Final Report Summary - METSGENES (Genes, Mediterranean dietary pattern and metabolic syndrome risk)
Increased blood pressure, high blood sugar level, excess body fat around the waist and abnormal cholesterol levels come under the metabolic syndrome (MetS) umbrella. When these conditions occur together, there is an increased risk — up to six-fold — of developing heart disease, stroke and diabetes. The prevalence of MetS in the adult population is relatively high and it has a high socioeconomic cost (1).
Genomics is revolutionizing biomedical research and providing expectations with regard to disease prevention and treatment. Landmark successes from genome wide association (GWA) studies have identified numerous genetic variants underlying MetS-related traits and type 2 diabetes mellitus (T2DM) (2-4). The major remaining research challenge was to characterize gene environment interactions because these are essential for the translation of genomics into clinical medicine and improved public health. Diet is one of the most important environmental factors that interacts with the genome to modulate disease risk, and better understanding of these interactions has the potential to support disease prevention via modification of dietary recommendations. Thus, the 'Genes, Mediterranean dietary pattern and metabolic syndrome risk' (MetSgeneS) project studied the interaction of genes behind the syndrome. Combined with influence of the Mediterranean diet as well as risk factors and new biomarkers for MetS traits, we developed an obesity genetic predisposition score (GPS), in which over 60 single nucleotide polymorphisms (SNPs)— a DNA sequence that varies in a population — were integrated. The measure of obesity was also weighted according to the United States population under study — these included a United States population from a multi-ethnic study of atherosclerosis, a United States population from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study and a population from the Boston–Puerto Rican study. Then, we studied the links between the obesity GPS and body mass index (BMI) with a focus on total and saturated fat in the diet in two of the groups. A significant association between the obesity GPS and fat intake (mainly saturated fat intake) with the BMI was observed.
Furthermore, we looked at gene–diet interactions with other MetS traits such as high-density lipoprotein cholesterol, known as the 'good' cholesterol. Results show there is a significant connection between genes and diet in a Spanish population. The intake of healthy nutrients associated with the healthy Mediterranean diet such as omega-3 fatty acids, folic acid and vitamin E taken in an enriched milk improved lipid profiles compared to unenriched, reduced fat forms of milk.
We also observed that these findings are promising but preliminary. The different genetic background of the Boston–Puerto Rican population may explain in part why the same GPS cannot be used to replicate the gene–diet interactions observed in another population. We should replicate these findings in additional populations to examine whether the interaction between fat intake and GPS for BMI is replicated in other groups. If we observe similar results in other populations, we could further develop and refine the GPS to make it usable in a general population. At the same time, additional investigation using different study designs such as prospective cohorts and intervention studies may enable us to demonstrate which of the two nutrients, total dietary fat or saturated fat, is the more important modifier of the genetic risk of obesity as reflected in the GPS. Additional research is necessary before the obesity GPS can be applied practically in the development of personalized dietary recommendations to reduce obesity. Gene-diet interaction studies like this one not only identify people with genetic risk of obesity, but also suggest specific strategies to ameliorate this risk. Overall, the data so far suggest that dietary recommendations to reduce BMI in populations with high obesity GPS would be to reduce total fat intake mainly by limiting saturated fat intake. This confirms that combining SNPs using a GPS is considered as a preferred method in analyses of gene-environment interactions, although a GPS may be less informative at biological level.
As known environmental and genetic influences can only explain a part in the overall variability in cardiovascular disease (CVD) risk, epigenomics has emerged as a promising area that can hopefully decipher some of the gaps in our current knowledge of cardiovascular risk factors. Epigenetic modifications that can influence CVD risk include DNA methylation, histone acetylation and deacetylation and alteration in the levels or function of non-coding RNAs (a.k.a. microRNAs) (5). MicroRNAs are small non coding RNAs that have emerged as important modulators of different cellular process that affect both physiological and pathological process (6,7). MicroRNAs bind partially or entirely to complementary target site in the 3’untranslated regions (3’UTRs) of mRNA, causing translational repression and/or mRNA destabilization (8). An atherogenic dyslipidemia characterized by elevated levels of triglycerides-rich lipoproteins, low levels of high density lipoproteins (HDL) cholesterol are implicated in both CDV risk and suggested to contribute in the progression of microvascular complications of diabetes or MetS (9-11). A better understanding of how microRNAs regulate metabolic abnormalities related to the MetS such as insulin resistance, cholesterol and lipoprotein metabolism point out the possibility of antisense therapeutic targeting different microRNAs as a strategy to manage MetS.
For this reason, we also studied the role of diet (mainly the type of fat) on microRNA regulation. Although there is some evidence that certain fatty acids (FA) may modulate the expression of certain microRNAs, our global understanding of which particular microRNA is modulated by a particular FA in certain physiological or pathological condition remains obscure. Thus, taking advantage of preliminary studies (12), we determined the effects of different dietary FA on the expression of whole-genome microRNAs (miRNome) in liver tissue of pregnant rats and their pups.
