PERSONALIZED NUTRITION AND HEALTHY AGING

• Sponsoring Institution: Agricultural Research Service/USDA
• Project Status: COMPLETE
• Funding Source: USDA INHOUSE
• Reporting Frequency: Annual
• Accession No.: 0436415
• Project Start Date: Oct 1, 2019
• Project End Date: Mar 11, 2024
• Program Code: [(N/A)]- (N/A)

Recipient Organization
AGRICULTURAL RESEARCH SERVICE
(N/A)
BOSTON,MA 02111

Performing Department
(N/A)

Non Technical Summary
(N/A)

Animal Health Component

0%

Research Effort Categories

Basic

100%

Applied

0%

Developmental

0%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
702	5010	1010	50%
702	6010	1010	50%

Knowledge Area
702 - Requirements and Function of Nutrients and Other Food Components;

Subject Of Investigation
6010 - Individuals; 5010 - Food;

Field Of Science
1010 - Nutrition and metabolism;

Keywords

aging

cardiometabolic

disease

cardiovascular

diseases

diabetes

epigenomics

gene-diet

interactions

genomics

metabolomics

microbiome

nutrigenetics

nutrigenomics

nutrition

personalized

nutrition

precision

nutrition

obesity

Goals / Objectives
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.

Project Methods
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 â¿¿ 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 10/01/19 to 03/11/24

Outputs
PROGRESS REPORT Objectives (from AD-416): 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. Approach (from AD-416): 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. The research conducted over the life of this program has significantly advanced our understanding of gene-diet interactions and their impact on cardiometabolic health and aging. The integration of -omic technologies in our studies has provided more profound insights into the underlying mechanisms of these interactions. Our findings support the development of personalized dietary interventions to promote healthy aging and optimize health outcomes in older adults. A. We investigated the effects of a personalized nutrition program on cardiometabolic health using a randomized controlled trial. Participants [n=347], aged 41⿿70 years and generally representative of the average U.S. population, were randomized to the personalized dietary program (PDP) [n=177] or control [n=170]. This trial compared the impact of a PDP tailored diet to individual postprandial glucose and triglyceride responses versus general dietary advice. The PDP led to significant reductions in triglycerides and improvements in other health markers, showing how personalized diets can influence type 2 diabetes (T2D) and cardiovascular disease (CVD) risk factors. B. We examined genetic, metabolic, microbiome, and meal composition/ context contributions to postprandial metabolic responses in the Personalized Responses to Dietary Composition Trial PREDICT-1 Study, enrolling 1,102 twins and unrelated healthy U.K./U.S. adults. Our analysis revealed significant and consistent differences in blood triglyceride, glucose, and insulin responses to identical meals, influenced by person-specific factors such as the gut microbiome, with genetic variants having a modest impact. Modifiable factors like meal timing significantly affected these responses. We developed a machine learning model that predicts both triglyceride and glycemic responses to food, potentially informing personalized diet strategies. C. In the Coronary Diet Intervention with Olive Oil and Cardiovascular Prevention CORDIOPREV study, a randomized clinical trial including 1002 patients with coronary heart disease (CHD), we investigated the efficacy of a Mediterranean versus low-fat diet over 5 years. We found that the Mediterranean diet significantly preserved kidney function in patients with T2D and obesity. This supports the role of personalized dietary interventions based on genetic variations to manage chronic disease risk. D. We also examined the association between telomere length and diabetes remission in the same cohort. Our findings indicated that patients with longer telomeres at baseline were more likely to achieve diabetes remission following dietary intervention. E. We explored novel factors for inclusion in precision nutrition studies, such as chronobiology and its association with cardiometabolic risk. Our analyses in the CORDIOPREV study showed that evening chronotypes had higher cardiometabolic risk and less robust circadian-related rhythms than morning types, even during nutritional intervention. We also carried out an integrated analysis of sweet taste preference and its modulation by T2D, identifying genetic factors associated with sweet taste preference. F. We investigated the genetic and microbiome contributions to dietary T2D prevention and remission. The CORDIOPREV study assessed T2D risk and remission, finding different baseline gut microbiota associated with dietary patterns and T2D outcomes. Our findings provide evidence of the microbiome as a predictive factor for diet-induced T2D remission. G. We examined the AMY1 gene related to dietary carbohydrate intake and T2D, discovering that low AMY1 copy numbers increase the risk of insulin resistance with age, suggesting dietary modifications to mitigate T2D risk. Additionally, we analyzed the ABCG1 gene for epigenetic changes relating to statin use and the risk of T2D. We found a strong association between statin use and DNA methylation at the ABCG1 gene, leading to an increased risk of T2D. H. We identified associations between dietary intake and DNA methylation at the CPT1A gene, finding that carbohydrate intake directly correlates with DNA methylation, while fat intake shows an inverse relationship. This balance between carbohydrates and fat influences gene activity and has significant health consequences. I. We conducted a prospective observational cohort study in the Boston Puerto Rican Health Study (BPRHS) and the Atherosclerosis Risk in Communities (ARIC) Study, identifying metabolite profiles associated with cardiometabolic stress, all-cause mortality, and CHD. We also investigated clusters of co-regulated metabolites associated with T2D among Puerto Rican adults, identifying several metabolite clusters linked to T2D prevalence. These findings underscore the shared molecular pathophysiology of metabolic dysfunction, cardiovascular disease, and longevity, suggesting pathways for improving prognosis across these conditions J. We developed, validated, and improved dietary and disease risk biomarkers to support precision nutrition approaches. Using continuous glucose monitors (CGM) in the PREDICT-1 Study, we demonstrated strong concordance in measuring postprandial glycemic responses, supporting their potential application in precision nutrition. K. We initiated an effort to discover metabolites correlating with the protective e2 allele of the apolipoprotein E (APOE) gene, identifying a unique signature of 19 metabolites associated with the E2 group. This research provides insights into lipid metabolism, aging, and the gut- brain axis, thereby deepening our understanding of the molecular mechanisms underpinning healthy aging and longevity. L. We identified metabolites associated with cognitive decline and Alzheimer's disease in Puerto Rican adults, linking medium-chain fatty acids and tyrosine metabolism to cognitive health. The findings help identify genetic and dietary factors that modify metabolic traits, aligning with our objective of understanding biomarkers of aging. M. Our research demonstrated that adherence to the Mediterranean diet is associated with longer telomeres, particularly in women. This highlights the role of diet and genetics in influencing aging biomarkers, supporting our goal of identifying factors that promote healthy aging. N. Our study revealed sex-specific variations in gut microbiota linked to CHD. We identified bacterial taxa as key discriminators between sexes and CHD status, contributing to understanding how the genome, epigenome, and diet interact to affect lipid profiles and coronary microvascular dysfunction (CMD) risk. Artificial Intelligence (AI)/Machine Learning (ML) For this project, we utilized convolutional neural networks (CNNs) and a variety of machine learning methods. Specifically, to develop an obesity prediction model, we employed a generalized multifactor dimensionality reduction approach to identify key factors and their combinations from the whole genome, epigenome, and various dietary and lifestyle variables. We tested a series of machine learning methods, including Gamma regression, Tweedie regression, Bayesian Ridge, Elastic Net, Lasso, Support Vector Machine Regression, Stochastic Gradient Descent (SGD) regressor, and Gaussian Process Regression, to obtain the best prediction model. Computing Resources: Our computations and analyses were performed on local GPU-based machines and SCINet's High-Performance Computing (HPC) clusters (Ceres), depending on the task's scale and the need for parallelization. Additionally, we collaborated with external partners to test a series of machine-learning approaches. Benefits of AI Methods: The application of AI methods has significantly benefited this project by accelerating research progress. The use of CNNs and machine learning techniques enabled us to process and analyze large datasets more efficiently, allowing for the identification of complex patterns and interactions that would be difficult to discern using traditional statistical methods. This has expanded the scale and scope of our research, leading to more accurate prediction models, and enhancing our understanding of the genetic and environmental factors contributing to obesity. Consequently, the integration of AI methods has improved the precision and effectiveness of our dietary intervention strategies, promoting better health outcomes. ACCOMPLISHMENTS 01 Targeting diets based on genetics may improve weight loss. ARS-funded researchers in Boston, Massachusetts, examined whether people with different versions of a specific gene called APOA2 lose weight differently when following a low-carbohydrate diet or a low-fat diet. The researchers found that people with a certain version of the APOA2 gene, known as TT, lost more weight on a low-carb diet compared to a low-fat diet. Conversely, people with other versions of the gene (CT or CC) lost more weight on a low-carb diet compared to a low-fat diet in the first three months. However, the differences disappeared by six months on each diet. This study suggests that tailoring diets based on genetic makeup, particularly the APOA2 gene type, could help improve weight loss outcomes. However, individuals with certain genetic makeups may need more educational and behavioral reinforcement to maintain long- term weight loss. This approach, known as precision nutrition, can lead to significant health benefits.

