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Research Project: Personalized Nutrition and Healthy Aging

Location: Jean Mayer Human Nutrition Research Center On Aging

2023 Annual Report

Objective 1: Conduct and analyze dietary intervention studies to validate gene-diet interactions and identify the underlying mechanisms using omic technologies. Sub-Objective 1A: To characterize the response of cardiometabolic, epigenetics and other age-related biomarkers and the microbiome to diets differing in saturated fat and prebiotics content (animal-based diet versus plant-based diet) in individuals carrying CC and TT genotypes at the common APOA2 -265T>C (rs5082) SNP using a short-term crossover, randomized feeding study, and to elucidate the physiological mechanism(s) by which diet impinges on metabolic pathways through APOA2 genotypes. Sub-Objective 1B: To characterize the TCF7L2-by-diet interaction with respect to those type 2 diabetes (T2D) and cardiovascular disease (CVD) risk factors identified in observational studies for validation in the context of a short-term randomized controlled feeding study (low-fat diet versus Mediterranean diet), and to elucidate the molecular mechanisms responsible for these GxD interactions using epigenetics and metabolomics. Sub-Objective 1C: To develop polygenic risk scores (PRS) predicting the changes in and relationships between cardiovascular disease (CVD) risk factors and disease incidence in response to long-term (>=1 y) dietary interventions [Mediterranean diet (MedDiet) or Low-fat control diet]. Objective 2: Identify genomic, epigenomic, metabolomic, and microbiome-related biomarkers that sustain healthy aging, and define specific personalized dietary, physical activity, and other lifestyle factors associated with optimal health of older adults. Subobjective 2A: To identify genetic and dietary factors that modify CPT1A methylation and cardio-metabolic traits. Subobjective 2B: To identify interactions between the genome, epigenome and diet and lifestyle on lipid profiles that signify CMD risk.

Promoting healthy aging by tailoring nutritional guidance based on a person's genetic makeup is an emerging science that has great promise. The Nutrition and Genomics lab is a pioneer in this area and focuses its research on the role of precision nutrition and cardiometabolic diseases – the leading cause of death in the United States. Our approach harnesses the availability of tremendous computing power and huge datasets from existing cohorts to study the crosstalk between habitual diets and the genome to identify gene-by-diet interactions that sustain individual optimal health for older adults. This objective will be accomplished using Big Data analytics of omics data (i.e., genome-wide datasets on gene and protein expression, genetic variation, methylation, and metabolite levels). We also conduct short-term feeding studies in people preselected based on particular genotypes to validate gene-by-diet interactions revealed by previous observational studies and, using multi-omic data integration (i.e., genomics, epigenomics, microbiomics, and metabolomics) methods, identifying the mechanisms underlying such interactions. This research will generate new knowledge on how non-modifiable and modifiable factors interact to prevent cardiovascular diseases and type 2 diabetes. Further, it will contribute much-needed evidence and tools to define and implement personalized nutrition as a common practice for the benefit of all stakeholders.

