circumference observed as a trend in the three groups [9]. Glucose-related and lipid profile parameters also improved after both three-month MedDiet interventions versus their baselines and versus the low fat diet control group in PREDIMED participants [8,10]. Likewise, lipoprotein subclasses were shifted to a less atherogenic pattern by both MedDiet consumptions versus their baselines, especially the one enriched with nuts [11]. Beneficial effects of the intervention with the MedDiet have also been observed in other populations. Thus, MedDiet intervention, in chronic renal failure patients in Algeria, improved triglycerides, and total cholesterol concentration versus the control group [12] and an enhancement in lipid profile and fibrinogen was observed after the MedDiet versus its baseline [12]. Likewise, in and intervention study carried out in Greece, hypercholesterolaemic an improvement in adherence to the MedDiet in hypercholesterolaemic subjects, was associated with a reduction low-density lipoprotein (LDL) cholesterol, triglycerides, systolic and diastolic blood pressure, coagulation markers, as well as with an increase in high-density lipoprotein (HDL) cholesterol concentration [13]. Accordingly, a meta-analysis of high quality trials with MedDiet, in metabolic syndrome volunteers, showed significant effects of the MedDiet in systolic and diastolic blood pressure, homeostasis model assessment (HOMA), glucose, HDL cholesterol, and triglycerides [14]. Also in Spanish patients with metabolic syndrome, intensive life-style intervention counseling, including MedDiet, resulted in improvements in abdominal circumference, blood pressure, and HDL-cholesterol [15]. Similarly, an updated meta-analysis including 29 intervention trials, found that intervention with MedDiet significantly decreased waist circumference, triglyceride concentrations, fasting glucose, and blood pressure (systolic and diastolic). These beneficial effects were higher in studies of longer duration and carried out in Europe [16]. Although none focused on primary CVD prevention, but on secondary prevention, the CORDIOPREV Study (CORonary Diet Intervention with Olive oil and cardiovascular PREVention), is an ongoing intervention trial with MedDiet versus a low-fat diet on coronary patients from Spain aimed at analyzing the effects of these intervention both on traditional cardiovascular biomarkers and CVD recurrence [17]. Interesting results will be obtained in the next few years. Despite their overall value, some classical cardiovascular risk biomarkers tend to lose predictive power in specific populations such as the elderly [18]. In addition, a percentage of individuals who develop CVD have only one or none of the classical CVD risk factors [19,20] and might even prove to have a worse prognosis [21]. There is, therefore, interest in identifying new biomarkers linked to residual risk for CVD development [22].
Atherosclerosis, which is the major underlying cause of CVD and is associated with many other chronic degenerative diseases, involves several highly interrelated processes, including lipid metabolism, platelet activation, and thrombosis, endothelial dysfunction, inflammation, oxidation, vascular smooth cell activation, matrix metabolism, remodeling, and genetic factors [23]. At present, research into these processes is leading to the emergence of new non-omics and omics biomarkers related to CVD.
LDL cholesterol concentration has long been associated with cardiovascular risk. Moreover, oxidative modifications of the LDL particle may play a key role in atherosclerosis [24] and have been associated with the severity of coronary heart disease [25]. Several follow-up studies have reported that systemic circulating oxidized LDL could be a predictor of acute coronary syndromes in the general population [26,27]. In PREDIMED decreased oxidized LDL was observed three months and one year after both MedDiets versus each baseline [28,29]. In addition, NT-proBNP (a) and Lp (a) plasma concentrations, also presented a one-year reduction compared with participants assigned to a low-fat diet [29]. Although in all the intervention groups of PREDIMED systemic F2-isoprostanes and 8-oxo-deoxiguanosine were reduced after a one-year intervention, in women with metabolic syndrome who improved their diet towards a MedDiet pattern, the decrease in 8-oxo-deoxiguanosine was greater [30]. In addition, both MedDiet interventions increased plasma non-enzymatic antioxidant capacity after a one-year intervention, within the frame of the same study [31]. In a longer three-year follow-up, both MedDiets—particularly the EVOO-rich one—were associated with higher levels of plasma antioxidant capacity [32]. In other studies, it has been reported that adherence to a MedDiet may protect against artery wall production of inflammatory mediators [33]. In this regard, C reactive protein, interleukin-6, soluble vascular cell adhesion molecule 1 (sVCAM1), and soluble intercellular cell adhesion molecule 1 (sICAM1) concentrations decreased after both MedDiets three-month interventions in the PREDIMED study [8]. In addition, T lymphocytes sVCAM1 and C-reactive protein only decreased after the MedDiet supplemented with EVOO, whereas interleukin-6, sVCAM1, and sICAM1, increased in the control group [8]. Similarly, in another study, a two-month hypocaloric MedDiet resulted in reductions in proinflammatory markers, such as the adipose tissue derived retinol-binding protein 4, one of the most important adipokines linked with the metabolic syndrome [34]. Moreover, recent data from the PREDIMED study have demonstrated that has demonstrated the anti-inflammatory effects short-term anti-inflammatory effects of the MedDiet also remain significant after a long-term (more than three years) intervention period [35]. Also it has been reported that, when a habitual diet high in monounsaturated fatty acid, such as the MedDiet, is consumed, a lower post-prandial increase of coagulation factor VII (FVIIc) has been reported in other studies [36,37]. Moreover, some genetic polymorphisms have been linked to this observation [38], showing the interest in the integration of omic data. Although those biomarkers are mainly associated with CVD, some of them may have an important role in other diseases such. Thus a recent meta-analysis has investigated the association between the MedDiet and the main chronic diseases and has found a consistent protection of the MedDiet not only against CVD, but also on cancer [39].

4. Omics Integration and New Omic-Based Biomarkers

Despite the progress in the identification of the associations between diet and classical and emerging CVD biomarkers, genetic markers are becoming increasingly important as research advances on inter-individual variability [40,41]. In particular, genetic polymorphisms in relevant genes have been related with each one of the traditional biomarkers previously mentioned. In addition, the underlying mechanisms by which the MedDiet exercises its beneficial effects on CVD are not well known (Table 1) [42,43,44,45,46,47,48]. It is thought that omics integration in the studies analyzing nutrition and CVD will provide new clues regarding these mechanisms [49]. All of this underlines the importance of the joint integration of the several omics and increases interest in undertaking more integrated studies [50]. A deeper integration of omics technologies along with new high-throughput computational methods and a systems biology approach will allow us to identify a better list of biomarkers useful for diagnosis and therapies of cardiovascular diseases and related gene-diet interactions [49,51].
The PREDIMED study provides an ideal framework for obtaining new knowledge on classical and new omic-based biomarkers not only at a single-omic, but also at an integrative multi-omics level. Omic-based biomarkers can be classified as detailed in Table 2 [40,52,53,54,55,56,57,58,59,60].
Omic biomarkers require the obtaining of different types of biological samples for their measurements. The most commonly employed are blood, saliva and urine, although other samples are used such as adipose tissue or other tissues depending on the aims of the study. Although obtaining DNA for genotyping allows general determinations that are independent of the tissue used. Obtaining DNA for epigenetic analyses or RNA for transcriptomic analyses implies an additional difficulty, as methylation and gene expression varies depending on the tissue analyzed. Despite these limitations, it is suitable to obtain and store biological samples in any new nutritional epidemiology studies that are started.

5. Omic Biomarkers in the PREDIMED Study and in Other Studies: Gene–MedDiet Interactions

5.1. Genomic Biomarkers

Currently, we have increasingly faster and cheaper technology to analyze genetic variability. Thus, we have progressed from studies on candidate genes, in which a few genetic polymorphisms, basically SNPs, were analyzed to genome-wide association studies (GWAs), in which hundreds of thousands of SNPs are analyzed [40]. Moreover, next generation sequencing will provide more detailed information. However, it is still expensive for large samples. Therefore, one of the widely used approaches to combine genetic information provided by different SNPs is the use of the so-called “genetic risk scores” (GRS). There are two types of GRS: unweighted and weighted. The unweighted GRS are built by summing up the number of risk alleles (0 for wild-type, 1 for heterozygous subjects, and 2 for homozygous) genotypes for the selected SNPs. In the weighted approach, the loci are also corrected by the strength of the corresponding association, using the regression coefficients previously obtained for the selected associations.
Genomic biomarkers can be either of intake or of effect or disease risk. Concerning biomarkers of disease, our results in the PREDIMED study, in agreement with other investigations [40,41], have shown an important genetic heterogeneity in determining classical cardiovascular risk factors such as plasma lipid concentrations [61,62,63,64], fasting glucose [62,63,65], inflammatory markers [66], blood pressure [67], and obesity-related measurements [65,68]. Therefore, these results add more evidence to the idea that both traditional and omic biomarkers should be integrated and analyzed together in order to provide better information. In addition, in the PREDIMED study we have found several gene-diet interactions in determining such intermediate cardiovascular risk phenotypes or also called traditional biomarkers [62,63,64]. Although several studies in other populations have analyzed gene-diet interactions focusing on specific foods or macronutrients, very few studies have analyzed gene-diet interactions focusing on the whole MedDiet pattern. Frequently, in observational studies, adherence to MedDiet has is measured using several validated questionnaires [69,70,71] consisting of several questions related to typical foods of the MedDiet. In the PREDIMED study, adherence to MedDiet was measured using a validated 14-item questionnaire [71]. A higher score in this questionnaire indicated greater adherence, 9 points being the mean for the population. In the PREDIMED study, we focused on two SNPs strongly associated with obesity and type-2 diabetes: the FTO-rs9939609 and the MC4R-rs17782313 polymorphisms. We also calculated their additive GRS (unweighted). We found statistically significant gene-diet interactions between the FTO-rs9939609 SNP and the MC4R-rs17782313 SNP, separately, with the MedDiet score in determining type 2 diabetes risk at baseline. Likewise, we found a statistically significant gene-diet interaction between the GRS of these polymorphisms and the adherence to MedDiet in determining type 2 diabetes risk [65]. Thus, when the adherence to MedDiet was low (less than 9 points), these polymorphisms (the minor alleles in a dominant model) were associated with higher risk of type 2 diabetes, both individually and in the GRS. However, when adherence to MedDiet was high, these polymorphisms were not associated with greater type 2 diabetes risk, so reversing genetic susceptibility [65]. Likewise, some other observational studies in different populations have reported gene-MedDiet interactions on body-weight [72,73] or oxidation markers [74].
However, these gene-diet interactions were observed cross-sectionally and they could be subject to bias. The important thing is to discover in the PREDIMED study whether the intervention with the MedDiet is capable of modulating genetic effects in different cardiovascular phenotypes, so providing a higher level of scientific evidence. There are very few dietary intervention trials with MedDiet analyzing gene-diet interactions on cardiovascular phenotypes. We have summarized the current evidence of reported interactions in Table 3 [63,64,75,76,77,78]. Moreover, only the PREDIMED study has analyzed gene-diet interactions from intervention with MedDiet in determining hard cardiovascular end points (total CVD incidence, stroke, myocardial infarction, CVD mortality, etc.).
When we started to carry out these studies, there was no published study in the field of nutrigenetics that had analyzed a gene-diet interaction in determining the incidence of CVD using a randomized and controlled nutrition intervention trial.
In the PREDIMED study, we have found several gene-diet interactions in determining incidence of CVD. Among them, we would like to underline that found with the TCF7L2-rs7903146 C>T SNP and the dietary intervention (MedDiet versus control) in determining stroke incidence [63]. The TCF7L2-rs7903146 polymorphism has been associated with a higher risk of type 2 diabetes, as well as with CVD risk [79,80] in carriers of the variant T-allele. In the PREDIMED trial we showed that MedDiet intervention modulated the effects of the TCF7L2-rs7903146 C>T SNP on stroke incidence. Thus, TT subjects had a higher stroke incidence in the control group (compared with CC individuals), whereas dietary intervention with MedDiet reduced stroke incidence in TT subjects and no differences with CC subjects were detected. This result represents the first report showing that a dietary intervention, in this case the MedDiet, counteracts a genetic risk of stroke.
Moreover, focusing on myocardial infarction, although the results of the PREDIMED study did not show a statistically significant MedDiet protection for myocardial infarction incidence, in a nutrigenetic study in PREDIMED participants [64], we did show the protective effect of the MedDiet in individuals with a certain Max-like protein x (MLX) interacting protein like (MLXIPL) variant. In a previous GWAS [81], we found that the minor allele of the MLXIPL-rs17145738 SNP was associated with significantly lower triglyceride concentration. This SNP was intergenic and later a functional variant (rs3812316, C771G: Gln241His), in high LD with rs17145738, was described [82]. In PREDIMED, we observed a strong association between the rs3812316-MLXIPL and lower triglycerides concentrations in carriers of the minor allele [64]. Moreover, we found a significant gene-diet interaction for this SNP in hypertriglyceridemia. When adherence to MedDiet was high, the protective effect of the minor allele against hypertriglyceridemia was stronger. However, when adherence to MedDiet was low, the protection did not reach statistical significance. More importantly, the protective effect of the minor allele against myocardial infarction was only statistically significant in subjects in the intervention group receiving MedDiet.
In addition to these pioneering studies showing, for the first time, that intervention with MedDiet can counteract the higher risk of stroke associated with the minor allele for the TCF7L2-rs7903146 SNP [63] as well as to enhance the protective effect of the minor allele for the MLXIPL-rs3812316 SNP [64] against myocardial infarction, we have published other gene-MedDiet intervention interactions in determining total CVD or stroke [77,78] ,and we are obtaining novel, still unpublished, gene-MedDiet interactions that provide accumulative evidence of relevant dietary modulations on the genetic effects in the CVD risk determination.