Impacts
(N/A)

Publications

• Coltell, O., Asensio, E.M., Sorli, J.V., Ortega-Azorin, C., Fernandez- Carrion, R., Pascual, E.C., Barragan, R., Gonzalez, J.I., Estruch, R., Alzate, J.F., Perez-Fidalgo, A., Portoles, O., Ordovas, J.M. 2023. Associations between the new DNA-methylation-based telo-mere length estimator, the Mediterranean diet, and genetics in a Spanish population at high cardiovascular risk. Antioxidants. https://doi.org/10.3390/ antiox12112004.
• Guan, Y., Cheng, C., Bellomo, L.I., Narain, S., Bigornia, S., Garelnabi, M. , Scott, T., Ordovas, J.M., Tucker, K.L., Bhadelia, R., Koo, B. 2023. APOE4 allele-specific associations between diet, multimodal biomarkers, and cognition among Puerto Rican adults in Massachusetts. Frontiers in Aging Neuroscience. https://doi.org/10.3389/fnagi.2023.1285333.
• Coltell, O., Asensio, E.M., Sorli, J.V., Ortega-Azorin, C., Fernandez- Carrion, R., Pascual, E.C., Barragan, R., Gonzalez, J.I., Estruch, R., Alzate, J.F., Perez-Fidalgo, A., Portoles, O., Ordovas, J.M., Corella, D. 2023. Associations between the new DNA-methylation-based telomere length estimator, the mediterranean diet and genetics in a Spanish population at high cardiovascular risk. Antioxidants. https://doi.org/10.3390/ antiox12112004.
• Parnell, L.D., Magadmi, R., Zwanger, S., Shukitt Hale, B., Lai, C., Ordovas, J.M. 2023. Dietary responses of dementia-related genes encoding metabolic enzymes. Nutrients. 15(3):644. https://doi.org/10.3390/ nu15030644.
• Sanchez-Cabo, F., Fuster, V., Silla, J., Gonzalez, G., Lorenzo-Vivas, E., Alvarez, R., Callejas, S., Benguria, A., Gil, E., Nunez, E., Oliva, B., Mendiguren, J.M., Cortes-Canteli, M., Bueno, H., Andres, V., Ordovas, J.M., Fernandez-Freira, L., Quesada, A.J., Garcia, J.M., Rossello, X., Vasquez, J., Dopazo, A., Fernandez-Ortiz, A., Ibanez, B., Fuster, J.J., Lara-Pezzi, E. 2023. A multi-omics approach unveils an association between subclinical atherosclerosis and epigenetic age acceleration mediated by systemic inflammation. European Heart Journal. https://doi.org/10.1093/eurheartj/ ehad361.
• Rivas-Garcia, L., Quintana-Navarro, G.M., Torres-Pena, J.D., Arenas-de Larriva, A.P., Alcala-Diaz, J.F., Yubero-Serrano, E.M., Perez-Caballero, A. I., Ortiz-Morales, A.M., Rangel-Zuniga, O., Lopez-Moreno, A., Ordovas, J.M. , Perez-Martinez, P., Lopez-Miranda, J., Delgado-Lista, J. 2023. Dietary antioxidant intake reduces carotid intima-media thickness in coronary heart disease patients: From the CORDIOPREV study. Free Radicals in Biology and Medicine. https://doi.org/10.1016/j.freeradbiomed.2023.11.026.
• Baccarelli, A., Ordovas, J.M. 2023. The epigenetics of early cardiovascular and metabolic diseases development: From mechanisms to precision medicine. Circulation Research. 132(12):1648-1662. https://doi. org/10.1161/CIRCRESAHA.123.322135.
• Runblad, A., Sandoval, V., Holven, K., Ordovas, J.M., Ulven, S. 2023. Omega-3 fatty acids and individual variability in plasma triglyceride response: A mini-review. Redox Biology. https://doi.org/10.1016/j.redox. 2023.102730.
• Graniel, I.P., Babio, N., Becerra-Tomas, N., Toledo, E., Camacho-Barcia, L. , Corella, D., Castaner-Nino, O., Romaguera, D., Vioque, J., Alonso-Gomez, A.M., Warnberg, J., Martinez, J.A., Serra-Majem, L., Estruch, R., Tinahones, F., Fernandez-Aranda, F., Lapertra, J., Pinto, X., Tur, J.A., Garcia-Rios, A., Bueno-Cavanillas, A., Gaforio, J.J., Matia-Martin, P., Daimiel, L., Sanchez, V.M., Prieto-Sanchez, L., Ros, E., Razquin, C., Mestres, C., Sorli, J.V., Cuenca-Royo, A., Rios, A., Torres-Collado, L., Vaquero-Luna, J., Perez-Farinos, N., Zulet, M.A., Sanchez-Villegas, A., Casas, R., Bernal-Lkopez, M., Santos-Lozano, J., Corbella, X., Mateos, D., Buil-Cosiales, P., Jimenez-Murcia, S., Fernandez-Carrion, R., Forcano- Gamazo, L., Lopez, M., Sempere-Pascual, M.A., Moreno-Rodriquez, A., Gea, A. , de la Torre-Fornell, R., Salas-Salvado, J., Ordovas, J.M. 2020. Association between coffee consumption and total dietary caffeine intake with cognitive functioning: Cross-sectional assessment in an elderly Mediterranean population. European Journal of Nutrition. https://doi.org/ 10.1007/s00394-020-02415-w.
• Parnell, L.D., McCaffrey, K.S., Brooks, A., Smith, C.E., Lai, C., Christensen, J.J., Wiley, C.D., Ordovas, J.M. 2023. Rate-limiting enzymes in cardiometabolic health and aging in humans. Lifestyle Genomics. 16:124- 138. https://doi.org/10.1159/000531350.
• Lai, C., Parnell, L.D., Lee, Y., Zeng, H., Smith, C., Mckeowan, N.M., Ordovas, J.M. 2023. The impact of alcoholic drinks and dietary factors on epigenetic markers associated with triglyceride levels. Frontiers in Genetics. https://doi.org/10.3389/fgene.2023.1117778.
• Bermingham, K.M., Mazidi, M., Franks, P.W., Maher, T., Valdes, A.M., Linenberg, I., Wolf, J., Hadjigeorgiou, G., Spector, T.D., Menni, C., Ordovas, J.M., Berry, S.E., Hall, W.L. 2023. Characterisation of fasting and postprandial NMR metabolites: Insights from the ZOE PREDICT 1 study. Nutrients. https://doi.org/10.3390/nu15112638.
• Fernandez-Carrion, R., Sorli, J.V., Asensio, E.M., Pascual, E.C., Portoles, O., Alvarez Sala, A., Frances, F., Ramirez Sabio, J.B., Perez Fidalgo, A., Villamil, L., Tinahones, F.J., Estruch, R., Ordovas, J.M., Coltell, O., Corella, D. 2023. DNA methylation signatures of tobacco smoking in a high cardiovascular risk population: Modulation by the mediterranean diet. International Journal of Environmental Research and Public Health. https:// doi.org/10.3390/ijerph20043635.
• Guitierrez Mariscal, F., Alcala Diaz, J., Quintana Navarro, G., de la Cruz, A.S., Torres Pena, J.D., Cardelo, M.P., Arenas Larriva, A.P., Malagon, M. M., Romero Cabrera, J.L., Ordovas, J.M., Perez Martinez, P., Delgado Lista, J., Yubero Serrano, E., Lopez Miranda, J. 2023. Changes in quantity plant based protein intake on type 2 diabetes remission in coronary heart disease patients: From the CORDIOPREV study. European Journal of Nutrition. https://doi.org/10.1007/s00394-022-03080-x.
• Garcia-Fernandez, H., Arenas-De Larriva, A.P., Lopez-Moreno, J., Gutierrez Mariscal, F., Romero Cabrera, J.L., Molina-Abril, H., Torres Pena, J.D., Rodriguez-Cano, D., Malagon, M.M., Ordovas, J.M., Delgado Lista, J., Perez Martinez, P., Lopez Miranda, J., Camargo, A. 2024. Sex-specific differences in intestinal microbiota associated with cardiovascular disease. Biology of Sex Differences. https://doi.org/10.1186/s13293-024- 00582-7.