Progress Report
a) In support of Objective 1, we investigated the efficacy of diets in the secondary prevention of cardiovascular disease (CVD) in the Coronary Diet Intervention with Olive Oil and Cardiovascular Prevention (CORDIOPREV) Study, a randomized clinical trial including 1002 patients with established coronary heart disease (CHD) who were randomly assigned to receive a Mediterranean diet or a low-fat diet intervention, with a follow-up of 7 years. Our study revealed a lower incidence of cardiovascular events in the group assigned to the Mediterranean diet, showing the potential of diet as a powerful tool in secondary prevention strategies. Then, to understand better the gene-diet interactions responsible for the interindividual variability in response to the study diets, we investigated the dietary modulation of postprandial triglycerides (TGs) through the Zinc finger protein 1 (ZPR1) gene. The ZPR1 protein has a crucial role in cell division, growth, and the proper functioning of the mitochondria. A variant of the ZPR1 gene, called rs964184, has been associated with changes in how the body processes fats. Therefore, we analyzed data from this variant in CORDIOPREV Study participants. We found that subjects with the risk allele (G) showed a higher postprandial response under a high-fat diet. However, the response was attenuated under a low-fat diet, suggesting the potential for personalized dietary interventions based on genetic variations to manage CHD risk factors. b) In search of new CVD risk predictors, we investigated metabolomic markers that could predict remission in patients with type 2 diabetes (T2D) after dietary intervention. Our study involved 190 patients newly diagnosed with T2D. We employed an untargeted metabolomics approach to identify metabolic differences between individuals who achieved remission (RE) and those who did not (non-RE) during a 5-year dietary intervention study. A robust biostatistical pipeline was implemented. Our analysis revealed a significant increase in 12 metabolites in the non-RE group compared to the RE group. Therefore, our study has identified 12 endogenous metabolites with the potential to predict T2DM remission following dietary intervention. This suggests that these metabolites, combined with clinical variables, could help devise more precise and personalized therapeutic strategies in clinical practice for T2DM patients. c) In support of Objective 1A, we examined epigenetic (microRNA (miRNA))-diet interactions. We found differences in miRNA expression when individuals with the CC genotype of APOA2 switch from a high-fat diet to a low-fat diet. Specifically, the expression of 8 common miRNAs increased while the expression of 5 miRNAs decreased. This could potentially influence how these individuals metabolize foods and process nutrients and could possibly have implications for understanding disease risks or developing personalized nutrition plans. d) Complementing our clinical findings, we undertook a mechanistic study concerning Trimethylamine N-oxide (TMAO). TMAO is generated from dietary nutrients, including choline and carnitine, which are found in high amounts in red meat, eggs, and some seafood. The gut bacteria convert these nutrients into trimethylamine (TMA), which is then absorbed and converted in the liver to TMAO. Previous observational studies have linked higher levels of TMAO in the blood with an increased risk of adverse cardiovascular events. However, more research is needed to understand the underlying mechanisms. Therefore, we investigated whether TMAO could influence epigenetic mechanisms, such as miRs levels, using human coronary artery endothelial cells (HCAECs). TMAO was observed to significantly increase the expression of all analyzed members of the miR-17/92 cluster, a finding that supports our previous work indicating that the cluster is related to inflammatory and atherosclerosis signaling pathways. e) Lastly, we also investigated the impact of menopause on postprandial metabolism, metabolic health, and lifestyle. Menopause is a significant transition in a woman's life, often associated with adverse health changes. Nonetheless, the postprandial metabolic alterations and their underlying factors remain largely unexplored during this period. Our research utilized data from the Personalised Responses to Dietary Composition Trial (PREDICT 1) UK cohort. We collected a wide range of data, including phenotypic characteristics, anthropometric measurements, dietary habits, gut microbiome data, and fasting and postprandial cardiometabolic blood measurements. Continuous glucose monitoring (CGM) data was also used. We compared the data between the different menopausal groups while controlling for factors like age, BMI, menopausal hormone therapy (MHT) use, and smoking status. Our findings reveal that post-menopausal women exhibited higher fasting blood measures, higher sugar intake, poorer sleep, and unfavorable postprandial metabolic responses compared to pre-menopausal women. This group also showed unfavorable CGM measures. Even when we controlled for age, postprandial glucose responses remained higher in post-menopausal women. MHT was linked with positive health outcomes, including favorable visceral fat, fasting, and postprandial measures. Our mediation analysis indicates that dietary habits and specific gut