5.2. Epigenomic Biomarkers

Epigenomics is term is used to describe a diversity of modifications to the genome that do not involve changes in DNA sequence and can result in alteration of gene expression [53]. It constitutes the missing link between genetics, the environment and the outcome phenotypes. The epigenetic marks are reversible, and may allow a quick adaptation to the environment. There are three categories of epigenetic biomarkers based on the main epigenetic regulators: DNA methylation, histone modification, and non-coding RNAs.
Most DNA methylation occurs at cytosine–phosphate–guanine (CpG) dinucleotides, providing marks in the genome by which genes are set to be transcriptionally activated or silenced [83,84] Although some studies have reported dietary modification in DNA-methylation related to diet in different studies [84,85,86,87,88,89], the heterogeneity of the interventions and the small sample size of these preliminary studies requires further replication to validate the findings. Moreover, although in some of these studies [85,89], intervention with MedDiet has been evaluated, taking into account that the main aim of these studies was to test the effect of an energy-restricted MedDiet on weight loss, the statistically significant changes in DNA methylation reported in these studies may be due both to the intervention with MedDiet and to the decrease in weight. Therefore, how the process of DNA methylation-demethylation is regulated by MedDiet remains unclear and more studies are needed to establish epigenomic biomarkers for methylation related to MedDiet intervention. In the PREDIMED study we are currently investigating some of these biomarkers. Similarly, alterations of histone modification (acetylation, etc.) have still to be explored in human intervention studies with MedDiet, and, therefore, new research is needed. In animal models, there is emerging evidence that a high fat diet intervention induces chromatin accessibility changes and that persistent chromatin accessibility changes are associated with histone post-translational modifications [90]. Likewise, the effect of the MedDiet intervention on circulating microRNA (miRNA) and their regulation of gene expression have been largely unexplored. miRNAs have emerged as crucial epigenetic regulators of many process related to CVD [91,92,93,94]. miRNAs are small (18–25 nucleotide) functional non-coding RNAs that regulate gene expression of their target mRNA in a post-transcriptional manner. miRNAs can be detected and measured in different tissues and miRNA expression may be cell specific. However, the discovery that microRNAs circulate in a stable form in plasma, facilitates that circulating microRNAs can serve as new omics biomarkers for CVD [94]. Currently, distinctive patterns of circulating miRNAs have been found for several intermediate and final CVD phenotypes including hypertension, diabetes, stroke, myocardial infarction, etc. [94,95,96]. Numerous groups of researchers are investigating the effect of several dietary interventions on the miRNA circulating profile associated with the CVD phenotypes. However, published results are still scarce and heterogeneous. Among them, it has been reported that subjects with prediabetes consuming a pistachio-supplemented diet in comparison with an isocaloric diet in a randomized crossover nutrition intervention trial, had lower circulating levels of miR-192 and miR-375 compared to the isocaloric diet [97]. Both miRNA have been directly correlated with fasting glucose and insulin. In terms of macronutrients, it has been reported that intervention with a high protein diet modifies the microRNA circulating profile of miR-223, in comparison with a normal protein diet [98]. Likewise, it has been shown that an eight-week trial with a normocaloric diet enriched with PUFAs is associated with changes in the miRNA circulating profile including decreased miR-328, miR-330-3p, miR-221, and miR-125a-5p; and increased miR-192, miR-486-5p, miR-19b, miR-106a, miR-769-5p, miR-130b, and miR-18a [99]. Despite these interesting results supporting the hypothesis that diet can influence the circulating miRNA profile, the influence of a whole dietary intervention with MedDiet on circulating miRNA remains to be evaluated.
Regarding miRNA expression in different tissues, interesting results have been obtained when analyzing miRNA expression in white blood cells (WBC) in metabolic syndrome patients receiving intervention with MedDiet in the RESMENA (The reduction of the metabolyc syndrome in Navarra-Spain) study. In this dietary intervention, the MedDiet was able to induce changes in the expression of let-7b and miR-155-3p (both associated with inflammatory parameters) in WBC [100].
On the other hand, the identification of the respective targets of the circulating or of the tissue-specific miRNAs may provide novel molecular insight. On this regard, SNPs in miRNA target sites have also been demonstrated to have allele-specific effects [101]. Thus, the minor allele of the SNP rs13702 T>C in the lipoprotein lipase (LPL) 3’UTR gene disrupts a miRNA recognition element seed site for the human miRNA-410, resulting in a gain-of-function and lower plasma triglyceride concentrations [101]. Regarding epigenetic biomarkers and omics integration, in the PREDIMED study we have reported a gene-diet interaction involving the mRNA-410 target site polymorphism in the LPL gene in determining plasma triglyceride concentrations and stroke incidence [77]. We observed a strong interaction of the rs13702-LPL miRNA target site SNP with monounsaturated fatty acids (MUFA) intake at baseline in determining fasting triglycerides in such a way that the decreasing triglyceride effect of the minor C allele was increased with MUFA intake. Accordingly, after a three-year follow-up period we obtained statistically significant gene-diet interactions with intervention with MedDiet on triglycerides, as expected. Thus, C-carriers had a greater decrease in triglyceride concentrations when allocated to the MedDiet group in comparison with the control group. Moreover, when analyzing the effect of the miRNA-410target site SNP on the incidence of CVD, we observed a significant protection of this SNP only in subjects of the MedDiet. These results are the first of further promising research in this integrative field.

5.3. Transcriptomic Biomarkers

There are very few intervention studies analyzing the effect of the whole MedDiet pattern on gene expression. Two intervention studies (including the PREDIMED and another study carried out on subjects with Crohn’s disease) have analyzed gene expression changes in the whole transcriptome using high density arrays [56,102] in response to the intervention with MedDiet, while others have focused on selected candidate genes and intervention with MedDiet or even analyzing the response of typical foods of the MedDiet such as olive oil [103,104,105,106,107]. The results of these studies have been extensively reviewed in recent work [108,109]. Briefly, despite the great diversity among studies, intervention with MedDiet or with EVOO has been related to decreased gene expression of several pro-atherosclerotic genes involved in vascular inflammation, foam cell formation, and oxidative stress [56,100,101,102,103,104,105,106,107]. In the transcriptomic analysis in the PREDIMED study [56] carried out in a subsample after a three-month intervention with MedDiets in comparison with the control group, we detected that the key pathways in the physiopathology of cardiovascular events, such as atherosclerosis, renin-angiotensin, nitric oxide and angiopoietin signaling, were modulated by MedDiet + EVOO, whereas hypoxia and endothelial nitric oxide synthases (eNOS) signaling pathways were modified by both MedDiet; while none of the pathways were modulated by the control group in blood cells. These scarce results are promising, but much work is needed to discover good transcriptomic biomarkers in response to MedDiet.

5.4. Metabolomic Biomarkers

The emergence of metabolomics is still recent compared to other omics, but its particular features have contributed greatly to its increasing use [59,110]. Currently, there are a growing number of metabolomic studies in the MedDiet field including the PREDIMED study as well as other studies in other populations [60,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118]. Metabolomics is currently used both to investigate markers of dietary intake and biomarkers of disease [115,116]. Plasma/serum and urine samples of participants are being used depending on the aims and technical issues and more work is needed to standardize the methods and results when comparing different studies on different samples. In the PREDIMED study, we have characterized the dietary walnut fingerprinting in urine samples using an high pressure liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC-q-ToF-MS) untargeted metabolomics approach [111]. Consumption of walnuts was characterized by 18 metabolites, including markers of fatty acid metabolism, ellagitannin-derived microbial compounds, and intermediate metabolites of the tryptophan/serotonin pathway. Likewise, we characterized the metabolic signature of cocoa consumption in PREDIMED participants [112]. Moreover, we assessed the effect of the MedDiet intervention on the urinary metabolome by comparing a sub-sample of non-diabetic subjects at one and three years of follow-ups [60]. We used multivariate data analysis methods (orthogonal signal correction (OSC) of partial least squares projection to latent structures (PLS) discriminant analysis (DA) (OSC-PLS-DA) and hierarchical clustering analysis (HCA)) to identify the potential biomarker discriminating groups. Our results showed that the most relevant hallmarks related to MedDiet intervention were related to the metabolism of carbohydrates (3-hydroxybutyrate, citrate, and cis-aconitate), creatine, creatinine, amino acids (proline, N-acetylglutamine, glycine, branched-chain amino acids, and derived metabolites), lipids (oleic and suberic acids), and microbial cometabolites (phenylacetylglutamine and p-cresol). Likewise, in the RESMENA study, an intervention study with two energy-restricted diets; a diet MedDiet and a control diet (low-fat) carried out on 72 subjects with metabolic syndrome features [113], showed that the MedDiet intervention resulted in significant changes in the plasma metabolic profile at two months (mainly phospholipids and lysophospholipids). However, differences were attenuated at six months.
Overall, our results show that metabolomics may allow the classification of individuals regarding their specific food consumption or dietary patterns. However, more studies are needed to improve the validity and precision of this classification.
On the other hand, we are using metabolomics to identify plasma biomarkers of cardiovascular disease and diabetes in PREDIMED participants. Thus, in a case-cohort study including all the cardiovascular events in the PREDIMED study that occurred after 4.8 years of median follow-up and a random sample of the cohort (10% of participants), we have been able to identify that metabolite profiles characterized by elevated concentrations of plasma acylcarnitines (mainly, short- and medium-chain acylcarnitines) are associated with higher incidence of cardiovascular diseases [117]. Moreover, in this study, we detected that MedDiet intervention modulates the association between baseline plasma acylcarnitines and further cardiovascular risk, mainly for stroke risk, in such a way that MedDiet interventions may diminish the risk of cardiovascular diseases associated with higher plasma concentrations of acylcarnitines before nutritional intervention. Similarly, using the same case-cohort design in the PREDIMED participants, we analyzed the effect of plasma of branched-chain amino acids (leucine, isoleucine, and valine) at baseline and cardiovascular disease risk in the follow-up [118]. We observed a higher incidence of stroke associated with higher branched-chain amino acid concentrations. Moreover, we detected a statistically significant interaction between these concentrations and intervention with MedDiet, in such a way that such intervention counteracted the metabolite effects on stroke. In addition to these metabolomics analyses focused on specific metabolites, a more comprehensive approach analyzing all the metabolites together is needed to better understand the relationships at the whole metabolome level.