Progress 10/01/22 to 09/30/23

Outputs
PROGRESS REPORT Objectives (from AD-416): 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. Approach (from AD-416): 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. 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. Artificial Intelligence (AI)/Machine Learning (ML) In fiscal year 2023, we leveraged both classical machine learning and deep learning methods in our research. This was done to enhance our understanding and prediction capabilities in areas related to disease detection and prevention. The following provides an overview of our use of AI/ML methods in our work. AI Methods: The research undertaken made use of non-neural machine learning algorithms, such as support vector machines, and deep learning techniques, primarily convolutional neural networks (CNNs). naive Bayes, Elastic Net (EN), gradient boosting machine, and distributed random forest. For instance, our study on subclinical atherosclerosis used a hypothesis-free proteomics approach, followed by a machine-learning model to predict subclinical atherosclerosis. This approach was based on data of plasma protein levels, which were independent of traditional risk factors (PMCID: PMC8844841). In another study, we combined genome-wide and epigenome-wide scans with machine learning to predict obesity risk. This was based on interaction models involving single nucleotide polymorphisms, DNA methylation sites, and dietary/lifestyle factors, utilizing the Generalized Multifactor Dimensionality Reduction method (PMCID: PMC8763388). Computing Resources: We performed our computations and analyses on local Graphics Processing Units (GPU)-based machines and on SCINet's High- Performance Computing (HPC) clusters (Ceres), depending on the scale of the task and the need for parallelization. Other computing resources provided by external collaborators include a HPC cluster with 38 nodes: 34 physical nodes (master and compute nodes) and 4 virtual nodes (shadow master and submit nodes) with global 1590 vCPU and 4447 GB of RAM. All interconnect by an isolated, secure network of 40 Gbs. A second cluster is a Hadoop cluster with 5 nodes: 4 physical (master and compute nodes) and a virtual node (ambari) with global 104 vCPU and 377 GB of RAM running the Hortonwork HDP 3.1.4.0 distribution managed with Ambari 2.7.4.0. As before, all interconnects by an isolated, secure network of 40 Gbs. The clusters mounts by Network File System version 4 (NFS4) or Hadoop Distributed File System (HDFS) a shared file- system from a dedicated HPC ultra-low latency storage (EMC-Isilon) with 110 TB of capacity. Benefits to the Project: The use of AI methods has catalyzed our research progress, enabling us to process vast datasets and uncover complex, high- dimensional patterns that would have been challenging to identify through traditional statistical methods. For instance, the use of machine learning algorithms in our study on cardiovascular risk (PMID: 33004133) allowed us to predict the presence and extent of subclinical atherosclerosis more accurately than traditional risk prediction tools. Furthermore, our research on postprandial responses to food (PMCID: PMC8265154) also benefited from AI methods, helping us predict metabolic responses to food intake. This opens the way for precision nutrition strategies tailored to individual metabolic profiles. In conclusion, AI methods have been a cornerstone of our research accelerating progress, enhancing scale, and expanding the scope of our projects. They have not only facilitated the processing of big data, but also facilitated more accurate modeling and prediction, contributing to our success in securing the AIMINGS (Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems) Center Pilot Grant. The AIMINGS Center serves as the AI Center for the NIH Nutrition for Precision Health (NPH) Consortium. ACCOMPLISHMENTS 01 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.

Impacts
(N/A)

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. https://doi.org/10.1093/ajcn/nqac237.
• 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. https://doi.org/10.1038/s41598-022-26322-8.
• 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. https://doi.org/10.3390/ijms232112947.
• 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. https://doi. org/10.3390/nu12082362.
• 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. https://doi. org/10.1111/eci.13738.
• 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. https://doi.org/10. 1186/s12933-021-01260-3.
• 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. https://doi.org/ 10.3389/fnut.2022.979665.
• 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. https://doi.org/10. 1093/advances/nmz075.
• 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. https://doi.org/10.1038/s41569-020-00437-9.
• 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. https://doi.org/10.1007/s10654-020-00638-z.
• 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. https://doi. org/10.3748/wjg.v26.i28.4108.
• 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. https://doi.org/10.3390/ jpm12010115.
• Garcia-Rios, A., Ordovas, J.M. 2022. Chronodisruption and cardiovascular disease. Clinica e Investigacion en Arteriosclerosis. 34:S32-S37. https:// doi.org/10.1016/j.arteri.2021.12.004.
• 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. https://doi.org/10.1007/s11357-022-00646-9.
• 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. https://doi.org/10.1016/j.jacc.2020.08.017.
• 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. https://doi.org/10.1016/j.ebiom.2019.10.046.
• 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. https://doi.org/10.2337/db19-1134.
• 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. https://doi.org/10.1007/s00394-019-02070-W.
• 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. https://doi. org/10.3390/ijms232012107.
• 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. https://doi. org/10.1093/database/baz097.
• 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. https://doi.org/10.1038/s41591-020-01183-8.
• 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. https://doi.org/10.1210/clinem/dgaa620.

Progress 10/01/21 to 09/30/22

Outputs
PROGRESS REPORT Objectives (from AD-416): 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. Approach (from AD-416): 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. Under Objective 1, we investigated more deeply the association between genetic loci and cardiometabolic traits. Whereas, many genetic loci have shown associations with individual cardiometabolic disease (CMD)-related traits, no investigations have simultaneously tested associations identifying loci across multiple traits. Therefore, we conducted separate genome-wide association studies (GWAS) for systolic and diastolic blood pressure (SBP/DBP), hemoglobin A1c (HbA1c), low- and high-density lipoprotein cholesterol (LDL-C/HDL-C), waist-to-hip-ratio (WHR), and triglycerides (TGs) in the UK Biobank (N~456,823). Multiple loci were significant (N=145-333) for each trait. Still, only four loci (VEGFA, GRB14-COBLL1, KLF14, and RGS19-OPRL1) were associated with risk across all seven traits (P<5ÿ10-8). Understanding the pathways between these loci and CMD risk may eventually identify factors that can be used to identify new gene-by-diet interactions to be used in precision nutrition. We also explored novel factors of interest for inclusion in precision nutrition studies. In this regard, chronobiology is an emerging factor associated with CMD and could be the target of dietary intervention. Therefore, we investigated in the CORDIOPREV study (n=857) whether individuals with evening chronotypes are prone to suffer chronodisruption and display worse lifestyle habits and higher cardiovascular disease (CVD) risk than morning types. We also investigated whether potential associations were moderated by long-term consumption of two healthy diets (Mediterranean and low-fat diets). Our analyses show that evening types had higher TGs, C-reactive protein, and homocysteine and lower HDL-C than morning types (P<0.05). Moreover, they were more sedentary, displayed less and delayed physical activity, and ate and slept later. In conclusion, evening types with CVD had higher cardiometabolic risk and less robust circadian-related rhythms than morning types, which remained even during the nutritional intervention. Other emerging factors relevant to precision nutrition are smell and taste. Therefore, we conducted an integrated analysis of the influence of sweet taste preference on the preference for sugary foods and its modulation by T2D. Moreover, we explored new genetic factors associated with sweet taste preference. We studied 425 elderly white European subjects with metabolic syndrome and analyzed taste preference, taste perception, sugary-food likings, and biochemical and genetic markers. We found that T2D subjects have a higher preference for sweet taste and thus sugary foods than non-T2D subjects. We did not detect statistically significant differences in preferences for the other tastes (bitter, salty, sour, or umami). In an exploratory GWAS, we identified some single nucleotide polymorphisms (SNPs) associated with sweet taste preference, especially in the PTPRN2 (Protein Tyrosine Phosphatase Receptor Type N2) gene, whose minor allele was associated with a lower sweet taste preference. In conclusion, this population strongly related sweet taste preference to sugary food liking. Our exploratory GWAS identified an interesting new candidate gene associated with sweet taste preference. For Objective 2, we investigated whether network analysis revealed clusters of coregulated metabolites associated with T2D among Puerto Rican adults. We measured fasting plasma metabolites (n>600) among participants aged 40-75 years in the Boston Puerto Rican Health Study (BPRHS) and San Juan Overweight Adult Longitudinal Study (SOALS), with and without T2D. Our results show that six metabolite clusters, including glucose transport, sphingolipids, acyl-cholines, sugar metabolism, branched-chain and aromatic amino acids, and fatty acid biosynthesis, were significantly associated with T2D in the BPRHS and replicated in SOALS. In summary, we identified several known and novel metabolite clusters among Puerto Rican adults associated with the prevalence of T2D. We have been developing, validating, and improving several dietary and disease risk biomarkers to support both objectives. These activities are essential to improve the reliability of the data used to inform Artificial Intelligence and Machine Learning approaches in precision nutrition. In this regard, continuous glucose monitors (CGM) are commonly used devices in precision nutrition that measure glycemic variation throughout the day. However, despite their popularity, there are concerns about their reliability for categorizing glycemic responses to foods that would limit their potential application. Using the PREDICT (Personalised REsponses to DIetary Composition Trial) 1 Study, we evaluated the concordance of two simultaneously worn CGM devices in measuring postprandial glycemic responses. Participants wore 2 CGM devices simultaneously, either from the same brand or different brands, for =14 d while consuming standardized and ad libitum foods. We examined the coefficient of variation (CV) and correlation of the incremental area under the glucose curve at 2 h (glucoseiAUC0-2 h). Our results demonstrated that the CV of glucoseiAUC0-2 h was 3.7% for intrabrand device and 12.5% for interbrand device comparisons. Overall, our data showed strong concordance of CGM devices in monitoring glycemic responses and support their potential use in precision nutrition.