Pregnant rats received five experimental diets with different sources of FA -the soybean oil (SO) diet contained 9% SO; the olive oil (OO) diet contained 9% OO; the fish oil (FO) diet contained 8% fish oil plus 1% sunflower oil; the linseed oil (LO) diet contained 8% LO plus 1% sunflower oil; and the palm oil (PO) diet contained 8% PO plus 1% SO as the only non-vitamin lipophilic component- from conception to day 12 of gestation (first half of pregnancy) and returned to the standard diet through the rest of gestation. The whole miRNome was evaluated in pregnant rats at day 20 and in one day-old offspring by standard Real time qPCR. The composition of plasma FA at day 20 resembled the diet consumed during the first 12 days. In liver samples, some miRNAs were specifically modulated by a particular source of FA. Bioinformatic analysis of predicted and validated targets of these miRNAs suggest that different FA may contribute, through the modulation of miRNAs, to the regulation of different metabolic pathways including those related to insulin signaling. FA concentration in fetal plasma was also influenced by the maternal diet during the first 12 days of pregnancy. microRNAs analysis of pup’s liver samples suggest that the expression of certain miRNAs might be influenced by either pup’s FA composition, maternal diet or both.
In overall our results showed that maternal consumption of different fatty acids fed during early pregnancy influences microRNA expression in both maternal and offspring tissues, which may epigenetically explain the long-term phenotypic changes –such as insulin sensibility- of the male offspring.
In conclusion, three years into the MetGeneS resulted in four published papers, one paper under revision and two manuscript drafts. All of them have been disseminated at appropriate conferences translating results into clinical practice and public health recommendations for prevention and therapy of the pre-diabetes phase of MetS.
References:
1) Kassi E, et al. BMC Med 9: 48.
2) Scott LJ et al. Science. 2007;316(5829):1341-5.
3) Zeggini E, et al. Nat Genet. 2008;40(5):638-45.
4) Rung J, et al. Nat Genet. 2009;41(10):1110-5.
5) Ordovás JM, Smith CE. Nat Rev Cardiol. 2010.
6) Hannon GJ, Conklin DS. Methods Mol Biol. 2004;257:255-66.
7) Small EM et al. Circulation. 2010;121(8):1022-32.
8) Bartel DP. Cell. 2009;23;136(2):215-33.
9) Misra A, et al. Am J Cardiovasc Drugs. 2003;3(5):325-38.
10) Brown WV. Am J Cardiol. 2008;102(12A):10L-13L.
11) Jones PH. Am J Cardiol. 2008;102[suppl]:41L–47L.
12) Sardinha FL, et al. Am J Physiol Regul Integr Comp Physiol. 2013:304:R313-320.
Genomics is revolutionizing biomedical research and providing expectations with regard to disease prevention and treatment. Landmark successes from genome wide association (GWA) studies have identified numerous genetic variants underlying MetS-related traits and type 2 diabetes mellitus (T2DM) (2-4). The major remaining research challenge was to characterize gene environment interactions because these are essential for the translation of genomics into clinical medicine and improved public health. Diet is one of the most important environmental factors that interacts with the genome to modulate disease risk, and better understanding of these interactions has the potential to support disease prevention via modification of dietary recommendations. Thus, the 'Genes, Mediterranean dietary pattern and metabolic syndrome risk' (MetSgeneS) project studied the interaction of genes behind the syndrome. Combined with influence of the Mediterranean diet as well as risk factors and new biomarkers for MetS traits, we developed an obesity genetic predisposition score (GPS), in which over 60 single nucleotide polymorphisms (SNPs)— a DNA sequence that varies in a population — were integrated. The measure of obesity was also weighted according to the United States population under study — these included a United States population from a multi-ethnic study of atherosclerosis, a United States population from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study and a population from the Boston–Puerto Rican study. Then, we studied the links between the obesity GPS and body mass index (BMI) with a focus on total and saturated fat in the diet in two of the groups. A significant association between the obesity GPS and fat intake (mainly saturated fat intake) with the BMI was observed.
Furthermore, we looked at gene–diet interactions with other MetS traits such as high-density lipoprotein cholesterol, known as the 'good' cholesterol. Results show there is a significant connection between genes and diet in a Spanish population. The intake of healthy nutrients associated with the healthy Mediterranean diet such as omega-3 fatty acids, folic acid and vitamin E taken in an enriched milk improved lipid profiles compared to unenriched, reduced fat forms of milk.