5.5. Lipidomic Biomarkers

Lipidomics is a subfield of metabolomics that focuses on the overall study of molecular lipids within cells, tissues, and biological fluids. Lipids classification comprises hundreds of thousands distinct lipid molecules which play a role on cellular membranes, signaling molecules, and as energy sources [119]. In a recent revision of the new omics in cardiovascular prevention, lipidomics was included as relevant to evaluate the comprehensive lipid profile beyond the traditional biomarkers for plasma lipid concentrations, and it is increasingly being applied to the CVD field [120]. Although several studies have examined the influence of the diet on lipidomic biomarkers, the number of published studies analyzing the change in lipidomic biomarkers in response to the MedDiet or to its main components, is still very scarce (but the number can be increased if some metabolomics biomarkers are classified as lipidomic biomarkers instead). Among other human studies analyzing lipidomic biomarkers in response to diet, we can mention, as an example for the short-term (postprandial) intervention, the plasma lipidome study carried out to assess the post-prandial effects of dairy fat and soy oil in 16 men [121]. This study showed increased concentrations of plasmalògens (with antioxidant properties), after dairy but not soy meals. With regard mid-term assays, in a two-month trial with fatty fish or lean fish in individuals who had suffered a myocardial infarction or unstable ischemic attack, a lipidomic study was performed [122]. On one hand, species including including ceramides, lysophosphatidylcholines, glycerophospholipids, phosphatidylcholines, and lysophosphatidylethanolamines were decreased in the group on fatty fish diet, and on the other hand in the lean fish intervention cholesterol esters and specific long-chain triglycerides increased. In another study [123], the effects of n-3 fatty acid and polyphenol rich two-month diets on plasma and HDL particle lipidomic patterns, in high cardiovascular risk volunteers, were studied. Associations among clinical variables and lipid molecular species were described, mainly after the diets high in n-3 fatty acids and polyphenols.
Interestingly, a recent study has reported the effect of intervention with a whole diet (Nordic diet) versus a control diet on the fasting plasma lipidomic profile in subjects with metabolic syndrome [124]. Statistically significant changes in 21 plasma lipids were observed between the intervention groups at 12 weeks, including increases in plasmalogens and decreases in ceramides in the healthy Nordic diet group. However, at the end of the study, changes in lipidomic profiles did not differ between the groups. Analyzing the factors contributing to this, including small sample size or other genetic and environmental factors will help to better understand the results.

5.6. Multi-Omics Integration in the Response to MedDiet and Cardiovascular Risk

Currently, there is a great interest in omics integration in nutritional epidemiology and cardiovascular prevention [125,126]. Some recent studies in animal models [126] and plants [127] have provided examples of integrated multi-omic analyses, and there are some observational human studies integrating two [128,129,130,131] or three omics (genomics, epigenomics, and metabolomics) [132]. However, multi-omic integration in large nutritional intervention studies is still extremely scarce. In addition to the high cost limitation of obtaining omics data from large sample sizes, there are also important computational limitations to integrating multidimensional omic data coming from GWAs, epigenome-wide methylation studies, genome-wide-transcriptomics, metabolomics, proteomics, and even metagenomics [133]. However, important advances are coming [134,135] and the PREDIMED study provides an ideal framework for multi-omics analyses and for obtaining new knowledge on Nutritional Genomics and its association with incidence of cardiovascular diseases as well as for better understanding the contribution of intermediate cardiovascular biomarkers. Currently, we and other groups are working on such integration in the field of the responses to the MedDiet intervention in determining cardiovascular biomarkers and disease risk to provide novel and interesting findings.

6. Conclusions

In summary, the protective effect of the MedDiet on CVDcan be explained by the beneficial effect of this diet on classical risk cardiovascular factors and non-classical ones. Omics integration, both at a single-omic level or better still at a multi-omics level, will be crucial to better understand the mechanisms behind the protective effects of the MedDiet as well as the inter-individual differences in CVD risk and dietary response for further applications in nutritional epidemiology and in personalized nutrition or precision nutrition.


This study has been supported by The Spanish Ministry of Economy and Innovation and the Fondo Europeo de Desarrollo Regional (projects CIBER 06/03, CNIC-06, PI11/02505, AGL2010-22319-C03-03, AGL2009-13517-C03-01, and PI13/00728), by the Real Colegio Complutense at Harvard (Harvard University, MA, USA), by Miguel Servet’s contract (CP06/00100), the AGAUR (2014-SGR-240), and the European Commission (QLRT-2001-00287). CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN) is an initiative of the Instituto de Salud Carlos III, Madrid, Spain and by Fundación MAPFRE-Larramendy.