Impacts
(N/A)

Publications

• Diez-Ricote, L., Ruiz-Valderrey, P., Mico, V., Blanco-Rojo, R., Tome- Carneiro, J., Davalos, A., Ordovas, J.M., Daimiel, L. 2021. Trimethylamine n-Oxide (TMAO) modulates the expression of cardiovascular disease-related microRNAs and their targets. International Journal of Molecular Sciences. 22(20):11145. https://doi.org/10.3390/ijms222011145.
• Rangel-Zuniga, O.A., Vals-Delgado, C., Alcala-Diaz, J.F., Quintana-Navarro, G., Krylova, Y., Leon-Acuna, A., Luque, R.M., Gomez-Delgado, F., Delgado- Lista, J., Ordovas, J.M., Perez-Martinez, P., Camargo, A., Lopez-Miranda, J. 2020. A set of miRNAs predicts T2DM remission in patients with coronary heart disease: from the CORDIOPREV study. Molecular Therapy - Nucleic Acids. 23:255-263. https://doi.org/10.1016/j.omtn.2020.11.001.
• Roncero-Ramos, I., Gutierrez-Mariscal, F.M., Gomez-Delgado, F., Villasanta- Gonzalez, A., Torres-Pena, J.D., De La Cruz-Ares, S., Rangel-Zuniga, O.A., Luque, R.M., Ordovas, J.M., Delgado-Lista, J., Perez-Martinez, P., Camargo, A., Alcala-Diaz, J.F., Lopez-Miranda, J. 2021. Beta cell functionality and hepatic insulin resistance are major contributors to type 2 diabetes remission and starting pharmacological therapy: from CORDIOPREV randomized controlled trial. Translational Research. https://doi.org/10.1016/j.trsl. 2021.07.001.
• Westerman, K., Fernandez-Sanles, A., Patil, P., Sebastiani, P., Jacques, P. F., Starr, J.M., Deary, I.J., Liu, Q., Liu, S., Elosua, R., DeMeo, D.F., Ordovas, J.M. 2020. Epigenomic assessment of cardiovascular disease risk and interactions with traditional risk metrics. Journal of the American Heart Association. 9(8):e015299. https://doi.org/10.1161/JAHA.119.015299.
• Daimiel, L., Mico, V., Valls, R.M., Pedret, A., Motilva, M., Rubio, L., Fito, M., Farras, M., Covas, M., Sola, R., Ordovas, J.M. 2020. Impact of phenol-enriched virgin olive oils on the postprandial levels of circulating microRNAs related to cardiovascular disease. Molecular Nutrition and Food Research. 64(15):e2000049. https://doi.org/10.1002/mnfr. 202000049.
• Baquerizo-Sedano, L., Chaquila, J.A., Aguilar, L., Ordovas, J.M., Gonzalez- Muniesa, P., Garaulet, M. 2021. Anti-COVID-19 measures threaten our healthy body weight: Changes in sleep and external synchronizers of circadian clocks during confinement. Clinical Nutrition. https://doi.org/ 10.1016/j.clnu.2021.06.019.
• Jimenez-Torres, J., Alcala-Diaz, J.F., Torres-Pena, J.D., Gutierrez- Mariscal, F.M., Leon-Acuna, A., Gomez-Luna, P., Fernandez-Gandara, C., Quintana-Navarro, G., Fernandez-Garcia, J.C., Perez-Martinez, P., Ordovas, J.M., Delgado-Lista, J., Yubero-Serrano, E.M., Lopez-Miranda, J. 2021. Mediterranean diet reduces atherosclerosis progression in coronary heart disease: An analysis of the CORDIOPREV randomized controlled trial. Stroke. 52(11):3440-3449. https://doi.org/10.1161/STROKEAHA.120.033214.
• Camargo, A., Vais-Delgado, C., Alcala-Diaz, J.F., Vallasanta-Gonzalez, A., Gomez-Delgado, F., Haro, C., Leon-Acuna, A., Cardelo, M.P., Torres-Pena, J. D., Guler, I., Malagon, M.M., Ordovas, J.M., Perez-Martinez, P., Delgado- Lista, J., Lopez-Miranda, J. 2020. A diet-dependent microbiota profile associated with incident type 2 diabetes: From the CORDIOPREV study. Molecular Nutrition and Food Research. 64(23):2000730. https://doi.org/10. 1002/mnfr.202000730.
• Martinez-Perez, C., San-Cristobal, R., Guallar-Castillon, P., Martinez- Gonzalez, M.A., Salas-Salvado, J., Corella, D., Castaner, O., Martinez, J., Alonso-Gomez, A., Warnberg, J., Vioque, J., Romaguera, D., Lopez-Miranda, J., Estruch, R., Tinahones, F., Lapetra, J., Serra-Majem, L., Bueno- Cavanillas, A., Tur, J.A., Sanchez, V.M., Pinto, X., Gaforio, J., Matia- Martin, P., Vidal, J., Vasquez, C., Ros, E., Bes-Rostrollo, M., Babio, N., Sorli, J.V., Lassale, C., Perez-Sanz, B., Vaquero-Luna, J., Ajejas Bazan, M., Barcelo-Iglesias, M.N., Konieczna, J., Garcia-Rios, A., Bernal-Lopez, M., Santos-Lozano, J., Toledo, E., Becerra-Tomas, N., Portoles, O., Zomeno, M., Abete, I., Moreno-Rodriguez, A., Lecea-Juarez, O., Nishi, S., Munoz- Martinez, J., Ordovas, J.M., Daimiel, L. 2021. Use of different food classification systems to assess the association between ultra-processed food consumption and cardiometabolic health in an elderly population with metabolic syndrome (PREDIMED-Plus Cohort). Nutrients. 13(7):2471. https:// doi.org/10.3390/nu13072471.
• Haslam, D.E., Liang, L., Wang, D.D., Kelly, R.S., Wittenbecher, C., Perez, C.M., Martinez, M., Lee, C., Clish, C., Wong, D., Parnell, L.D., Lai, C., Ordovas, J.M., Manson, J.E., Hu, F.B., Stampfer, M.J., Tucker, K.L., Joshipura, K., Bhupathiraju, S.N. 2021. Associations of network-derived metabolite clusters with prevalent type 2 diabetes among adults of Puerto Rican descent. BMJ Open Diabetes Research & Care. https://doi.org/10.1136/ bmjdrc-2021-002298.
• Penalvo, J.L., Mertens, E., Munoz-Cabrejas, A., Leon-Latre, M., Jarauta, E. , Laclaustra, M., Ordovas, J.M., Casasnovas, J.A., Uzhova, I., Moreno- Franco, B. 2021. Work shift, lifestyle factors, and subclinical atherosclerosis in Spanish male workers: A mediation analysis. Nutrients. https://doi.org/10.3390/nu13041077.
• Astrup, A., Magkos, F., Bier, D.M., Brenna, J.Thomas, de Oliveira Otto, M. C., Hill, J.O., King, J.C., Mente, A., Ordovas, J.M., Volek, J.S., Yusuf, S., Krauss, R.M. 2020. Saturated fats and health: A reassessment and proposal for food-based recommendations: JACC State-of-the-Art review. Journal of the American College of Cardiology. 76(7):844-857. https://doi. org/10.1016/j.jacc.2020.05.077.
• Astrup, A., Teicholz, N., Magkos, F., Bier, D., Brenna, J., King, J.C., Mente, A., Ordovas, J.M., Volek, J.S., Yussuf, S., Krauss, R.M. 2021. Dietary saturated fats and health: Are the U.S. guidelines evidence-based? Nutrients. 13(10):3305. https://doi.org/10.3390/nu13103305.
• Bush, C.L., Blumberg, J.B., El-Sohemy, A., Minich, D.M., Ordovas, J.M., Reed, D.G., Yunez Behm, V.A. 2019. Toward the definition of personalized nutrition: A proposal by the American Nutrition Association. Journal of the American College of Nutrition. https://doi.org/10.1080/07315724.2019. 1685332.
• Westerman, K.E., Ordovas, J.M. 2020. DNA methylation and incident cardiovascular disease. Current Opinion in Clinical Nutrition and Metabolic Care. 23(4):236-240. https://doi.org/10.1097/MCO. 0000000000000659.
• Dashti, H.S., Ordovas, J.M. 2021. Genetics of sleep and insights into its relationship with obesity. Annual Review of Nutrition. https://doi.org/10. 1146/annurev-nutr-082018-124258.
• Lee, Y., Christensen, J.J., Parnell, L.D., Smith, C.E., Shao, J.Y., McKeown, N.M., Ordovas, J.M., Lai, C. 2022. Using machine learning to predict obesity based on genome-wide, epigenome-wide gene-gene and gene- diet interactions. Frontiers in Genetics. 12:783845. https://doi.org/10. 3389/fgene.2021.783845.
• Tsereteli, N., Vallat, R., Fernandez-Tajes, J., Delahanty, L.M., Ordovas, J.M., Drew, D.A., Valdes, A.M., Segata, N., Chan, A., Wolf, J., Berry, S.E. , Walker, M.P., Spector, T.D., Franks, P.W. 2021. Impact of insufficient sleep on dysregulated blood glucose control under standardised meal conditions. Diabetologia. 65:356-365. https://doi.org/10.1007/s00125-021- 05608-y.
• Sorli, J.V., Barragan, R., Coltell, O., Portoles, O., Pascual, E.C., Ortega-Azorin, C., Gonzalez, J.I., Estruch, R., Saiz, C., Perez-Fidalgo, A. , Ordovas, J.M., Corella, D. 2020. Chronological age interacts with the circadian melatonin receptor 1B gene variation, determining fasting glucose concentrations in Mediterranean populations. Additional analyses on type-2 diabetes risk. Nutrients. 12(11):3323. https://doi.org/10.3390/ nu12113323.
• Yubero-Serrano, E., Fernandez-Gandara, C., Garcia-Rios, A., Rangel-Zuniga, O.A., Gutierrez-Mariscal, F.M., Torres-Pena, J.D., Marin, C., Lopez-Moreno, J., Castano, J., Delgado-Lista, J., Ordovas, J.M., Perez-Martinez, P., Lopez-Miranda, J. 2020. Mediterranean diet and endothelial function in patients with coronary heart disease: an analysis of the CORDIOPREV randomized controlled trial. PLoS Medicine. 17(9):e1003282. https://doi. org/10.1371/journal.pmed.1003282.