bacterial species partially mediated the associations between menopause and metabolic health indicators. In conclusion, our findings underscore the importance of monitoring risk factors for T2DM and CVD in women transitioning to and beyond menopause. This understanding could play a critical role in reducing morbidity and mortality associated with the decline in estrogen during this life stage. f) Further bolstering Objective 2, we conducted a study to discover blood proteins that could predict subclinical atherosclerosis. The impetus for this research was that cardiovascular imaging, while beneficial for enhancing risk prediction beyond traditional risk factors, is not universally accessible. In this investigation, we employed a hypothesis-free proteomics approach to analyze plasma samples from 444 subjects drawn from the PESA cohort study. Of these, 222 had extensive atherosclerosis as determined by imaging, with the remaining 222 as matched controls. Samples were analyzed at two time points, spaced three years apart, for discovery, and further external validation was conducted using 350 subjects from the AWHS cohort study and a broader group of 2,999 subjects from the Assessing the Prevalence of Subclinical Vascular Disease and Hidden Kidney Disease (ILERVAS) cohort study. Our analysis revealed that the plasma proteins PIGR, IGHA2, APOA, HPT, and HEP2 were associated with subclinical atherosclerosis independently of traditional risk factors at both time points in the discovery and validation cohorts. From these, the multivariate analysis yielded a potential three-protein biomarker panel comprised of IGHA2, APOA, and HPT. A machine-learning model utilizing these three proteins was able to predict subclinical atherosclerosis in the ILERVAS cohort, demonstrating remarkable predictive power even among individuals at low cardiovascular risk according to the FHS 10-year score. The proposed three-protein panel may offer a valuable tool for predicting subclinical atherosclerosis, particularly in areas where imaging technology is not readily available. This development could significantly improve primary prevention strategies and help target interventions more accurately. g) Lastly, we initiated an effort to discover metabolites that significantly correlate with the protective e2 allele of the apolipoprotein E (APOE) gene. To this end, we established a consortium of five studies of healthy aging and extreme human longevity, which included 3,545 participants. The consortium comprised the New England Centenarian Study, the Baltimore Longitudinal Study of Aging, the Arivale Study, the Longevity Genes Project/LonGenity studies, and the Long Life Family Study. In these studies, we analyzed the association between APOE genotype groups: E2, E3, and E4 and metabolite profiles. We then used a fixed-effect meta-analysis to aggregate the results across these five studies. Our meta-analysis identified a unique signature of 19 metabolites that were significantly associated with the E2 group. This signature includes 10 glycerolipids and 4 glycerophospholipids, all elevated in E2 carriers compared to E3. The organic acid 6-hydroxy indole sulfate, previously linked to changes in the gut microbiome indicative of healthy aging and longevity, was also found in higher levels in E2 carriers compared to E3 carriers. This work consolidates and extends prior results tying the APOE gene to lipid regulation. It uncovers new connections between the e2 allele, lipid metabolism, aging, and the gut-brain axis, thereby deepening our understanding of the molecular mechanisms that underpin healthy aging and longevity.

1. Alcohol and carbohydrates impact blood differently and may influence how genes influence cardiovascular risk factors. The practice of advocating for personalized diets as a strategy to lower the risk of heart disease is on the rise, even though the links among diet, lifestyle, and heart disease risk factors aren't entirely clear. To bridge this knowledge gap, ARS-funded researchers from Boston, Massachusetts, explored the impact of diet and lifestyle choices on alterable sections of an individual’s DNA, referred to as methylation sites. These sites were studied in relation to their association with blood lipid levels, an indicator of heart disease risk, in two population studies. The findings revealed that alcohol and carbohydrate consumption have separate impacts on blood lipids, offering valuable insights into the potential benefits of altering the way genes influence cardiovascular risk factors. This breakthrough will propel the fast-developing field of precision nutrition and personalized diets. It will also assist healthcare professionals in crafting customized dietary recommendations.

Review Publications
Lee, B.Y., Ordovas, J.M., Parks, E.J., Anderson, C.A., Barabasi, A., Clinton, S.K., De La Haye, K., Duffy, V.B., Franks, P.W., Ginexi, E.M., Hammond, K.J., Hanlon, E.C., Hittle, M., Ho, E., Horn, A.L., Isaacson, R.S., Mabry, P.L., Malone, S., Martin, C.K., Mattei, J., Meydani, S.N., Nelson, L.M., Neuhouser, M., Parent, B.J., Pronk, N.P., Roche, H.M., Saira, S., Scheer, F.A., Segal, E., Sevick, M., Spector, T.D., Van Horn, L.B., Varady, K.A., Saroja Voruganti, V., Ferguson, M. 2022. Research gaps and opportunities in precision nutrition: an NIH workshop report. The American Journal of Clinical Nutrition.