Author Contributions

We thank Stephanie Londsdale and George Mattingley for English language revision and Oscar Coltell (Universitat Jaume I, Castellon, Spain) for revising, editing, and formatting the manuscript; organizing and formatting the references, and co-designing and making the graphical abstract. The authors’ contributions to manuscript are as follows. Montserrat Fito and Dolores Corella wrote paper and had primary responsibility for final content. Dolores Corella designed the graphical abstract. Montserrat Fitó, Dolores Corella, José Alfredo Martínez, Estefanía Toledo, Christian Carpéné, and Olle Melander provided essential materials and comments. All authors have read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Estruch, R.; Ros, E.; Salas-Salvadó, J.; Covas, M.I.; Corella, D.; Arós, F.; Gómez-Gracia, E.; Ruiz-Gutiérrez, V.; Fiol, M.; Lapetra, J.; et al. Primary prevention of cardiovascular disease with a Mediterranean diet. N. Engl. J. Med. 2013, 368, 1279–1290. [Google Scholar] [CrossRef] [PubMed]
  2. Guasch-Ferré, M.; Hu, F.B.; Martínez-González, M.A.; Fitó, M.; Bulló, M.; Estruch, R.; Ros, E.; Corella, D.; Recondo, J.; Gómez-Gracia, E.; et al. Olive oil intake and risk of cardiovascular disease and mortality in the PREDIMED Study. BMC Med. 2014, 12, 78. [Google Scholar] [CrossRef] [PubMed][Green Version]
  3. Martínez-González, M.Á.; Toledo, E.; Arós, F.; Fiol, M.; Corella, D.; Salas-Salvadó, J.; Ros, E.; Covas, M.I.; Fernández-Crehuet, J.; Lapetra, J.; et al. Extravirgin olive oil consumption reduces risk of atrial fibrillation: The PREDIMED (Prevención con Dieta Mediterránea) trial. Circulation 2014, 130, 18–26. [Google Scholar] [CrossRef] [PubMed]
  4. Salas-Salvadó, J.; Bulló, M.; Babio, N.; Martínez-González, M.Á.; Ibarrola-Jurado, N.; Basora, J.; Estruch, R.; Covas, M.I.; Corella, D.; Arós, F.; et al. Reduction in the incidence of type 2 diabetes with the Mediterranean diet: Results of the PREDIMED-Reus nutrition intervention randomized trial. Diabetes Care 2011, 34, 14–19. [Google Scholar] [CrossRef] [PubMed]
  5. Ruiz-Canela, M.; Estruch, R.; Corella, D.; Salas-Salvadó, J.; Martínez-González, M.A. Association of Mediterranean diet with peripheral artery disease: The PREDIMED randomized trial. JAMA 2014, 311, 415–417. [Google Scholar] [CrossRef] [PubMed]
  6. Coughlin, S.S. Toward a road map for global-omics: A primer on -omic technologies. Am. J. Epidemiol. 2014, 180, 1188–1195. [Google Scholar] [CrossRef] [PubMed]
  7. Corella, D.; Ordovás, J.M. Biomarkers: Background, classification and guidelines for applications in nutritional epidemiology. Nutr. Hosp. 2015, 31, 177–188. [Google Scholar] [PubMed]
  8. Estruch, R.; Martínez-González, M.A.; Corella, D.; Salas-Salvadó, J.; Ruiz-Gutiérrez, V.; Covas, M.I.; Fiol, M.; Gómez-Gracia, E.; López-Sabater, M.C.; Vinyoles, E.; et al. Effects of a Mediterranean-style diet on cardiovascular risk factors. Ann. Intern. Med. 2006, 145, 1–11. [Google Scholar] [CrossRef] [PubMed]
  9. Estruch, R.; Martínez-González, M.A.; Corella, D.; Salas-Salvadó, J.; Fitó, M.; Chiva-Blanch, G.; Fiol, M.; Gómez-Gracia, E.; Arós, F.; Lapetra, J.; et al. Lamuela-Raven effect of a high-fat Mediterranean diet on bodyweight and waist circumference: A prespecified secondary outcomes analysis of the PREDIMED randomised controlled trial. Lancet Diabetes Endocrinol. 2016, 4, 666–676. [Google Scholar] [CrossRef]
  10. Solá, R.; Fitó, M.; Estruch, R.; Salas-Salvadó, J.; Corella, D.; de La Torre, R.; Muñoz, M.A.; López-Sabater, M.C.; Martínez-González, M.A.; Arós, F.; et al. Effect of a traditional Mediterranean diet on apolipoproteins B, A-I, and their ratio: A randomized, controlled trial. Atherosclerosis 2011, 218, 174–180. [Google Scholar] [CrossRef] [PubMed]
  11. Damasceno, N.R.; Sala-Vila, A.; Cofán, M.; Pérez-Heras, A.M.; Fitó, M.; Ruiz-Gutiérrez, V.; Martínez-González, M.A.; Corella, D.; Arós, F.; Estruch, R.; et al. Mediterranean diet supplemented with nuts reduces waist circumference and shifts lipoprotein subfractions to a less atherogenic pattern in subjects at high cardiovascular risk. Atherosclerosis 2013, 230, 347–353. [Google Scholar] [CrossRef] [PubMed]
  12. Mekki, K.; Bouzidi-bekada, N.; Kaddous, A.; Bouchenak, M. Mediterranean diet improves dyslipidemia and biomarkers in chronic renal failure patients. Food Funct. 2010, 1, 110–115. [Google Scholar] [CrossRef] [PubMed]
  13. Athyros, V.G.; Kakafika, A.I.; Papageorgiou, A.A.; Tziomalos, K.; Peletidou, A.; Vosikis, C.; Karagiannis, A.; Mikhailidis, D.P. Effect of a plant stanol ester-containing spread, placebo spread, or Mediterranean diet on estimated cardiovascular risk and lipid, inflammatory and haemostatic factors. Nutr. Metab. Cardiovasc. Dis. 2011, 21, 213–221. [Google Scholar] [CrossRef] [PubMed]
  14. Kastorini, C.M.; Haralampos, J.M.; Esposito, K.; Giugliano, D.; Goudevenos, J.A.; Panagiotakos, D.B. The effect of Mediterranean diet on metabolic syndrome and its components a meta-analysis of 50 studies and 534,906 individuals. J. Am. Coll. Cardiol. 2011, 57, 1299–1213. [Google Scholar] [CrossRef] [PubMed]
  15. Gomez-Huelgas, R.; Jansen-Chaparro, S.; Baca-Osorio, A.J.; Mancera-Romero, J.; Tinahones, F.J.; Bernal-López, M.R. Effects of a long-term lifestyle intervention program with Mediterranean diet and exercise for the management of patients with metabolic syndrome in a primary care setting. Eur. J. Intern. Med. 2015, 26, 317–323. [Google Scholar] [CrossRef] [PubMed]
  16. Garcia, M.; Bihuniak, J.D.; Shook, J.; Kenny, A.; Kerstetter, J.; Huedo-Medina, T.B. The effect of the traditional Mediterranean-style diet on metabolic risk factors: A meta-analysis. Nutrients 2016, 8, 168. [Google Scholar] [CrossRef] [PubMed]
  17. Delgado-Lista, J.; Perez-Martinez, P.; Garcia-Rios, A.; Alcala-Diaz, J.F.; Perez-Caballero, A.I.; Gomez-Delgado, F.; Fuentes, F.; Quintana-Navarro, G.; Lopez-Segura, F.; Ortiz-Morales, A.M.; et al. CORonary Diet Intervention with Olive oil and cardiovascular PREVention study (the CORDIOPREV study): Rationale, methods, and baseline characteristics: A clinical trial comparing the efficacy of a Mediterranean diet rich in olive oil versus a low-fat diet. Am. Heart J. 2016, 177, 42–50. [Google Scholar] [CrossRef] [PubMed]
  18. Moller, C.S.; Zethelius, B.; Sundstrom, J.; Lind, L. Impact of follow-up time and re-measurement of the electrocardiogram and conventional cardiovascular risk factors on their predictive value for myocardial infarction. J. Intern. Med. 2006, 260, 22–30. [Google Scholar] [CrossRef] [PubMed]
  19. Gómez, M.; Valle, V.; Arós, F.; Sanz, G.; Sala, J.; Fiol, M.; Bruguera, J.; Elosua, R.; Molina, L.; Martí, H.; et al. Oxidized LDL, lipoprotein (a) and other emergent risk factors in acute myocardial infarction (FORTIAM study). Rev. Esp. Cardiol. 2009, 62, 373–382. [Google Scholar] [CrossRef]
  20. Khot, U.N.; Khot, M.B.; Bajzer, C.T.; Sapp, S.K.; Ohman, E.M.; Brener, S.J.; Ellis, S.G.; Lincoff, A.M.; Topol, E.J. Prevalence of conventional risk factors in patients with coronary heart disease. JAMA 2003, 290, 898–904. [Google Scholar] [CrossRef] [PubMed]
  21. Fiol, M.; Cabadés, A.; Sala, J.; Marrugat, J.; Elosua, R.; Vega, G.; José Tormo Díaz, M.; Segura, A.; Aldasoro, E.; Moreno-Iribas, C.; et al. Variability in the in-hospital management of acute myocardial infarction in Spain: IBERICA Study. Rev. Esp. Cardiol. 2001, 54, 443–452. [Google Scholar] [CrossRef]
  22. Björnson, E.; Borén, J.; Mardinoglu, A. Personalized cardiovascular disease prediction and treatment—A review of existing strategies and novel systems medicine tools. Front. Physiol. 2016, 7, 2. [Google Scholar] [CrossRef] [PubMed]
  23. Libby, P. Mechanisms of acute coronary syndromes and their implications for therapy. N. Engl. J. Med. 2013, 368, 2004–2013. [Google Scholar] [CrossRef]
  24. Witzum, J. The oxidation hypothesis of atherosclerosis. Lancet 1994, 349, 793–795. [Google Scholar] [CrossRef]
  25. Ehara, S.; Ueda, M.; Naruko, T.; Haze, K.; Itoh, A.; Otsuka, M.; Komatsu, R.; Matsuo, T.; Itabe, H.; Takano, T.; et al. Elevated levels of oxidized low density lipoprotein show a positive relationship with the severity of acute coronary syndromes. Circulation 2001, 103, 1955–1960. [Google Scholar] [CrossRef] [PubMed]
  26. Meisinger, C.; Baumert, J.; Khuseyinova, N.; Loewel, H.; Koenig, W. Plasma oxidized low-density lipoprotein, a strong predictor for acute coronary heart disease events in apparently healthy, middle-aged men from the general population. Circulation 2005, 112, 651–657. [Google Scholar] [CrossRef] [PubMed]
  27. Gómez, M.; Vila, J.; Elosua, R.; Molina, L.; Bruguera, J.; Sala, J.; Masià, R.; Covas, M.I.; Marrugat, J.; Fitó, M. Relationship of lipid oxidation with subclinical atherosclerosis and 10-year coronary events in general population. Atherosclerosis 2014, 232, 134–140. [Google Scholar]
  28. Fitó, M.; Guxens, M.; Corella, D.; Sáez, G.; Estruch, R.; de la Torre, R.; Francés, F.; Cabezas, C.; López-Sabater Mdel, C.; Marrugat, J.; et al. Effect of a traditional Mediterranean diet on lipoprotein oxidation: A randomized controlled trial. Arch. Intern. Med. 2007, 167, 1195–1203. [Google Scholar] [CrossRef] [PubMed]
  29. Fitó, M.; Estruch, R.; Salas-Salvadó, J.; Martínez-Gonzalez, M.A.; Arós, F.; Vila, J.; Corella, D.; Díaz, O.; Sáez, G.; de la Torre, R.; et al. Effect of the Mediterranean diet on heart failure biomarkers: A randomized sample from the PREDIMED trial. Eur. J. Heart Fail. 2014, 16, 543–550. [Google Scholar] [CrossRef] [PubMed]
  30. Mitjavila, M.T.; Fandos, M.; Salas-Salvadó, J.; Covas, M.I.; Borrego, S.; Estruch, R.; Lamuela-Raventós, R.; Corella, D.; Martínez-Gonzalez, M.Á.; Sánchez, J.M.; et al. The Mediterranean diet improves the systemic lipid and DNA oxidative damage in metabolic syndrome individuals: A randomized, controlled, trial. Clin. Nutr. 2013, 32, 172–178. [Google Scholar] [CrossRef] [PubMed]