• Berry, S.E., Valdes, A.M., Drew, D.A., Asnicar, F., Mazidi, M., Wolf, J., Capdevila, J., Hadjigeorgiou, G., Davies, R., Al Khatib, H., Bonnett, C., Ganesh, S., Bakker, E., Hart, D., Mangino, M., Merino, J., Linenberg, I., Wyatt, P., Ordovas, J.M., Gardner, C.D., Dalahanty, L.M., Chan, A.T., Segata, N., Franks, P.W., Spector, T.D. 2020. Human postprandial responses to food and potential for precision nutrition. Nature Medicine. 26(6):964- 973. https://doi.org/10.1038/s41591-020-0934-0.
• Mazidi, M., Valdes, A.M., Ordovas, J.M., Hall, W.L., Pujol, J.C., Wolf, J., Hadjigeorgiou, G., Segata, N., Sattar, N., Koivula, R., Spector, T.D., Franks, P.W., Berry, S.E. 2021. Meal-induced inflammation: postprandial insights from the Personalised REsponses to DIetary Composition Trial (PREDICT) study in 1000 participants. American Journal of Clinical Nutrition. 114(3):1028-1038. https://doi.org/10.1093/ajcn/nqab132.
• Ma, Y., Fu, Y., Tian, Y., Gou, W., Miao, Z., Yang, M., Ordovas, J.M., Zheng, J. 2021. Individual postprandial glycemic responses to diet in n-of- 1 trials: Westlake N-of-1 trials for macronutrient intake (WE-MACNUTR). Journal of Nutrition. 151(10):3158-3167. https://doi.org/10.1093/jn/ nxab227.
• Romero-Cabrera, J., Garaulet, M., Jimenez-Torres, J., Alcala-Diaz, J.F., Quintana-Navarro, G.M., Martin-Piedra, L., Torres-Pena, J.D., Rodriguez- Cantalejo, F., Rangel-Zuniga, O.A., Yubero-Serrano, E.M., Luque, R.M., Ordovas, J.M., Lopez-Miranda, J., Perez-Martinez, P., Garcia-Rios, A. 2021. Chronodisruption and diet associated with increased cardiometabolic risk in coronary heart disease patients: the CORDIOPREV study. Translational Research. https://doi.org/10.1016/j.trsl.2021.11.001.
• Vals-Delgado, C., Alcala-Diaz, J.F., Molina-Abril, H., Roncero-Ramos, I., Caspers, M.P., Schuren, F.H., Van Den Broek, T.J., Luque, R.M., Perez- Martinez, P., Katsiki, N., Delgado-Lista, J., Ordovas, J., Van Ommen, B., Camargo, A., Lopez-Miranda, J. 2021. An altered microbiota pattern precedes Type 2 diabetes mellitus development: From the CORDIOPREV study. Journal of Advanced Research. https://doi.org/10.1016/j.jare.2021.05.001.
• Fernandez-Carrion, R., Sorli, J.V., Coltell, O., Pascual, E.C., Ortega- Azorin, C., Barragan, R., Gimenez-Alba, I., Alvarez-Sala, A., Fito, M., Ordovas, J., Corella, D. 2021. Sweet taste preference: Relationships with other tastes, liking for sugary foods and exploratory genome-wide association analysis in subjects with metabolic syndrome. Biomedicines. https://doi.org/10.3390/biomedicines10010079.
• Podadera-Herreros, A., Alcala-Diaz, J.F., Gutierrez-Mariscal, F.M., Jimenez-Torres, J., Cruz-Ares, S., Arenas-De Larriva, A.P., Cardelo, M.P., Torres-Pena, J.D., Luque, R.M., Ordovas, J.M., Delgado-Lista, J., Lopez- Miranda, J., Yubereo-Serrano, E.M. 2022. Long-term consumption of a mediterranean diet or a low-fat diet on kidney function in coronary heart disease patients: The CORDIOPREV randomized controlled trial. Clinical Nutrition. 41(2):552-559. https://doi.org/10.1016/j.clnu.2021.12.041.
• Daimiel, L., Mico, V., Diez-Ricote, L., Ruiz-Valderrey, P., Istas, G., Rodriguez-Mateos, A., Ordovas, J. 2020. Alcoholic and non-alcoholic beer modulate plasma and macrophage microRNAs differently in a pilot intervention in humans with cardiovascular risk. Nutrients. 13(1):69. https://doi.org/10.3390/nu13010069.
• Martinez-Perez, C., Daimiel, L., Climent-Mainar, C., Martinez-Gonzalez, M. A., Salas-Salvado, J., Corella, D., Schroder, H., Martinez, J., Alonso- Gomez, A.M., Warnberg, J., Vioque, J., Romaguera, D., Lopez-Miranda, J., Estruch, R., Tinahones, F.J., Lapetra, J., Serra-Majem, L., Bueno- Cavanillas, A., Tur, J.A., Sanchez, V.M., Pinto, X., Delgado-Rodriguez, M. A., Matia-Martin, P., Vidal, J., Vazquez, C., Ros, E., Basterra, J., Babio, N., Guillem-Saiz, P., Zomeno, M., Abete, I., Vaquero-Luna, J., Baron- Lopez, F.J., Gonzalez-Palacios, S., Konieczna, J., Garcia-Rios, A., Bernal- Lopez, M., Santos-Lozano, J., Bes-Rastrollo, M., Khoury, N., Saiz, C., Perez-Vega, K.A., Zulet, M., Tojal-Sierra, L., Vazquez Ruiz, Z., Martinez, M.A., Malcampo, M., Ordovas, J.M., San-Cristobal, R. 2022. Integrative development of a short screening questionnaire of highly processed food consumption (sQ-HPF). International Journal of Behavioral Nutrition and Physical Activity. 19(1):6. https://doi.org/10.1186/s12966-021-01240-6.
• Merino, J., Linenberg, I., Bermingham, K.M., Ganesh, S., Bakker, E., Delahanty, L.M., Chan, A.T., Pujol, J.C., Wolf, J., Al Khatib, H., Franks, P.W., Spector, T.D., Ordovas, J.M., Berry, S.E., Valdes, A.M. 2022. Validity of continuous glucose monitoring for categorizing glycemic responses to diet: implications for use in personalized nutrition. American Journal of Clinical Nutrition. https://doi.org/10.1093/ajcn/ nqac026.
• Daimiel, L., Martinez-Gonzalez, M.A., Corella, D., Salas-Salvado, J., Schroder, H., Vioque, J., Romaguera, D., Martinez, J.A., Warnberg, J., Lopez-Miranda, J., Estruch, R., Cano-Ibanez, N., Alonso-Gomez, A.M., Tur, J.A., Tinahones, F.J., Serra-Majem, L., Mico-Perez, R.M., Lapetra, J., Galdon, A., Pinto, X., Vidal, J., Mico, V., Colmenarejo, G., Gaforio, J.J., Matia-Martin, P., Ros, E., Buil-Cosiales, P., Vazquez-Ruiz, Z., Sorli, J. V., Graniel, I.P., Cuenca-Royo, A., Gisbert-Selles, C., Galmes-Panades, A. M., Zulet, M., Garcia-Rios, A., Diaz-Lopez, A., De La Torre, R., Galilea- Zabalza, I., Ordovas, J.M. 2020. Physical fitness and physical activity association with cognitive function and quality of life: baseline cross- sectional analysis of the PREDIMED-Plus trial. Scientific Reports. 10(1) :3472. https://doi.org/10.1038/s41598-020-59458-6.
• Mieko de Meneses Fujii, T., Maintinguer Norde, M., Fisberg, R., Lobo Marchioni, D., Ordovas, J.M., Macedo Rogero, M. 2020. FADS1 and ELOVL2 polymorphisms reveal associations for differences in lipid metabolism in a cross-sectional population-based survey of Brazilian men and women. Nutrition Research. 78:42-49. http://doi.org/10.1016/j.nutres.2020.04.003.
• Westerman, K., Liu, Q., Liu, S., Parnell, L.D., Sebastiani, P., Jacques, P. F., Demeo, D.L., Ordovas, J. 2020. A gene-diet interaction-based score predicts response to dietary fat in the Women's Health Initiative. American Journal of Clinical Nutrition. 111(4):893-902. https://doi.org/10. 1093/ajcn/nqaa037.
• Ordovas, J.M., Berciano, S. 2020. Personalized nutrition and healthy aging. Nutrition Reviews. 78(S3):58-65. https://doi.org/10.1093/nutrit/nuaa102.
• Aronica, L., Ordovas, J.M., Volkov, A., Lamb, J.J., Stone, P., Minich, D.M. , Leary, M., Class, M., Metti, D., Larson, I.A., Contractor, N., Eck, B., Bland, J. 2022. Genetic biomarkers of metabolic detoxification for personalized lifestyle medicine. Nutrients. https://doi.org/10.3390/ nu14040768.
• Jimenez-Lucena, R., Alcala-Diaz, J.F., Roncero-Ramos, I., Lopez-Moreno, J., Camargo, A., Gomez-Delgado, F., Quintana-Navarro, G., Vals-Delgado, C., Rodriguez-Cantalejo, F., Luque, R.M., Delgado-Lista, J., Ordovas, J.M., Perez-Martinez, P., Rangel-Zuniga, O.A., Lopez-Miranda, J. 2020. MiRNAs profile as biomarkers of nutritional therapy for the prevention of type 2 diabetes mellitus: from the CORDIOPREV study. Clinical Nutrition. https:// doi.org/10.1016/j.clnu.2020.06.035.
• Ortiz-Morales, A.M., Alcala-Diaz, J.F., Rangel-Zuniga, O.A., Corina, A., Quintana-Navarro, G., Cardelo, M.P., Yubero-Serrano, E.M., Malagon, M.M., Delgado-Lista, J., Ordovas, J.M., Lopez-Miranda, J., Perez-Martinez, P. 2020. Biological senescence risk score. A practical tool to predict biological senescence status. European Journal of Clinical Investigation. 50(11):e13305. https://doi.org/10.1111/eci.13305.
• Westerman, K., Kelly, J.M., Ordovas, J.M., Booth, S.L., DeMeo, D.F. 2020. Epigenome-wide association study reveals a molecular signature of response to phylloquinone (vitaminK1) supplementation. Epigenetics. 15(8):859-870. https://doi.org/10.1080/15592294.2020.1734714.
• Moreno, V., Areces, F., Ruiz-Vincente, D., Ordovas, J.M., Del Coso, J. 2020. Influence of the ACTN3 R577X genotype on the injury epidemiology of marathon runners. PLoS ONE. 15(1):e0227548. https://doi.org/10.1371/ journal.pone.0227548.
• Cardelo, M.P., Alcala-Diaz, J.F., Gutierrez-Mariscal, F.M., Lopez-Moreno, J., Villasanta-Gonzalez, A., Arenas-De Larriva, A., De La Cruz-Ares, S., Delgado-Lista, J., Rodriguez-Cantalejo, F., Luque, R.M., Ordovas, J., Perez-Martinez, P., Camargo, A., Lopez-Miranda, J. 2022. Diabetes remission is modulated by branched chain amino acids according to the diet consumed: From the CORDIOPREV Study. Molecular Nutrition and Food Research. https://doi.org/10.1002/mnfr.202100652.