We also observed that these findings are promising but preliminary. The different genetic background of the Boston–Puerto Rican population may explain in part why the same GPS cannot be used to replicate the gene–diet interactions observed in another population. We should replicate these findings in additional populations to examine whether the interaction between fat intake and GPS for BMI is replicated in other groups. If we observe similar results in other populations, we could further develop and refine the GPS to make it usable in a general population. At the same time, additional investigation using different study designs such as prospective cohorts and intervention studies may enable us to demonstrate which of the two nutrients, total dietary fat or saturated fat, is the more important modifier of the genetic risk of obesity as reflected in the GPS. Additional research is necessary before the obesity GPS can be applied practically in the development of personalized dietary recommendations to reduce obesity. Gene-diet interaction studies like this one not only identify people with genetic risk of obesity, but also suggest specific strategies to ameliorate this risk. Overall, the data so far suggest that dietary recommendations to reduce BMI in populations with high obesity GPS would be to reduce total fat intake mainly by limiting saturated fat intake. This confirms that combining SNPs using a GPS is considered as a preferred method in analyses of gene-environment interactions, although a GPS may be less informative at biological level.
As known environmental and genetic influences can only explain a part in the overall variability in cardiovascular disease (CVD) risk, epigenomics has emerged as a promising area that can hopefully decipher some of the gaps in our current knowledge of cardiovascular risk factors. Epigenetic modifications that can influence CVD risk include DNA methylation, histone acetylation and deacetylation and alteration in the levels or function of non-coding RNAs (a.k.a. microRNAs) (5). MicroRNAs are small non coding RNAs that have emerged as important modulators of different cellular process that affect both physiological and pathological process (6,7). MicroRNAs bind partially or entirely to complementary target site in the 3’untranslated regions (3’UTRs) of mRNA, causing translational repression and/or mRNA destabilization (8). An atherogenic dyslipidemia characterized by elevated levels of triglycerides-rich lipoproteins, low levels of high density lipoproteins (HDL) cholesterol are implicated in both CDV risk and suggested to contribute in the progression of microvascular complications of diabetes or MetS (9-11). A better understanding of how microRNAs regulate metabolic abnormalities related to the MetS such as insulin resistance, cholesterol and lipoprotein metabolism point out the possibility of antisense therapeutic targeting different microRNAs as a strategy to manage MetS.
For this reason, we also studied the role of diet (mainly the type of fat) on microRNA regulation. Although there is some evidence that certain fatty acids (FA) may modulate the expression of certain microRNAs, our global understanding of which particular microRNA is modulated by a particular FA in certain physiological or pathological condition remains obscure. Thus, taking advantage of preliminary studies (12), we determined the effects of different dietary FA on the expression of whole-genome microRNAs (miRNome) in liver tissue of pregnant rats and their pups.
Pregnant rats received five experimental diets with different sources of FA -the soybean oil (SO) diet contained 9% SO; the olive oil (OO) diet contained 9% OO; the fish oil (FO) diet contained 8% fish oil plus 1% sunflower oil; the linseed oil (LO) diet contained 8% LO plus 1% sunflower oil; and the palm oil (PO) diet contained 8% PO plus 1% SO as the only non-vitamin lipophilic component- from conception to day 12 of gestation (first half of pregnancy) and returned to the standard diet through the rest of gestation. The whole miRNome was evaluated in pregnant rats at day 20 and in one day-old offspring by standard Real time qPCR. The composition of plasma FA at day 20 resembled the diet consumed during the first 12 days. In liver samples, some miRNAs were specifically modulated by a particular source of FA. Bioinformatic analysis of predicted and validated targets of these miRNAs suggest that different FA may contribute, through the modulation of miRNAs, to the regulation of different metabolic pathways including those related to insulin signaling. FA concentration in fetal plasma was also influenced by the maternal diet during the first 12 days of pregnancy. microRNAs analysis of pup’s liver samples suggest that the expression of certain miRNAs might be influenced by either pup’s FA composition, maternal diet or both.
In overall our results showed that maternal consumption of different fatty acids fed during early pregnancy influences microRNA expression in both maternal and offspring tissues, which may epigenetically explain the long-term phenotypic changes –such as insulin sensibility- of the male offspring.
In conclusion, three years into the MetGeneS resulted in four published papers, one paper under revision and two manuscript drafts. All of them have been disseminated at appropriate conferences translating results into clinical practice and public health recommendations for prevention and therapy of the pre-diabetes phase of MetS.
References:
1) Kassi E, et al. BMC Med 9: 48.
2) Scott LJ et al. Science. 2007;316(5829):1341-5.
3) Zeggini E, et al. Nat Genet. 2008;40(5):638-45.
4) Rung J, et al. Nat Genet. 2009;41(10):1110-5.
5) Ordovás JM, Smith CE. Nat Rev Cardiol. 2010.
6) Hannon GJ, Conklin DS. Methods Mol Biol. 2004;257:255-66.
7) Small EM et al. Circulation. 2010;121(8):1022-32.
8) Bartel DP. Cell. 2009;23;136(2):215-33.
9) Misra A, et al. Am J Cardiovasc Drugs. 2003;3(5):325-38.
10) Brown WV. Am J Cardiol. 2008;102(12A):10L-13L.
11) Jones PH. Am J Cardiol. 2008;102[suppl]:41L–47L.
12) Sardinha FL, et al. Am J Physiol Regul Integr Comp Physiol. 2013:304:R313-320.