  31. Zamora-Ros, R.; Serafini, M.; Estruch, R.; Lamuela-Raventós, R.M.; Martínez-González, M.A.; Salas-Salvadó, J.; Fiol, M.; Lapetra, J.; Arós, F.; Covas, M.I.; et al. Mediterranean diet and non enzymatic antioxidant capacity in the PREDIMED study: Evidence for a mechanism of antioxidant tuning. Nutr. Metab. Cardiovasc. Dis. 2013, 23, 1167–1174. [Google Scholar] [CrossRef] [PubMed]
  32. Razquin, C.; Martinez, J.A.; Martinez-Gonzalez, M.A.; Mitjavila, M.T.; Estruch, R.; Marti, A. A 3 years follow-up of a Mediterranean diet rich in virgin olive oil is associated with high plasma antioxidant capacity and reduced body weight gain. Eur. J. Clin. Nutr. 2009, 63, 1387–1393. [Google Scholar] [CrossRef] [PubMed]
  33. Serrano-Martinez, M.; Palacios, M.; Martinez-Losa, E.; Lezaun, R.; Maravi, C.; Prado, M.; Martínez, J.A.; Martinez-Gonzalez, M.A. A Mediterranean dietary style influences TNF-α and VCAM-1 coronary blood levels in unstable angina patients. Eur. J. Nutr. 2005, 44, 348–354. [Google Scholar] [CrossRef] [PubMed]
  34. Hermsdorff, H.H.; Zulet, M.Á.; Abete, I.; Martínez, J.A. Discriminated benefits of a Mediterranean dietary pattern within a hypocaloric diet program on plasma RBP4 concentrations and other inflammatory markers in obese subjects. Endocrine 2009, 36, 445–451. [Google Scholar] [CrossRef] [PubMed]
  35. Casas, R.; Sacanella, E.; Urpí-Sardà, M.; Corella, D.; Castañer, O.; Lamuela-Raventos, R.M.; Salas-Salvadó, J.; Martínez-González, M.A.; Ros, E.; Estruch, R. Long-term immunomodulatory effects of a mediterranean diet in adults at high risk of cardiovascular disease in the PREvención con Dieta MEDiterránea randomized controlled trial. J. Nutr. 2016, 146, 1684–1693. [Google Scholar] [CrossRef] [PubMed]
  36. Capurso, C.; Massaro, M.; Scoditti, E.; Vendemiale, G.; Capurso, A. Vascular effects of the Mediterranean diet part I: Anti-hypertensive and anti-thrombotic effects. Vascul. Pharmacol. 2014, 63, 118–126. [Google Scholar] [CrossRef] [PubMed]
  37. Delgado-Lista, J.; Garcia-Rios, A.; Perez-Martinez, P.; Lopez-Miranda, J.; Perez-Jimenez, F. Olive oil and haemostasis: Platelet function, thrombogenesis and fibrinolysis. Curr. Pharm. Des. 2011, 17, 778–785. [Google Scholar] [CrossRef] [PubMed]
  38. Fernández de la Puebla, R.A.; Pérez-Martínez, P.; Carmona, J.; López-Miranda Carmen Marín, J.; Paniagua, J.A.; Fuentes, F.; Pérez-Jiménez, F. Factor VII polymorphisms influence the plasma response to diets with different fat content, in a healthy caucasian population. Mol. Nutr. Food Res. 2007, 51, 618–624. [Google Scholar] [CrossRef] [PubMed]
  39. Bloomfield, H.E.; Koeller, E.; Greer, N.; MacDonald, R.; Kane, R.; Wilt, T.J. Effects on health outcomes of a Mediterranean diet with no restriction on fat intake: A systematic review and meta-analysis. Ann. Intern. Med. 2016. [Google Scholar] [CrossRef] [PubMed]
  40. Kathiresan, S.; Srivastava, D. Genetics of human cardiovascular disease. Cell 2012, 148, 1242–1257. [Google Scholar] [CrossRef] [PubMed]
  41. Roberts, R. Genetics of coronary artery disease. Circ. Res. 2014, 114, 1890–1903. [Google Scholar] [CrossRef] [PubMed]
  42. Scoditti, E.; Capurso, C.; Capurso, A.; Massaro, M. Vascular effects of the Mediterranean diet-part II: Role of ω-3 fatty acids and olive oil polyphenols. Vascul. Pharmacol. 2014, 63, 127–134. [Google Scholar] [CrossRef] [PubMed]
  43. Jaureguiberry, M.S.; Tricerri, M.A.; Sanchez, S.A.; Finarelli, G.S.; Montanaro, M.A.; Prieto, E.D.; Rimoldi, O.J. Role of plasma membrane lipid composition on cellular homeostasis: Learning from cell line models expressing fatty acid desaturases. Acta Biochim. Biophys. Sin. 2014, 46, 273–282. [Google Scholar] [CrossRef] [PubMed][Green Version]
  44. Freeman, B.A.; Baker, P.R.; Schopfer, F.J.; Woodcock, S.R.; Napolitano, A.; d'Ischia, M. Nitro-fatty acid formation and signaling. J. Biol. Chem. 2008, 283, 15515–15519. [Google Scholar] [CrossRef] [PubMed]
  45. Tuohy, K.M.; Fava, F.; Viola, R. The way to a man’s heart is through his gut microbiota’—Dietary pro- and prebiotics for the management of cardiovascular risk. Proc. Nutr. Soc. 2014, 73, 172–185. [Google Scholar] [CrossRef] [PubMed]
  46. Lopez-Legarrea, P.; Fuller, N.R.; Zulet, M.A.; Martinez, J.A.; Caterson, I.D. The influence of Mediterranean, carbohydrate and high protein diets on gut microbiota composition in the treatment of obesity and associated inflammatory state. Asia Pac. J. Clin. Nutr. 2014, 23, 360–368. [Google Scholar] [PubMed]
  47. Zulet, M.A.; Bondia-Pons, I.; Abete, I.; de la Iglesia, R.; López-Legarrea, P.; Forga, L.; Navas-Carretero, S.; Martínez, J.A. The reduction of the metabolyc syndrome in Navarra-Spain (RESMENA-S) study: A multidisciplinary strategy based on chrononutrition and nutritional education, together with dietetic and psychological control. Nutr. Hosp. 2011, 26, 16–26. [Google Scholar] [PubMed]
  48. Müller, M.; Kersten, S. Nutrigenomics: Goals and strategies. Nat. Rev. Genet. 2003, 4, 315–322. [Google Scholar] [CrossRef] [PubMed]
  49. Corella, D.; Ordovás, J.M. How does the Mediterranean diet promote cardiovascular health? Current progress toward molecular mechanisms: Gene-diet interactions at the genomic, transcriptomic, and epigenomic levels provide novel insights into new mechanisms. Bioessays 2014, 36, 526–537. [Google Scholar] [CrossRef] [PubMed]
  50. Van der Sijde, M.R.; Ng, A.; Fu, J. Systems genetics: From GWAS to disease pathways. Biochim. Biophys. Acta 2014, 1842, 1903–1909. [Google Scholar] [CrossRef] [PubMed]
  51. Abraham, G.; Bhalala, O.G.; de Bakker, P.I.; Ripatti, S.; Inouye, M. Towards a molecular systems model of coronary artery disease. Curr. Cardiol. Rep. 2014, 16, 488. [Google Scholar] [CrossRef] [PubMed]
  52. Corella, D.; Arregui, M.; Coltell, O.; Portolés, O.; Guillem-Sáiz, P.; Carrasco, P.; Sorlí, J.V.; Ortega-Azorín, C.; González, J.I.; Ordovás, J.M. Association of the LCT-13910 C>T polymorphism with obesity and its modulation by dairy products in a Mediterranean population. Obesity 2011, 19, 1707–1714. [Google Scholar] [CrossRef] [PubMed]
  53. Park, L.K.; Friso, S.; Choi, S.W. Nutritional influences on epigenetics and age-related disease. Proc. Nutr. Soc. 2012, 71, 75–83. [Google Scholar] [CrossRef] [PubMed]
  54. Aslibekyan, S.; Claas, S.A.; Arnett, D.K. Clinical applications of epigenetics in cardiovascular disease: The long road ahead. Transl. Res. 2015, 165, 143–153. [Google Scholar] [CrossRef] [PubMed]
  55. Siemelink, M.A.; Zeller, T. Biomarkers of coronary artery disease: The promise of the transcriptome. Curr. Cardiol. Rep. 2014, 16, 513. [Google Scholar] [CrossRef] [PubMed]
  56. Castañer, O.; Corella, D.; Covas, M.I.; Sorlí, J.V.; Subirana, I.; Flores-Mateo, G.; Nonell, L.; Bulló, M.; de la Torre, R.; Portolés, O.; et al. In vivo transcriptomic profile after a Mediterranean diet in high-cardiovascular risk patients: A randomized controlled trial. Am. J. Clin. Nutr. 2013, 98, 845–853. [Google Scholar] [CrossRef] [PubMed]
  57. Arab, S.; Gramolini, A.O.; Ping, P.; Kislinger, T.; Stanley, B.; van Eyk, J.; Ouzounian, M.; MacLennan, D.H.; Emili, A.; Liu, P.P. Cardiovascular proteomics: Tools to develop novel biomarkers and potential applications. J. Am. Coll. Cardiol. 2006, 48, 1733–1741. [Google Scholar] [CrossRef] [PubMed]
  58. Hinterwirth, H.; Stegemann, C.; Mayr, M. Lipidomics: Quest for molecular lipid biomarkers in cardiovascular disease. Circ. Cardiovasc. Genet. 2014, 7, 941–954. [Google Scholar] [CrossRef] [PubMed]
  59. Ganna, A.; Salihovic, S.; Sundström, J.; Broeckling, C.D.; Hedman, A.K.; Magnusson, P.K.; Pedersen, N.L.; Larsson, A.; Siegbahn, A.; Zilmer, M.; et al. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease. PLoS Genet. 2014, 10, e1004801. [Google Scholar] [CrossRef] [PubMed]
  60. Vázquez-Fresno, R.; Llorach, R.; Urpi-Sarda, M.; Lupianez-Barbero, A.; Estruch, R.; Corella, D.; Fitó, M.; Arós, F.; Ruiz-Canela, M.; Salas-Salvadó, J.; et al. Metabolomic pattern analysis after Mediterranean diet intervention in a nondiabetic population: A 1- and 3-year follow-up in the PREDIMED study. J. Proteome Res. 2015, 14, 531–540. [Google Scholar]
  61. Corella, D.; Carrasco, P.; Fitó, M.; Martínez-González, M.A.; Salas-Salvadó, J.; Arós, F.; Lapetra, J.; Guillén, M.; Ortega-Azorín, C.; Warnberg, J.; et al. Gene-environment interactions of CETP gene variation in a high cardiovascular risk Mediterranean population. J. Lipid Res. 2010, 51, 2798–2807. [Google Scholar] [CrossRef] [PubMed]
  62. Sotos-Prieto, M.; Guillén, M.; Vicente Sorli, J.; Portolés, O.; Guillem-Saiz, P.; Ignacio Gonzalez, J.; Qi, L.; Corella, D. Relevant associations of the glucokinase regulatory protein/glucokinase gene variation with TAG concentrations in a high-cardiovascular risk population: Modulation by the Mediterranean diet. Br. J. Nutr. 2013, 109, 193–201. [Google Scholar] [CrossRef] [PubMed]
  63. Corella, D.; Carrasco, P.; Sorlí, J.V.; Estruch, R.; Rico-Sanz, J.; Martínez-González, M.Á.; Salas-Salvadó, J.; Covas, M.I.; Coltell, O.; Arós, F.; et al. Mediterranean diet reduces the adverse effect of the TCF7L2-rs7903146 polymorphism on cardiovascular risk factors and stroke incidence: A randomized controlled trial in a high-cardiovascular-risk population. Diabetes Care 2013, 36, 3803–3811. [Google Scholar] [CrossRef] [PubMed]
  64. Ortega-Azorín, C.; Sorlí, J.V.; Estruch, R.; Asensio, E.M.; Coltell, O.; González, J.I.; Martínez-González, M.Á.; Ros, E.; Salas-Salvadó, J.; Fitó, M.; et al. Amino acid change in the carbohydrate response element binding protein is associated with lower triglycerides and myocardial infarction incidence depending on level of adherence to the Mediterranean diet in the PREDIMED trial. Circ. Cardiovasc. Genet. 2014, 7, 49–58. [Google Scholar] [CrossRef] [PubMed]