Progress 10/01/20 to 09/30/21

Outputs
PROGRESS REPORT Objectives (from AD-416): 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. Approach (from AD-416): 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 was made on the two objectives, both of which fall under National Program 107. The National Program in Human Nutrition is designed to improve the health of all Americans throughout their lifespan. Under Objective 1, our goal is to conduct and analyze dietary intervention studies to validate gene-diet interactions and identify the underlying mechanisms using omic technologies. In support of this objective, and in collaboration with investigators in the U.K. and other U.S. sites, we examined the genetic, metabolic, microbiome, and meal composition/ context contributions to postprandial metabolic responses in the PREDICT (Personalised REsponses to DIetary Composition Trial) 1 Study. This study has enrolled 1,102 twins and unrelated healthy adults in the U.K. and U.S. For this report, we will focus on postprandial responses and microbiome characteristics. Regarding postprandial response, meal-induced metabolic changes trigger an acute inflammatory response, contributing to chronic inflammation and associated diseases. Therefore, we aimed to characterize variability in postprandial inflammatory responses using traditional (IL-6) and novel [glycoprotein acetylation (GlycA)] biomarkers of inflammation and dissect their biological determinants. We measured postprandial glucose, triglyceride (TG), IL-6, and GlycA responses at multiple intervals after sequential mixed-nutrient meals (0 h and 4 h) in PREDICT1 participants. Our results show that the postprandial changes in GlycA and IL-6 concentrations were highly variable between individuals. Peak postprandial TG and glucose concentrations were significantly associated with 6-h GlycA (both P < 0.001) but not with 6-h IL-6 (both P > 0.26). A random forest model revealed that the maximum TG concentration was the strongest postprandial TG predictor of postprandial GlycA. Structural equation modeling showed that visceral fat mass (VFM) and fasting TG were most strongly associated with fasting and postprandial GlycA. Network Mendelian randomization demonstrated a causal link between VFM and fasting GlycA, mediated by fasting TG. Individuals eliciting enhanced GlycA responses had higher predicted cardiovascular disease (CVD) risk than the cohort. In summary, GlycA and its associations with TG metabolism highlight the importance of its modulation in concert with obesity to reduce GlycA and associated low-grade inflammation-related diseases. Our progress related to nutrition-microbiome-health involved PREDICT1 and the Coronary Diet Intervention with Olive Oil and Cardiovascular Prevention (CORDIOPREV) Studies. First, we performed metagenomic sequencing of gut microbiomes from PREDICT1 participants. We found significant associations between microbes and nutrients, foods, food groups, and general dietary indices, driven primarily by the presence and diversity of healthy and plant-based foods. Overall microbiome composition was predictive for a large panel of cardiometabolic markers, including fasting and postprandial glycemic, lipemic and inflammatory indices. The panel of microbes associated with healthy dietary habits overlapped with those associated with favorable cardiometabolic markers, indicating that we can potentially stratify the gut microbiome into generalizable health levels in individuals without clinically manifest disease. Moreover, we investigated the association between dietary type 2 diabetes (T2D) prevention and remission and the microbiome. For this purpose, we examined T2D dietary prevention on all CORDIOPREV patients without T2D at baseline (n=462). The risk of T2D was assessed, after a five-year follow-up, by Cox analysis. Linear discriminant analysis effect size (LEfSe) analysis showed a different baseline gut microbiota in patients who developed T2D consuming low-fat (LF) and Mediterranean (Med) diets. Higher Paraprevotella and lower Gammaproteobacteria and B. uniformis were associated with T2D risk when an LF-diet was consumed. Conversely, higher Saccharibacteria, Betaproteobacteria, and Prevotella were associated with T2D risk when a Med-diet was consumed, suggesting that different interactions between the microbiome and dietary patterns may partially determine T2D incidence. For T2D remission, we included 110 newly diagnosed T2D CORDIOPREV patients. We evaluated whether baseline gut microbiota composition improves the identification of patients undergoing T2D remission while consuming the two dietary models for 5 years. Using LEfSe, we showed that the responder group's gut microbiota was characterized by the Ruminococcus genus of the Lachnospiraceae family. Conversely, base-line gut microbiota in the non-responder group was enriched in the Porphyromonadaceae family and Parabacteroides genus. Therefore, our results reveal a gut microbiota profile associated with T2D remission and provide evidence of a role of the microbiome as a predictive factor for response to diet-induced T2D remission. For Objective 2, we have made substantial progress, specifically in developing computational models for cardiometabolic disease, responsible for decreased longevity and poorer cardiovascular outcomes. Our objective was to define a molecular basis for cardiometabolic stress and assess its association with cardiovascular prognosis. For this purpose, we conducted a prospective observational cohort study in a population-based setting across two centers Boston Puerto Rican Health Study (BPRHS) with a Hispanic population and Atherosclerosis Risk in Communities (ARIC) Study with White and African American populations. The primary exposure was metabolite profiles across both cohorts. Outcomes included associations with multisystem cardiometabolic stress and all-cause mortality and incident CHD (in ARIC). BPRHS participants had higher prevalent cardiometabolic risk relative to those in ARIC. Multisystem cardiometabolic stress was defined for the BPRHS as a composite of hypothalamic-adrenal axis activity, sympathetic activation, blood pressure, dyslipidemia, insulin resistance, visceral adiposity, and inflammation. Two hundred six metabolites associated with cardiometabolic stress were identified in the BPRHS. A parsimonious metabolite-based score was created and associated with cardiometabolic stress in the BPRHS; this score was applied to shared metabolites in the ARIC study, demonstrating significant associations with coronary heart disease (CHD) all-cause mortality after multivariable adjustment at a 30-year horizon. These results underscore the shared molecular pathophysiology of metabolic dysfunction, CVD, and longevity and suggest pathways for modification to improve prognosis across all linked conditions. In addition to biomarkers, clinical practice guidelines recommend assessing subclinical atherosclerosis (SA) using imaging techniques. Therefore, we aimed to develop a machine-learning model based on routine, quantitative and easily measured variables to predict the presence and extent of SA in young, asymptomatic individuals participating in the Progression of Early Subclinical Atherosclerosis [PESA] Study. The Elastic Net (EN) model was built to predict SA extent. The performance of the model for the prediction of SA was compared with traditional CVD risk scores. An external cohort was used for validation. The EN-PESA yielded a c-statistic of 0.88 for the prediction of generalized SA. Moreover, EN- PESA was found to be a predictor of 3-year progression, independent of the baseline extension of SA. In summary, the EN-PESA model uses age, systolic blood pressure, and 10 commonly used blood/urine tests and dietary intake values to identify young, asymptomatic individuals with an increased risk of CVD based on their extension and progression of SA. These individuals are most likely to benefit from dietary and pharmacological treatments. Record of Any Impact of Maximized Teleworking Requirement: During this period of teleworking our team has been meeting remotely weekly and we have continued to write manuscripts and have focused on grant writing. The teleworking requirement had an impact on some aspects of our work including the delayed laboratory analyses of Objectives 1a and 1b and required the suspension of human studies. ACCOMPLISHMENTS 01 Intake of carbohydrates and fats influences the risk of metabolic diseases. The specific role of a signaling mechanism, known as methylation, that controls genes associated with the risk of metabolic diseases such as hypertriglyceridemia, obesity, type 2 diabetes, hypertension, and metabolic syndrome remains unknown. ARS-funded researchers in Boston, Massachusetts, examined whether carbohydrate and fat intakes influenced methylation and the risk of metabolic diseases (i.e., high blood lipids, obesity, type 2 diabetes, and hypertension)in 3,954 people representing Hispanic, Black and White populations. The analyses demonstrated strong associations of a specific methylation marker with metabolic characteristics such as body mass index, triglyceride, glucose, and hypertension in each population and all three populations combined. The results demonstrated that carbohydrate intake induces a specific methylation site that reduces the risk of all metabolic diseases examined. In contrast, fat intake inhibits a specific methylation site and increases the risk of such metabolic diseases. These findings identify how balancing carbohydrate and fat intake can have a causal effect on the risk of metabolic diseases that currently affects millions of Americans.