Smith, C.E., Parnell, L.D., Lai, C., Rush, J.E., Adin, D.B., Ordovas, J.M., Freeman, L.M. 2022. Metabolomic profiling in dogs with dilated cardiomyopathy eating non-traditional or traditional diets and in healthy controls. Scientific Reports.
Civeira-Marin, M., Cenarro, A., Marco-Benedi, V., Bea, A.M., Mateo-Gallego, R., Moreno-Franco, B., Ordovas, J.M., Laclaustra, M., Civeira, F., Lamiquiz-Moneo, I. 2022. APOE genotypes modulate inflammation independently of their effect on lipid metabolism. International Journal of Molecular Sciences.
Alegria-Lertxundi, I., Aguirre, C., Bujanda, L., Fernandez, F.J., Polo, F., Ordovas, J.M., Etxezarraga, M., Zabalza, I., Larzabal, M., Portillo, I., M. de Pancorbo, M., Palencia-Madrid, L., Garcia-Etxebarria, K., Rocandio, A., Arroyo-Izaga, M. 2020. Gene-diet interactions in colorectal cancer: survey design, instruments, participants and descriptive data of a case-control study in the Basque country. Nutrients. 12(8):2362.
Valenzuela, P.L., Santos-Lozano, A., Torres Barran, A., Morales, J.S., Castillo-Garcia, A., Ruilope, L.M., Rios-Insua, D., Ordovas, J.M., Lucia, A. 2021. Poor self-reported sleep is associated with risk factors for cardiovascular disease: a cross-sectional analysis in half a million adults. European Journal of Clinical Investigation. e13738.
Sebastiani, P., Song, Z., Ellis, D., Tian, Q., Schwaiger-Haber, M., Stancliffe, E., Lustgarten, M., Funk, C., Baloni, P., Marron, M.M., Gurinovich, A., Li, M., Leschyk, A., Monti, S., Montasser, M., Feitosa, M.F., Ordovas, J.M., Haigis, M., Milman, S., Barzilai, N., Ferrucci, L., Rappaport, N., Patti, G.J., Perls, T.T. 2022. A metabolomic signature of the APOE2 allele. GeroScience.
Yubero-Serano, E.M., Alcala-Diaz, J.F., Gutierrez-Mariscal, F., Arrinas De Larriva, A.P., Pena-Orihuela, P.J., Blanco-Rojo, R., Martinez-Botas, J., Torres-Pena, J.D., Perez-Martinez, P., Ordovas, J.M., Delgado-Lista, J., Gomez-Coronado, D., Lopez-Miranda, J. 2021. Association between cholesterol efflux capacity and peripheral artery disease in coronary heart disease patients with and without type 2 diabetes: from the CORDIOPREV study. Cardiovascular Diabetology. 20:72.
Berciano, S., Figueiredo, J., Brisbois, T.D., Alford, S., Koecher, K., Eckhouse, S., Ciati, R., Kussmann, M., Ordovas, J.M., Stebbins, K., Blumberg, J.B. 2022. Precision nutrition: maintaining scientific integrity while realizing market potential. Frontiers in Nutrition.
Maruvada, P., Lampe, J.W., Wishart, D.S., Barupal, D., Chester, D.N., Dodd, D., Djoumbou-Feunang, Y., Dorrestein, P.C., Dragsted, L.O., Draper, J., Duffy, L.C., Dwyer, J.T., Emenaker, N.J., Fiehn, O., Gerszten, R.E., Hu, F.B., Karp, R.W., Klurfeld, D.M., Laughlin, M.R., Little, R.A., Lynch, C.J., Moore, S.C., Nicastro, H.L., O'Brien, D.M., Ordovas, J.M., Osganian, S.K., Playdon, M., Prentice, R., Raftery, D., Reisdorph, N., Roche, H.M., Ross, S.M., Sang, S., Scalbert, A., Srinivas, P.R., Zeisel, S.M. 2019. Perspective: Dietary biomarkers of intake and exposure-exploration with omics approaches. Advances in Nutrition. 11(2):200-215.