  65. Ortega-Azorín, C.; Sorlí, J.V.; Asensio, E.M.; Coltell, O.; Martínez-González, M.Á.; Salas-Salvadó, J.; Covas, M.I.; Arós, F.; Lapetra, J.; Serra-Majem, L.; et al. Associations of the FTO rs9939609 and the MC4R rs17782313 polymorphisms with type 2 diabetes are modulated by diet, being higher when adherence to the Mediterranean diet pattern is low. Cardiovasc. Diabetol. 2012, 11. [Google Scholar] [CrossRef] [PubMed]
  66. Corella, D.; González, J.I.; Bulló, M.; Carrasco, P.; Portolés, O.; Díez-Espino, J.; Covas, M.I.; Ruíz-Gutierrez, V.; Gómez-Gracia, E.; Arós, F.; et al. Polymorphisms cyclooxygenase-2-765G>C and interleukin-6-174G>C are associated with serum inflammation markers in a high cardiovascular risk population and do not modify the response to a Mediterranean diet supplemented with virgin olive oil or nuts. J. Nutr. 2009, 139, 128–134. [Google Scholar] [CrossRef] [PubMed]
  67. Sotos-Prieto, M.; Guillén, M.; Portolés, O.; Sorlí, J.V.; González, J.I.; Asensio, E.M.; Corella, D. Association between the rs6950982 polymorphism near the SERPINE1 gene and blood pressure and lipid parameters in a high-cardiovascular-risk population: Interaction with Mediterranean diet. Genes Nutr. 2013, 8, 401–409. [Google Scholar] [CrossRef] [PubMed]
  68. Corella, D.; Ortega-Azorín, C.; Sorlí, J.V.; Covas, M.I.; Carrasco, P.; Salas-Salvadó, J.; Martínez-González, M.Á.; Arós, F.; Lapetra, J.; Serra-Majem, L.; et al. Statistical and biological gene-lifestyle interactions of MC4R and FTO with diet and physical activity on obesity: New effects on alcohol consumption. PLoS ONE 2012, 7, e52344. [Google Scholar] [CrossRef] [PubMed]
  69. Trichopoulou, A.; Costacou, T.; Bamia, C.; Trichopoulos, D. Adherence to a Mediterranean diet and survival in a Greek population. N. Engl. J. Med. 2003, 348, 2599–2608. [Google Scholar] [CrossRef] [PubMed]
  70. Panagiotakos, D.B.; Pitsavos, C.; Arvaniti, F.; Stefanadis, C. Adherence to the Mediterranean food pattern predicts the prevalence of hypertension, hypercholesterolemia, diabetes and obesity, among healthy adults; the accuracy of the MedDietScore. Prev. Med. 2007, 44, 335–340. [Google Scholar] [CrossRef] [PubMed]
  71. Schröder, H.; Fitó, M.; Estruch, R.; Martínez-González, M.A.; Corella, D.; Salas-Salvadó, J.; Lamuela-Raventós, R.; Ros, E.; Salaverría, I.; Fiol, M.; et al. A short screener is valid for assessing Mediterranean diet adherence among older Spanish men and women. J. Nutr. 2011, 141, 1140–1145. [Google Scholar] [CrossRef] [PubMed]
  72. Barchitta, M.; Quattrocchi, A.; Adornetto, V.; Marchese, A.E.; Agodi, A. Tumor necrosis factor-α-308 G>A polymorphism, adherence to Mediterranean diet, and risk of overweight/obesity in young women. Biomed. Res. Int. 2014, 2014, 742620. [Google Scholar] [CrossRef] [PubMed]
  73. Roswall, N.; Ängquist, L.; Ahluwalia, T.S.; Romaguera, D.; Larsen, S.C.; Østergaard, J.N.; Halkjaer, J.; Vimaleswaran, K.S.; Wareham, N.J.; Bendinelli, B.; et al. Association between Mediterranean and Nordic diet scores and changes in weight and waist circumference: Influence of FTO and TCF7L2 loci. Am. J. Clin. Nutr. 2014, 100, 1188–1197. [Google Scholar] [CrossRef] [PubMed]
  74. Pitsavos, C.; Panagiotakos, D.; Trichopoulou, A.; Chrysohoou, C.; Dedoussis, G.; Chloptsios, Y.; Choumerianou, D.; Stefanadis, C. Interaction between Mediterranean diet and methylenetetrahydrofolate reductase C677T mutation on oxidized low density lipoprotein concentrations: The ATTICA study. Nutr. Metab. Cardiovasc. Dis. 2006, 16, 91–99. [Google Scholar] [CrossRef] [PubMed]
  75. Gomez-Delgado, F.; Alcala-Diaz, J.F.; Garcia-Rios, A.; Delgado-Lista, J.; Ortiz-Morales, A.; Rangel-Zuñiga, O.; Tinahones, F.J.; Gonzalez-Guardia, L.; Malagon, M.M.; Bellido-Muñoz, E.; et al. Polymorphism at the TNF-α gene interacts with Mediterranean diet to influence triglyceride metabolism and inflammation status in metabolic syndrome patients: From the CORDIOPREV clinical trial. Mol. Nutr. Food Res. 2014, 58, 1519–1527. [Google Scholar] [CrossRef] [PubMed]
  76. Di Daniele, N.; di Renzo, L.; Noce, A.; Iacopino, L.; Ferraro, P.M.; Rizzo, M.; Sarlo, F.; Domino, E.; de Lorenzo, A. Effects of Italian Mediterranean organic diet vs. low-protein diet in nephropathic patients according to MTHFR genotypes. J. Nephrol. 2014, 27, 529–536. [Google Scholar] [CrossRef] [PubMed]
  77. Corella, D.; Sorlí, J.V.; Estruch, R.; Coltell, O.; Ortega-Azorín, C.; Portolés, O.; Martínez-González, M.A.; Bulló, M.; Fitó, M.; Arós, F.; et al. MicroRNA-410 regulated lipoprotein lipase variant rs13702 is associated with stroke incidence and modulated by diet in the randomized controlled PREDIMED trial. Am. J. Clin. Nutr. 2014, 100, 719–731. [Google Scholar] [CrossRef] [PubMed]
  78. Corella, D.; Asensio, E.M.; Coltell, O.; Sorlí, J.V.; Estruch, R.; Martínez-González, M.Á.; Salas-Salvadó, J.; Castañer, O.; Arós, F.; Lapetra, J.; et al. CLOCK gene variation is associated with incidence of type-2 diabetes and cardiovascular diseases in type-2 diabetic subjects: Dietary modulation in the PREDIMED randomized trial. Cardiovasc. Diabetol. 2016, 15. [Google Scholar] [CrossRef] [PubMed]
  79. Sousa, A.G.; Marquezine, G.F.; Lemos, P.A.; Martinez, E.; Lopes, N.; Hueb, W.A.; Krieger, J.E.; Pereira, A.C. TCF7L2 polymorphism rs7903146 is associated with coronary artery disease severity and mortality. PLoS ONE 2009, 4, e7697. [Google Scholar] [CrossRef] [PubMed]
  80. Choi, H.J.; Lee, D.H.; Jeon, H.J.; Kim, D.S.; Lee, Y.H.; Oh, T. Transcription factor 7-like 2 (TCF7L2) gene polymorphism rs7903146 is associated with stroke in type 2 diabetes patients with long disease duration. Diabetes Res. Clin. Pract. 2014, 103, e3–e6. [Google Scholar] [CrossRef] [PubMed]
  81. Kathiresan, S.; Melander, O.; Guiducci, C.; Surti, A.; Burtt, N.P.; Rieder, M.J.; Cooper, G.M.; Roos, C.; Voight, B.F.; Havulinna, A.S.; et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat. Genet. 2008, 40, 189–197. [Google Scholar] [CrossRef] [PubMed]
  82. Kooner, J.S.; Chambers, J.C.; Aguilar-Salinas, C.A.; Hinds, D.A.; Hyde, C.L.; Warnes, G.R.; Gómez Pérez, F.J.; Frazer, K.A.; Elliott, P.; Scott, J.; et al. Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides. Nat. Genet. 2008, 40, 149–151. [Google Scholar] [CrossRef] [PubMed]
  83. Barrès, R.; Zierath, J.R. The role of diet and exercise in the transgenerational epigenetic landscape of T2DM. Nat. Rev. Endocrinol. 2016, 12, 441–451. [Google Scholar] [CrossRef] [PubMed]
  84. Rönn, T.; Ling, C. DNA methylation as a diagnostic and therapeutic target in the battle against Type 2 diabetes. Epigenomics 2015, 7, 451–460. [Google Scholar] [CrossRef] [PubMed]
  85. Abete, I.; Gomez-Uriz, A.M.; Mansego, M.L.; de Arce, A.; Goyenechea, E.; Blazquez, V.; Martinez-Zabaleta, M.T.; Gonzalez-Muniesa, P.; Lopez de Munain, A.; Martinez, J.A.; et al. Epigenetic changes in the methylation patterns of KCNQ1 and WT1 after a weight loss intervention program in obese stroke patients. Curr. Neurovasc. Res. 2015, 12, 321–333. [Google Scholar] [CrossRef] [PubMed]
  86. Agodi, A.; Barchitta, M.; Quattrocchi, A.; Maugeri, A.; Canto, C.; Marchese, A.E.; Vinciguerra, M. Low fruit consumption and folate deficiency are associated with LINE-1 hypomethylation in women of a cancer-free population. Genes Nutr. 2015, 10, 480. [Google Scholar] [CrossRef] [PubMed]
  87. Martín-Núñez, G.M.; Cabrera-Mulero, R.; Rubio-Martín, E.; Rojo-Martínez, G.; Olveira, G.; Valdés, S.; Soriguer, F.; Castaño, L.; Morcillo, S. Methylation levels of the SCD1 gene promoter and LINE-1 repeat region are associated with weight change: An intervention study. Mol. Nutr. Food Res. 2014, 58, 1528–1536. [Google Scholar] [CrossRef] [PubMed]
  88. De la Rocha, C.; Pérez-Mojica, J.E.; León, S.Z.; Cervantes-Paz, B.; Tristán-Flores, F.E.; Rodríguez-Ríos, D.; Molina-Torres, J.; Ramírez-Chávez, E.; Alvarado-Caudillo, Y.; Carmona, F.J.; et al. Associations between whole peripheral blood fatty acids and DNA methylation in humans. Sci. Rep. 2016, 6, 25867. [Google Scholar] [CrossRef] [PubMed]
  89. Nicoletti, C.F.; Nonino, C.B.; de Oliveira, B.A.; Pinhel, M.A.; Mansego, M.L.; Milagro, F.I.; Zulet, M.A.; Martinez, J.A. DNA methylation and hydroxymethylation levels in relation to two weight loss strategies: Energy-restricted diet or bariatric surgery. Obes. Surg. 2016, 26, 603–611. [Google Scholar] [CrossRef] [PubMed]
  90. Leung, A.; Trac, C.; Du, J.; Natarajan, R.; Schones, D.E. Persistent chromatin modifications induced by high fat diet. J. Biol. Chem. 2016, 291, 10446–10455. [Google Scholar] [CrossRef] [PubMed]
  91. Fritz, J.V.; Heintz-Buschart, A.; Ghosal, A.; Wampach, L.; Etheridge, A.; Galas, D.; Wilmes, P. Sources and functions of extracellular small RNAs in human circulation. Annu. Rev. Nutr. 2016, in press. [Google Scholar] [CrossRef] [PubMed]
  92. Schober, A.; Nazari-Jahantigh, M.; Weber, C. MicroRNA-mediated mechanisms of the cellular stress response in atherosclerosis. Nat. Rev. Cardiol. 2015, 12, 361–374. [Google Scholar] [CrossRef] [PubMed]
  93. Samanta, S.; Balasubramanian, S.; Rajasingh, S.; Patel, U.; Dhanasekaran, A.; Dawn, B.; Rajasingh, J. MicroRNA: A new therapeutic strategy for cardiovascular diseases. Trends Cardiovasc. Med. 2016, 26, 407–419. [Google Scholar] [CrossRef] [PubMed]
  94. Creemers, E.E.; Tijsen, A.J.; Pinto, Y.M. Circulating microRNAs: Novel biomarkers and extracellular communicators in cardiovascular disease? Circ. Res. 2012, 110, 483–495. [Google Scholar] [CrossRef] [PubMed]
  95. Xin, Y.; Yang, C.; Han, Z. Circulating miR-499 as a potential biomarker for acute myocardial infarction. Ann. Transl. Med. 2016, 4, 135. [Google Scholar] [CrossRef] [PubMed]