Impacts
(N/A)

Publications

• Lai, C., Parnell, L.D., Smith, C.E., Guo, T., Sayols-Baixeras, S., Aslibekyan, S., Tiwari, H.K., Irvin, M.R., Bender, C., Fei, D., Hidalgo, B. , Hopkins, P., Absher, D.M., Province, M., Elosua, R., Arnett, D.K., Ordovas, J.M. 2020. Carbohydrate and fat intake associated with risk of metabolic diseases through epigenetics of CPT1A. American Journal of Clinical Nutrition. 112(5):1200⿿1211. https://doi.org/10.1093/ajcn/nqaa233.
• Smith, C., Parnell, L.D., Lai, C., Rush, J.E., Freeman, L.M. 2021. Investigation of diets associated with dilated cardiomyopathy in dogs using foodomics analysis. Scientific Reports. 11:15881. https://doi.org/10. 1038/s41598-021-94464-2.

Progress 10/01/19 to 09/30/20

Outputs
Progress Report Objectives (from AD-416): 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. Approach (from AD-416): 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 was made on the two objectives, both of which fall under National Program 107. The National Program in Human Nutrition is designed to improve the health of all Americans throughout the lifespan. Under Objective 1, our goal is to conduct and analyze dietary intervention studies to validate gene-diet interactions and identify the underlying mechanisms using omic technologies. In support of this objective, and in collaboration with investigators in the U.K. and other U.S. sites, we examined the genetic, metabolic, microbiome, and meal composition/context contributions to postprandial metabolic responses (in clinic and at home) in the PREDICT 1 Study. This study has enrolled 1,102 twins and unrelated healthy adults in the U.K. and U.S. Our analysis reveals large and consistent differences between individuals in blood triglyceride, glucose, and insulin responses to identical meals. Person-specific factors, including gut microbiome, have a greater influence than meal macronutrients; genetic variants have a modest impact on predictions. Modifiable factors such as meal timing were found to have large effects. As predictors of cardiometabolic disease risk, postprandial triglyceride and glucose were more accurate than traditional fasting clinical markers. Moreover, we have developed a machine-learning model that predicts both triglyceride and glycemic responses to food. These findings may be informative for developing personalized diet strategies. For Subobjective 2a, we have examined three distinct populations representing different global genetic ancestries and found that the total amount of carbohydrate and the ratio of total carbohydrate to total fat intake have a direct association with DNA methylation at CPT1A gene. In contrast, total fat intake intake has an inverse association with DNA methylation. In addition, we found supportive evidence that the higher DNA methylation found at the CPT1A gene induced by carbohydrate intake can lower the risk of metabolic conditions and diseases including high triglycerides, obesity, type 2 diabetes, hypertension, and metabolic syndrome. Conversely, the lower DNA methylation at the CPT1A, as promoted with greater fat intake, can raise the risk of these diseases. Our findings on CPT1A methylation suggest that there is a balance between carbohydrate and fat in the diet that can influence the regulation of gene activity, and that such communication between diet and genome has health consequences. In support of Subobjective 2b, we analyzed the regional assignment of human amylase (AMY1) gene, encoding a starch-digesting enzyme, with regard to dietary carbohydrate intake and type 2 diabetes, and the ABCG1 gene, whose protein transports cholesterol byproducts, for epigenetic changes relating to a certain drug class and risk of type 2 diabetes. In addition, we developed specialized software that compares chemical structures to identify natural food chemicals with high potential to mimic commonly used pharmacological compounds. Starchy foods are a major source of dietary carbohydrates and contribute significantly to the energy intake of many Americans. An important carbohydrate- and starch-digesting enzyme is AMY1 amylase in the saliva, which initiates digestion. The number of copies of the AMY1 gene varies greatly among humans. People with a low copy number of AMY1 (fewer than 6 copies) had increased risk of insulin resistance as they aged, illustrating that AMY1 copy number interact with age to affect the risk of type 2 diabetes. Together, these results imply that people with low AMY1 copy numbers might benefit from lower starch intake as they age, in order to reduce the risk of type 2 diabetes. A large number of adults are prescribed cholesterol-lowering medication, most frequently statins, for the prevention or treatment of cardiovascular diseases. An unexpected side effect of these drugs is an increased risk of type 2 diabetes for reasons that are not known. We have examined two cohorts and found a strong association between statin use and DNA methylation (an epigenetic mark) at the ATP Binding Cassette Subfamily G Member 1 (ABCG1) gene. The research showed that statin use is the cause of changes in ABCG1 methylation, and this leads to the observed increased risk of type 2 diabetes. Because there is a scarcity of information on how chemical compounds naturally found in different foods contribute to specific health effects, we built software that could match, when possible, these chemical compounds to pharmacological compounds for which such information is documented. This software uses a machine-learning algorithm to compare chemical structures and then uses the wealth of biological and health information on drugs to assign potential biological effects to chemical compounds naturally found in food. A test case of the software began with the target of common diabetes medications and identified several natural compounds with potentially similar beneficial effects. Using this software can guide researchers in designing specific experiments to test if a food compound and its food source actually function as predicted in alleviating, either wholly or partially, specific conditions of common age-related metabolic diseases. Accomplishments 01 People with specific genes are more likely to gain weight when consuming sugar-sweetened beverages. Consuming sugar-loaded drinks is associated with obesity and obesity-related diseases, but the biological mechanism that connects sugar-sweetened beverage intake to obesity is not completely understood. ARS researchers in Boston, Massachusetts, examined the relationship of biochemical compounds found in the blood of participants in the Boston Puerto Rican Health Study as it related to their intake of sugar sweetened beverages and body mass index (BMI). The scientists identified 28 compounds, many of them implicated in fatty liver, that linked sugar-sweetened beverage intake to obesity. These findings suggest that drinking sugar-sweetened beverages disrupts liver metabolism leading to an increased risk of obesity in persons with specific versions of genes. Reducing consumption of sugar-sweetened beverages would contribute to reducing the risk of obesity and fatty liver disease that currently affects millions of Americans.