Valenzuela, P.L., Carrera-Bastos, P., Galvez, B.G., Ruiz-Hurtado, G., Ordovas, J.M., Ruilope, L.M., Lucia, A. 2020. Lifestyle interventions for the prevention and treatment of hypertension. Nature Reviews Cardiology. 18(4):251-275.
Zheng, Y., Huang, T., Want, T., Mei, Z., Sun, Z., Zhang, T., Ellervik, C., Chai, J., Sim, X., Van Dam, R.M., Tai, E., Koh, W., Dorajoo, R., Saw, S., Sabanayagam, C., Wong, T., Gupta, P., Rossing, P., Ahluwalia, T.S., Vinding, R.K., Bisgaard, H., Bonnelykke, K., Wang, Y., Graff, M., Voortman, T., Van Rooij, F., Hofman, A., Van Heemst, D., Noordam, R., Estampador, A.C., Varga, T.V., Enzenback, C., Scholz, M., Theiry, J., Burkhardt, R., Orho-Melander, M., Schulz, C.A., Ericson, U., Sonestedt, E., Kubo, M., Akiyama, M., Zhou, A., Kilpelainen, T.O., Hansen, T., Kleber, M.E., Dalgado, G., McCarthy, M., Lemaitre, R., Feliz, J.F., Jaddoe, V.W., Wu, Y., Mohlke, K.L., Lehtimaki, T., Wang, C.A., Pennell, C.E., Schunkert, H., Kessler, T., Zeng, L., Willenborg, C., Peters, A., Lieb, W., Grote, V., Rzehak, P., Koletzko, B., Erdmann, J., Munz, M., Wu, T., He, M., Yu, C., Lecoeur, C., Froguel, P., Corella, D., Moreno, L., Lai, C., Pitkanen, N., Boreham, C.A., Ridker, P.M., Rosendaal, F., de Mutsert, R., Power, C., Paternoster, L., Sorensen, T.I., Tjonneland, A., Overvad, K., Djousse, L., Rivadeneira, F., Lee, N.R., Raitakari, O., Kahonen, M., Viikari, J., Langhendries, J., Escribano, J., Verduci, E., Dedoussis, G., Coltell, O., Ordovas, J.M., Qi, L. 2020. Mendelian randomization analysis does not support causal associations of birth weight with hypertension risk and blood pressure in adulthood. European Journal of Epidemiology. 35:685-697.
Alegria-Lertxundi, I., Aguirre, C., Bujanda, L., Fernandez, F.J., Polo, F., Ordovas, J.M., Etxezarraga, M., Zabalza, I., Larzabal, M., Portillo, I., M de Pancorbo, M., Garcia-Etxebarria, K., Roncandio, A., Arroyo-Izaga, M. 2020. Food groups, diet quality and colorectal cancer risk in the Basque country. World Journal of Gastroenterology. 26(28):4108-4125.
Lamb, J.J., Sone, M., D'Adamo, C.R., Volkov, A., Metti, D., Aronica, L., Minich, D., Leary, M., Class, M., Carullo, M., Ryan, J.J., Larson, I.A., Lundquist, E., Contractor, N., Eck, B., Ordovas, J.M., Bland, J.S. 2022. Personalized lifestyle intervention and functional evaluation health outcomes survey: presentation of the LIFEHOUSE study using N-of-One Tent-Umbrella-Bucket Design. Personalized Medicine.
Garcia-Rios, A., Ordovas, J.M. 2022. Chronodisruption and cardiovascular disease. Clinica e Investigacion en Arteriosclerosis. 34:S32-S37.
Sanchez-Cabo, F., Rossello, X., Fuster, V., Benito, F., Manzano, J., Silla, J., Fernandez-Alvira, J.M., Oliva, B., Fernandez-Friera, L., Lopez-Melgar, B., Mendiguren, J.M., Sanz, J., Ordovas, J.M., Andres, V., Fernandez-Ortiz, A., Bueno, H., Ibanez, B., Garcia-Ruiz, J., Lara-Pezzi, E. 2020. Machine learning improves cardiovascular risk definition for young, asymptomatic individuals. Journal of the American College of Cardiology. 76(14):1674-1685.