  96. Párrizas, M.; Brugnara, L.; Esteban, Y.; González-Franquesa, A.; Canivell, S.; Murillo, S.; Gordillo-Bastidas, E.; Cussó, R.; Cadefau, J.A.; García-Roves, P.M.; et al. Circulating miR-192 and miR-193b are markers of prediabetes and are modulated by an exercise intervention. J. Clin. Endocrinol. Metab. 2015, 100, E407–E415. [Google Scholar] [CrossRef] [PubMed]
  97. Hernández-Alonso, P.; Giardina, S.; Salas-Salvadó, J.; Arcelin, P.; Bulló, M. Chronic pistachio intake modulates circulating microRNAs related to glucose metabolism and insulin resistance in prediabetic subjects. Eur. J. Nutr. 2016. [Google Scholar] [CrossRef] [PubMed]
  98. Tabet, F.; Cuesta Torres, L.F.; Ong, K.L.; Shrestha, S.; Choteau, S.A.; Barter, P.J.; Clifton, P.; Rye, K.A. High-density lipoprotein-associated miR-223 is altered after diet-induced weight loss in overweight and obese males. PLoS ONE 2016, 11, e0151061. [Google Scholar] [CrossRef] [PubMed]
  99. Ortega, F.J.; Cardona-Alvarado, M.I.; Mercader, J.M.; Moreno-Navarrete, J.M.; Moreno, M.; Sabater, M.; Fuentes-Batllevell, N.; Ramírez-Chávez, E.; Ricart, W.; Molina-Torres, J.; et al. Circulating profiling reveals the effect of a polyunsaturated fatty acid-enriched diet on common microRNAs. J. Nutr. Biochem. 2015, 26, 1095–1101. [Google Scholar] [CrossRef] [PubMed]
  100. Marques-Rocha, J.L.; Milagro, F.I.; Mansego, M.L.; Zulet, M.A.; Bressan, J.; Martínez, J.A. Expression of inflammation-related miRNAs in white blood cells from subjects with metabolic syndrome after 8 wk of following a Mediterranean diet-based weight loss program. Nutrition 2016, 32, 48–55. [Google Scholar] [CrossRef] [PubMed]
  101. Richardson, K.; Nettleton, J.A.; Rotllan, N.; Tanaka, T.; Smith, C.E.; Lai, C.Q.; Parnell, L.D.; Lee, Y.C.; Lahti, J.; Lemaitre, R.N.; et al. Gain-of-function lipoprotein lipase variant rs13702 modulates lipid traits through disruption of a microRNA-410 seed site. Am. J. Hum. Genet. 2013, 92, 5–14. [Google Scholar] [CrossRef] [PubMed]
  102. Marlow, G.; Ellett, S.; Ferguson, I.R.; Zhu, S.; Karunasinghe, N.; Jesuthasan, A.C.; Han, D.Y.; Fraser, A.G.; Ferguson, L.R. Transcriptomics to study the effect of a Mediterranean-inspired diet on inflammation in Crohn's disease patients. Hum. Genom. 2013, 7, 24. [Google Scholar] [CrossRef] [PubMed]
  103. Konstantinidou, V.; Covas, M.I.; Muñoz-Aguayo, D.; Khymenets, O.; de la Torre, R.; Saez, G.; Tormos, M.C.; Toledo, E.; Marti, A.; Ruiz-Gutiérrez, V.; et al. In vivo nutrigenomic effects of virgin olive oil polyphenols within the frame of the Mediterranean diet: A randomized controlled trial. FASEB J. 2010, 24, 2546–2557. [Google Scholar] [CrossRef] [PubMed][Green Version]
  104. Llorente-Cortés, V.; Estruch, R.; Mena, M.P.; Ros, E.; González, M.A.; Fitó, M.; Lamuela-Raventós, R.M.; Badimon, L. Effect of Mediterranean diet on the expression of pro-atherogenic genes in a population at high cardiovascular risk. Atherosclerosis 2010, 208, 442–450. [Google Scholar] [CrossRef] [PubMed]
  105. Di Renzo, L.; Carraro, A.; Valente, R.; Iacopino, L.; Colica, C.; de Lorenzo, A. Intake of red wine in different meals modulates oxidized LDL level, oxidative and inflammatory gene expression in healthy people: A randomized crossover trial. Oxid. Med. Cell Longev. 2014, 2014, 681318. [Google Scholar] [CrossRef] [PubMed]
  106. Storniolo, C.E.; Casillas, R.; Bulló, M.; Castañer, O.; Ros, E.; Sáez, G.T.; Toledo, E.; Estruch, R.; Ruiz-Gutiérrez, V.; Fitó, M.; et al. A Mediterranean diet supplemented with extra virgin olive oil or nuts improves endothelial markers involved in blood pressure control in hypertensive women. Eur. J. Nutr. 2015. [Google Scholar] [CrossRef] [PubMed]
  107. Martín-Peláez, S.; Castañer, O.; Konstantinidou, V.; Subirana, I.; Muñoz-Aguayo, D.; Blanchart, G.; Gaixas, S.; de la Torre, R.; Farré, M.; Sáez, G.T.; et al. Effect of olive oil phenolic compounds on the expression of blood pressure-related genes in healthy individuals. Eur. J. Nutr. 2015. [Google Scholar]
  108. Konstantinidou, V.; Covas, M.I.; Sola, R.; Fitó, M. Up-to date knowledge on the in vivo transcriptomic effect of the Mediterranean diet in humans. Mol. Nutr. Food Res. 2013, 57, 772–783. [Google Scholar] [CrossRef] [PubMed]
  109. Fitó, M.; Konstantinidou, V. Nutritional genomics and the Mediterranean diet's effects on human cardiovascular health. Nutrients 2016, 8, 218. [Google Scholar] [CrossRef] [PubMed]
  110. Shah, S.H.; Newgard, C.B. Integrated metabolomics and genomics: Systems approaches to biomarkers and mechanisms of cardiovascular disease. Circ. Cardiovasc. Genet. 2015, 8, 410–419. [Google Scholar] [CrossRef] [PubMed]
  111. Garcia-Aloy, M.; Llorach, R.; Urpi-Sarda, M.; Tulipani, S.; Estruch, R.; Martínez-González, M.A.; Corella, D.; Fitó, M.; Ros, E.; Salas-Salvadó, J.; et al. Novel multimetabolite prediction of walnut consumption by a urinary biomarker model in a free-living population: The PREDIMED study. J. Proteome Res. 2014, 13, 3476–3483. [Google Scholar] [CrossRef] [PubMed]
  112. Garcia-Aloy, M.; Llorach, R.; Urpi-Sarda, M.; Jáuregui, O.; Corella, D.; Ruiz-Canela, M.; Salas-Salvadó, J.; Fitó, M.; Ros, E.; Estruch, R.; et al. A metabolomics-driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free-living subjects from the PREDIMED study. Mol. Nutr. Food Res. 2015, 59, 212–220. [Google Scholar] [CrossRef] [PubMed]
  113. Bondia-Pons, I.; Martinez, J.A.; de la Iglesia, R.; Lopez-Legarrea, P.; Poutanen, K.; Hanhineva, K.; Zulet, M.L. Effects of short- and long-term Mediterranean-based dietary treatment on plasma LC-QTOF/MS metabolic profiling of subjects with metabolic syndrome features: The metabolic syndrome reduction in navarra (RESMENA) randomized controlled trial. Mol. Nutr. Food Res. 2015, 59, 711–728. [Google Scholar] [CrossRef] [PubMed]
  114. González-Guardia, L.; Yubero-Serrano, E.M.; Delgado-Lista, J.; Perez-Martinez, P.; Garcia-Rios, A.; Marin, C.; Camargo, A.; Delgado-Casado, N.; Roche, H.M.; Perez-Jimenez, F.; et al. Effects of the Mediterranean diet supplemented with coenzyme q10 on metabolomic profiles in elderly men and women. J. Gerontol. A Biol. Sci. Med. Sci. 2015, 70, 78–84. [Google Scholar] [CrossRef] [PubMed]
  115. Gibbons, H.; Brennan, L. Metabolomics as a tool in the identification of dietary biomarkers. Proc. Nutr. Soc. 2016, 1–12. [Google Scholar] [CrossRef] [PubMed]
  116. Dona, A.C.; Coffey, S.; Figtree, G. Translational and emerging clinical applications of metabolomics in cardiovascular disease diagnosis and treatment. Eur. J. Prev. Cardiol. 2016, in press. [Google Scholar] [CrossRef] [PubMed]
  117. Guasch-Ferré, M.; Zheng, Y.; Ruiz-Canela, M.; Hruby, A.; Martínez-González, M.A.; Clish, C.B.; Corella, D.; Estruch, R.; Ros, E.; Fitó, M.; et al. Plasma acylcarnitines and risk of cardiovascular disease: Effect of Mediterranean diet interventions. Am. J. Clin. Nutr. 2016, 103, 1408–1416. [Google Scholar]
  118. Ruiz-Canela, M.; Toledo, E.; Clish, C.B.; Hruby, A.; Liang, L.; Salas-Salvadó, J.; Razquin, C.; Corella, D.; Estruch, R.; Ros, E.; et al. Plasma branched-chain amino acids and incident cardiovascular disease in the PREDIMED trial. Clin. Chem. 2016, 62, 582–592. [Google Scholar] [CrossRef] [PubMed]
  119. Hyötyläinen, T.; Bondia-Pons, I.; Orešič, M. Lipidomics in nutrition and food research. Mol. Nutr. Food Res. 2013, 57, 1306–1318. [Google Scholar] [CrossRef] [PubMed]
  120. Ferguson, J.F.; Allayee, H.; Gerszten, R.E.; Ideraabdullah, F.; Kris-Etherton, P.M.; Ordovás, J.M.; Rimm, E.B.; Wang, T.J.; Bennett, B.J. Nutrigenomics, the microbiome, and gene-environment interactions: New directions in cardiovascular disease research, prevention, and treatment: A scientific statement from the american heart association. Circ. Cardiovasc. Genet. 2016, in press. [Google Scholar] [CrossRef] [PubMed]
  121. Huffman, K.M.; Redman, L.M.; Landerman, L.R.; Pieper, C.F.; Stevens, R.D.; Muehlbauer, M.J.; Wenner, B.R.; Bain, J.R.; Kraus, V.B.; Newgard, C.B.; et al. Caloric restriction alters the metabolic response to a mixed-meal: Results from a randomized, controlled trial. PLoS ONE 2012, 7, e28190. [Google Scholar] [CrossRef] [PubMed]
  122. Meikle, P.J.; Barlow, C.K.; Mellett, N.A.; Mundra, P.A.; Bonham, M.P.; Larsen, A.; Cameron-Smith, D.; Sinclair, A.; Nestel, P.J.; Wong, G. Postprandial plasma phospholipids in men are influenced by the source of dietary fat. J. Nutr. 2015, 145, 2012–2018. [Google Scholar] [CrossRef] [PubMed]
  123. Lankinen, M.; Schwab, U.; Erkkilä, A.; Seppänen-Laakso, T.; Hannila, M.L.; Mussalo, H.; Lehto, S.; Uusitupa, M.; Gylling, H.; Oresic, M. Fatty fish intake decreases lipids related to inflammation and insulin signaling—A lipidomics approach. PLoS ONE 2009, 4, e5258. [Google Scholar] [CrossRef] [PubMed]
  124. Bondia-Pons, I.; Pöhö, P.; Bozzetto, L.; Vetrani, C.; Patti, L.; Aura, A.M.; Annuzzi, G.; Hyötyläinen, T.; Rivellese, A.A.; Orešič, M. Isoenergetic diets differing in their n-3 fatty acid and polyphenol content reflect different plasma and HDL-fraction lipidomic profiles in subjects at high cardiovascular risk. Mol. Nutr. Food Res. 2014, 58, 1873–1882. [Google Scholar] [CrossRef] [PubMed]
  125. Lankinen, M.; Schwab, U.; Kolehmainen, M.; Paananen, J.; Nygren, H.; Seppänen-Laakso, T.; Poutanen, K.; Hyötyläinen, T.; Risérus, U.; Brader, L.; et al. A healthy nordic diet alters the plasma lipidomic profile in adults with features of metabolic syndrome in a multicenter randomized dietary intervention. J. Nutr. 2016, 146, 662–672. [Google Scholar] [CrossRef] [PubMed]
  126. Badimon, L.; Vilahur, G.; Padro, T. Systems biology approaches to understand the effects of nutrition and promote health. Br. J. Clin. Pharmacol. 2016, in press. [Google Scholar] [CrossRef] [PubMed]
  127. Williams, E.G.; Wu, Y.; Jha, P.; Dubuis, S.; Blattmann, P.; Argmann, C.A.; Houten, S.M.; Amariuta, T.; Wolski, W.; Zamboni, N.; et al. Systems proteomics of liver mitochondria function. Science 2016, 352, aad0189. [Google Scholar] [CrossRef] [PubMed]