Impacts
(N/A)

Publications

• Huang, T., Sun, D., Heianza, Y., Bergholdt, H.K., Gao, M., Fang, Z., Ding, M., Frazier-Wood, A.C., North, K.E., Marouli, E., Graff, M., Smith, C.E., Varbo, A., Lemaitre, R.N., Corella, D., Wang, C.A., Tjonneland, A., Overvad, K., Sorensen, T.I., Feitosa, M.F., Wojczynski, M.K., Kahonen, M., Mikkila, V., Bartz, T.M., Psaty, B.M., Siscovick, D.S., Danning, R.D., Dedoussis, G., Pedersen, O., Hansen, T., Havulinna, A.S., Mannisto, S., Rotter, J.I., Sares-Jaske, L., Allison, M.A., Rich, S.S., Sorli, J.V., Coltell, O., Pennell, C.E., Eastwood, P., Ridker, P.M., Viikari, J., Raitakari, O., Lehtimaki, T., Helminen, M., Wang, Y., Deloukas, P., Knekt, P., Kanerva, N., Kilpelainen, T.O., Province, M.A., Mozaffarian, D., Chasman, D.I., Nordestgaard, B.G., Ellervik, C., Qi, L. 2019. Dairy intake and body composition and cardiometabolic traits among adults: Mendelian randomization analysis of 182041 individuals from 18 studies: Mendelian randomization of dairy consumption working group. Clinical Chemistry. 65(6) :751-760.
• Camargo, A., Jimenez-Lucena, R., Alcala-Diaz, J.F., Rangel-Zuniga, O.A., Garcia-Carpintero, S., Lopez-Moreno, J., Blanco-Rojo, R., Delgado-Lista, J. , Perez-Martinez, P., Van Ommen, B., Malagon, M.M., Ordovas, J.M., Lopez- Miranda, J. 2018. Postprandial endotoxemia may influence the development of type 2 diabetes mellitus: from the CORDIOPREV study. Clinical Nutrition. 38(2):529-538.
• Sandoval-Insausti, H., Blanco-Rojo, R., Graciani, A., Lopez-Garcia, E., Moreno-Franco, B., Laclaustra, M., Donat-Vargas, C., Ordovas, J.M., Rodriguez-Artalejo, F., Guallar-Castillon, P. 2019. Ultra-processed food consumption and incident frailty: a prospective cohort study of older adults. The Journals of Gerontology: Medical Sciences.
• Quintana-Navarro, G.M., Alcala-Diaz, J.F., Lopez-Moreno, J., Perez-Corral, I., Leon-Acuna, A., Torres-Pena, J.D., Rangel-Zuniga, O.A., Arenas De Larriva, A.P., Corina, A., Camargo, A., Yubero-Serrano, E.M., Rodriguez- Cantalejo, F., Garcia-Rios, A., Luque, R.M., Ordovas, J.M., Perez-Martinez, P., Lopez-Miranda, J., Delgado-Lista, J. 2019. Long-term dietary adherence and changes in dietary intake in coronary patients after intervention with a Mediterranean diet or a low-fat diet: the CORDIOPREV randomized trial. European Journal of Nutrition.
• BIRTH-GENE (BIG) Study Working Group. 2019. Association of birth weight with type 2 diabetes and glycemic traits: A Mendelian randomization study. JAMA Network Open. 2(9):e1910915.
• Liu, Y., Smith, C.E., Parnell, L.D., Lee, Y., An, P., Straka, R.J., Tiwari, H.K., Wood, A.C., Kabagame, E.K., Hopkins, P., Province, M., Arnett, D.K., Tucker, K., Ordovas, J.M., Lai, C. 2020. Salivary AMY1 copy number variation modifies age-related type 2 diabetes risk. Clinical Chemistry. 66(5):718-726.
• Zhou, B., Ichikawa, R., Parnell, L.D., Noel, S.E., Zhang, X., Bhupathiraju, S., Smith, C., Tucker, K.L., Ordovas, J.M., Lai, C. 2020. Metabolomic links between sugar-sweetened beverage intake and obesity. Journal of Obesity.
• Gomez-Delgado, F., Alcala-Diaz, J.F., Leon-Acuna, A., Lopez-Moreno, J., Delgado-Lista, J., Gomez-Marin, B., Roncero-Ramos, I., Yubero-Serrano, E.M. , Rangel-Zuniga, O.A., Vals-Delgado, C., Luque, R.M., Ordovas, J.M., Lopez- Miranda, J., Perez-Martinez, P. 2019. Apolipoprotein E genetic variants interact with Mediterranean diet to modulate postprandial hypertriglyceridemia in coronary heart disease patients: CORDIOPREV study. European Journal of Clinical Investigation.
• Coltell, O., Sorli, J.V., Asensio, E.M., Barragan, R., Gonzalez, J.I., Gimenez-Alba, I.M., Zanon-Moreno, V.C., Estruch, R., Ramirez-Sabio, J.B., Pascual, E.C., Ortega-Azorin, C., Ordovas, J.M., Corella, D. 2020. Genome- wide association study for serum omega-3 and omega-6 polyunsaturated fatty acids: exploratory analysis of the sex-specific effects and dietary modulation in Mediterranean subjects with metabolic syndrome. Nutrients. 12(2):310.
• Murphy, A.M., Smith, C.E., Murphy, L.M., Follis, J.L., Tanaka, T., Richardson, K., Noordam, R., Lemaitre, R.N., Kahonen, M., Dupuis, J., Voortman, T., Marouli, E., Mook-Kanamori, D.O., Raitakari, O.T., Hong, J., Dehghan, A., Dedoussis, G., De Mutsert, R., Lehtimaki, T., Liu, C., Rivadeneira, F., Deloukas, P., Mikkila, V., Meigs, J.B., Uitterlinden, A., Ikram, M.A., Franco, O.H., Hughes, M., O'Gaora, P., Ordovas, J.M., Roche, H.M. 2019. Potential interplay between dietary saturated fats and genetic variants of the NLRP3 inflammasome to modulate insulin resistance and diabetes risk: insights from a meta-analysis of 19 005 individuals. Molecular Nutrition and Food Research. 63(22):1900226.
• Blanco-Rojo, R., Sandoval-Insausti, H., Lopez-Garcia, E., Graciani, A., Ordovas, J.M., Banegas, J.R., Rodriguez-Artalejo, F., Guallar-Castillon, P. 2019. Consumption of ultra-processed foods and mortality: a national prospective cohort in Spain. Mayo Clinic Proceedings. 94(11):2178-2188.
• Ruiz-Moreno, C., Lara, B., Salinero, J.J., Brito De Souxa, D., Ordovas, J. M., Del Coso, J. 2020. Time course of tolerance to adverse effects associated with the ingestion of a moderate dose of caffeine. European Journal of Nutrition.
• Ortega-Azorin, C., Coltell, O., Asensio, E.M., Sorli, J.V., Gonzalez, J.I., Portoles, O., Saiz, C., Estruch, R., Ramirez-Sabio, J.B., Perez-Fidalgo, A., Ordovas, J.M., Corella, D. 2019. Candidate gene and genome-wide association studies for circulating leptin levels reveal population and sex-specific association in high cardiovascular Mediterranean subjects. Nutrients. 11(11):2751.
• Pozuelo-Sanchez, I., Villasanta-Gonzalez, A., Alcala-Diaz, J.F., Vals- Delgado, C., Leon-Acuna, A., Gonzalez-Requero, A., Yubero-Serrano, E.M., Luque, R.M., Caballero-Villarraso, J., Quesada, I., Ordovas, J.M., Perez- Martinez, P., Roncero-Ramos, I., Lopez-Miranda, J. 2020. Postprandial lipemia modulates pancreatic alpha-cell function in the prediction of type 2 diabetes development: the CORDIOPREV study. Journal of Agricultural and Food Chemistry. 68(5):1266-1275.
• Westerman, K., Sebastiani, P., Jacques, P., Liu, S., Demeo, D., Ordovas, J. M. 2019. DNA methylation modules associate with incident cardiovascular disease and cumulative risk factor exposure. Journal of Clinical Epigenetics. 11:142.
• Sotos-Prieto, M., Smith, C.E., Lai, C., Tucker, K.L., Ordovas, J.M., Mattei, J. 2019. Mediterranean diet adherence modulates anthropometric measures by TCF7L2 genotypes among Puerto Rican adults. Journal of Nutrition. 150(1):167-175.
• Westerman, K., Harrington, S.M., Ordovas, J.M., Parnell, L.D. 2020. PhyteByte: Identification of foods containing compounds with specific pharmacological properties. BMC Bioinformatics.
• Leon-Acuna, A., Torres-Pena, J.D., Alcala-Diaz, J.F., Vals-Delgado, C., Roncero-Ramos, I., Yubero-Serrano, E., Tinahones, F.J., Castro Clerico, M., Delgado-Lista, J., Ordovas, J.M., Lopez-Miranda, J., Perez-Martinez, P. 2019. Lifestyle factors modulate postprandial hypertriglyceridemia: from the CORDIOPREV study. Atherosclerosis. 290:118-124.