Vin, X., Willinger, C.M., Keefe, J., Liu, J., Fernandez-Ortiz, A., Ibanez, B., Penalvo, J., Adourian, A., Chen, G., Corella, D., Pamplona, R., Portero-Otin, M., Jove, P., Courchesne, P., Van Duijn, C.M., Fuster, V., Ordovas, J.M., Demirkan, A., Larson, M.G., Levy, D. 2019. Lipidomic profiling identifies signatures of metabolic risk. EBioMedicine. 51:102520.
Klimentidis, Y.C., Arora, A., Newell, M., Zhou, J., Ordovas, J.M., Renquist, B.J., Wood, A.C. 2020. Phenotypic and genetic characterization of lower LDL cholesterol and increased type 2 diabetes risk in the UK Biobank. Diabetes. 69(10):2194-2205.
Diez-Ricote, L., Ruiz-Valderrey, P., Mico, V., Blanco, R., Tome-Carneiro, J., Davalos, A., Ordovas, J.M., Daimiel, L. 2022. TMAO upregulates members of the miR-17/92 cluster and impacts targets associated with atherosclerosis. International Journal of Molecular Sciences.
Martin-Hernandez, R., Regiero, G., Ordovas, J.M., Davalos, A. 2019. NutriGenomeDB: a nutrigenomics exploratory and analytical platform. Database: The Journal of Biological Databases and Curation.
Becerra-Tomas, N., Mena-Sanchez, G., Diaz-Lopez, A., Martinez-Gonzalez, M.A., Babio, N., Corella, D., Freixer, G., Romaguera, D., Vioque, J., Alonso-Gomez, A.M., Warnberg, J., Martinez, J., Serra-Majem, L., Estruch, R., Fernandez-Garcia, J., Lapetra, J., Pinto, X., Tur, J., Lopez-Miranda, J., Bueno-Cavanillas, A., Gaforio, J.J., Matia-Martin, P., Daimiel, L., Matia-Sanchez, V., Vidal, J., Vazquez, C., Ros, E., Razquin, C., Abellan Cano, I., Sorli, J.V., Torres, L., Morey, M., Navarrete-Muno, E.M., Tojal Sierra, L., Crespo-Oliva, E., Zulet, M., Sanchez-Villegas, A., Casas, R., Bernal-Lopez, M., Santos-Lozano, J., Corbella, E., del Mar Bibiloni, M., Ruiz-Canela, M., Fernandez-Carrion, R., Quifer, M., Prieto, R.M., Fernandez-Brufal, N., Salaverria Lete, I., Cenoz, J., Llimona, R., Salas-Salvado, J., Ordovas, J.M. 2019. Cross-sectional association between non-soy legume consumption, serum uric acid and hyperuricemia: the PREDIMED-plus study. European Journal of Nutrition.
Asnicar, F., Berry, S.E., Valdes, A.M., Nguyen, L.H., Piccinno, G., Drew, D.A., Leeming, E., Gibson, R., Le Roy, C., Khatib, H.A., Francis, L., Mazidi, M., Mompeo, O., Valles-Colomer, M., Tett, A., Beghini, F., Dubois, L., Bazzani, D., Thomas, A., Mirzayi, C., Khleborodova, A., Oh, S., Hine, R., Bonnett, C., Capdevila, J., Danzanvilliers, S., Giordano, F., Geistlinger, L., Waldron, L., Davies, R., Hadjigeorgiou, G., Wolf, J., Ordovas, J.M., Gardner, C.D., Franks, P.W., Chan, A., Huttenhower, C., Spector, T.D., Segata, N. 2021. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nature Medicine. 27:321-332.
Rossello, X., Fuster, V., Oliva, B., Fernandez-Friera, L., Lopez-Melgar, B., Mendiguren, J.M., Lara-Pezzi, E., Bueno, H., Fernandez-Ortiz, A., Ibanez, B., Ordovas, J.M. 2020. Association between body size phenotypes and subclinical atherosclerosis. Journal of Clinical Endocrinology and Metabolism. 105(12):3734-3744.