  128. Acharjee, A.; Kloosterman, B.; Visser, R.G.; Maliepaard, C. Integration of multi-omics data for prediction of phenotypic traits using random forest. BMC Bioinform. 2016, 17, 180. [Google Scholar] [CrossRef] [PubMed]
  129. Kim, Y.J.; Lee, H.S.; Kim, Y.K.; Park, S.; Kim, J.M.; Yun, J.H.; Yu, H.Y.; Kim, B.J. Association of metabolites with obesity and type 2 diabetes based on FTO genotype. PLoS ONE 2016, 11, e0156612. [Google Scholar] [CrossRef] [PubMed]
  130. Hartiala, J.A.; Tang, W.H.; Wang, Z.; Crow, A.L.; Stewart, A.F.; Roberts, R.; McPherson, R.; Erdmann, J.; Willenborg, C.; Hazen, S.L.; et al. Genome-wide association study and targeted metabolomics identifies sex-specific association of CPS1 with coronary artery disease. Nat. Commun. 2016, 7, 10558. [Google Scholar] [CrossRef] [PubMed]
  131. Gieger, C.; Geistlinger, L.; Altmaier, E.; Hrabé de Angelis, M.; Kronenberg, F.; Meitinger, T.; Mewes, H.W.; Wichmann, H.E.; Weinberger, K.M.; Adamski, J.; et al. Genetics meets metabolomics: A genome-wide association study of metabolite profiles in human serum. PLoS Genet. 2008, 4, e1000282. [Google Scholar] [CrossRef] [PubMed][Green Version]
  132. Raffler, J.; Friedrich, N.; Arnold, M.; Kacprowski, T.; Rueedi, R.; Altmaier, E.; Bergmann, S.; Budde, K.; Gieger, C.; Homuth, G.; et al. Genome-wide association study with targeted and non-targeted nmr metabolomics identifies 15 novel loci of urinary human metabolic individuality. PLoS Genet. 2015, 11, e1005487. [Google Scholar] [CrossRef] [PubMed]
  133. Petersen, A.K.; Zeilinger, S.; Kastenmüller, G.; Römisch-Margl, W.; Brugger, M.; Peters, A.; Meisinger, C.; Strauch, K.; Hengstenberg, C.; Pagel, P.; et al. Epigenetics meets metabolomics: An epigenome-wide association study with blood serum metabolic traits. Hum. Mol. Genet. 2014, 23, 534–545. [Google Scholar] [CrossRef] [PubMed]
  134. Sun, Y.V.; Hu, Y.J. Integrative Analysis of multi-omics data for discovery and functional studies of complex human diseases. Adv. Genet. 2016, 93, 147–190. [Google Scholar] [PubMed]
  135. Vivek-Ananth, R.P.; Samal, A. Advances in the integration of transcriptional regulatory information into genome-scale metabolic models. Biosystems 2016, in press. [Google Scholar] [CrossRef] [PubMed]
Table 1. Underlying mechanisms of the beneficial effects of the Mediterranean diet on cardiovascular diseases (CVD).
Table 1. Underlying mechanisms of the beneficial effects of the Mediterranean diet on cardiovascular diseases (CVD).
Underlying MechanismsReferences
1. Richness in antioxidants
1.1. Protects blood and tissue components from oxidative stress[42]
1.2. Limits the oxidation of unsaturated fatty acids during intestinal transit[42]
2. MUFA 1 and PUFA 2 content in membranes preserves membrane fluidity and functionality[43]
3. Richness in nitrates
3.1. Production of nitrolipids by the nitration of unsaturated fatty acids[44]
3.2. Nitric oxide generation from the nitrate-nitrite-nitric oxide pathway[37]
4. Modulation of microbial populations and activities[45,46]
5. Temporal distribution of food consumption throughout the day[47]
6. Synergistic interactions and cumulative effects betwee different foods and nutrients[42]
7. Modulation of gene expression[48]
8. Modulation of metabolite production[48]
1 MUFA, monounsaturated fatty acids; 2 PUFA, polyunsaturated fatty acids.
Table 2. Classification of new omic-based biomarkers.
Table 2. Classification of new omic-based biomarkers.
Omic-Based BiomarkersDescriptionReferences
Genetic biomarkersBased on changes in DNA, single nucleotide polymorphisms (SNP). Examples:
SNPs in the lactase gene (LCT) as proxies of milk consumption in Mendelian randomization analyses.[52]
SNPs in the lipoprotein lipase (LPL) gene as biomarkers of genetic risk of stroke.[40]
Epigenetic biomarkersBiomarkers based on the main epigenetic regulators: DNA methylation, histone modification, and non-coding RNAs. Examples:
DNA hypermetylation or hypomethylation of specific genes depending on food intake; Levels of circulating microRNAs associated with several nutrition-related diseases.[53,54]
Transcriptomic biomarkersBiomarkers based on RNA expression (whole transcriptome or differences in the expression of selected genes). Example:[55]
Differences in the gene expression profile in subjects following a Mediterranean diet in comparison with control subjects.[56]
Proteomic biomarkersBiomarkers based on the study of the proteome.[57]
Lipidomic biomarkersBiomarkers based on the study of the lipidome (comprehensive analysis of the molecular lipid species).[58]
Metabolomic biomarkersBiomarkers based on the study of the metabolome [the entire small molecule (metabolite) component of a system]. Metabolites (including peptides, lipids, nucleotides, carbohydrates, amino acids, and many other classes of small molecules) are generally defined as having an atomic mass of less than 1.5 kDa and can be exogenous, endogenous, or derived from the microbiome. Example:[59]
The 1H NMR urinary profile in subjects following a traditional Mediterranean diet in comparison with the urinary profile of subject on a low fat diet.[60]
Table 3. Intervention studies with Mediterranean diet (MedDiet) and modification of the intervention effect by genetic variants on cardiovascular risk factors and disease.
Table 3. Intervention studies with Mediterranean diet (MedDiet) and modification of the intervention effect by genetic variants on cardiovascular risk factors and disease.
ReferencePopulation AnalyzedPhenotype AnalyzedStudy CharacteristicsMain Results
Corella et al., 2013 [63]7018 high cardiovascular risk subjects participating in the PREDIMED studyStroke incidenceRandomized controlled trial with MedDiet (two groups pooled) versus a control diet (4.8 years of median follow-up)The association between the TCF7L2-rs7903146 (C>T) polymorphism and stroke was modulated by the intervention with MedDiet. TT subjects had a higher stroke incidence in the control group (p = 0.006 compared with CC), whereas dietary intervention with MedDiet reduced stroke incidence in TT homozygotes (p = 0.892 compared with CC).
Ortega Azorín et al., 2014 [64]7166 high cardiovascular risk subjects participating in the PREDIMED studyMyocardial infarction incidenceRandomized controlled trial with MedDiet (two groups pooled) versus a control diet (4.8 years of median follow-up)The association between the rs3812316 C>G SNP and myocardial infarction incidence was modulated by the intervention with MedDiet. Carriers of the G allele had significantly lower incidence of myocardial infarction only in the MedDiet intervention group.
Gómez-Delgado et al., 2014 [75]507 metabolic syndrome (MetS) patients selected from the CORDIOPREV clinical trialTriglycerides and high sensitivity C-reactive protein (hsCRP)Randomized trial: MedDiet, compared with a low-fat diet (1 year of follow-up)The rs1800629 polymorphism at the TNFA gene interacted with intervention with MedDiet to influence triglyceride metabolism and inflammation status in MetS subjects. The decrease in triglycerides and hsCRP was statistically significant in G/G subjects compared with carriers of the minor A-allele.
Di Daniele et al., 2014 [76]40 male patients with chronic kidney diseaseHomocysteine levels and other biochemical parametersDietary intervention with an Italian Mediterranean organic diet (IMOD) versus low-protein diet (LPD) for 14 daysThey found a significant interaction between MTHFR C667T polymorphism and the IMOD on homocysteine levels compared to LPD The IMOD resulted in significant improvement of homocysteine levels in TT.
Corella et al., 2014 [77]7187 high cardiovascular risk subjects participating in the PREDIMED studyFasting triglycerides and stroke incidenceRandomized controlled trial with MedDiet (two groups pooled) versus a control diet (4.8 years of median follow-up)The rs13702 T>C in the 3′ untranslated region of the LPL gene interacted with the intervention with MedDiet in determining changes in triglycerides and stroke incidence. The decreasing effect of the C allele on triglycerides and stroke incidence was only significant in the MedDiet intervention group.
Corella et al., 2016 [78]3671 non-diabetic subjects participating in the PREDIMED studyType-2 diabetes incidenceRandomized controlled trial with MedDiet (two groups pooled) versus a control diet (4.8 years of median follow-up)The CLOCK-rs4580704 C>G SNP was associated with incidence of type-2 diabetes, with variant allele (G) carriers showing decreased incidence (dominant model) compared with CC homozygotes. However, this protection was more significant in the MedDiet intervention group (p < 0.001) than in the control group (p = 0.818).
PREDIMED: PREvención con DIeta MEDiterránea; CORDIOPREV: CORonary Diet Intervention with Olive oil and cardiovascular PREVention; TNFA: human tumor necrosis factor α.

Share and Cite

MDPI and ACS Style

Fitó, M.; Melander, O.; Martínez, J.A.; Toledo, E.; Carpéné, C.; Corella, D. Advances in Integrating Traditional and Omic Biomarkers When Analyzing the Effects of the Mediterranean Diet Intervention in Cardiovascular Prevention. Int. J. Mol. Sci. 2016, 17, 1469.

AMA Style

Fitó M, Melander O, Martínez JA, Toledo E, Carpéné C, Corella D. Advances in Integrating Traditional and Omic Biomarkers When Analyzing the Effects of the Mediterranean Diet Intervention in Cardiovascular Prevention. International Journal of Molecular Sciences. 2016; 17(9):1469.

Chicago/Turabian Style

Fitó, Montserrat, Olle Melander, José Alfredo Martínez, Estefanía Toledo, Christian Carpéné, and Dolores Corella. 2016. "Advances in Integrating Traditional and Omic Biomarkers When Analyzing the Effects of the Mediterranean Diet Intervention in Cardiovascular Prevention" International Journal of Molecular Sciences 17, no. 9: 1469.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop