Search

Your search keyword '"Hu, Frank"' showing total 7,300 results
Start Over Author "Hu, Frank"
7,300 results on '"Hu, Frank"'

1. Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning

Author

Hu, Frank, Chen, Michael S., Rotskoff, Grant M., Kanan, Matthew W., and Markland, Thomas E.

Subjects
Physics - Chemical Physics, Computer Science - Machine Learning
Abstract

Rapid determination of molecular structures can greatly accelerate workflows across many chemical disciplines. However, elucidating structure using only one-dimensional (1D) NMR spectra, the most readily accessible data, remains an extremely challenging problem because of the combinatorial explosion of the number of possible molecules as the number of constituent atoms is increased. Here, we introduce a multitask machine learning framework that predicts the molecular structure (formula and connectivity) of an unknown compound solely based on its 1D 1H and/or 13C NMR spectra. First, we show how a transformer architecture can be constructed to efficiently solve the task, traditionally performed by chemists, of assembling large numbers of molecular fragments into molecular structures. Integrating this capability with a convolutional neural network (CNN), we build an end-to-end model for predicting structure from spectra that is fast and accurate. We demonstrate the effectiveness of this framework on molecules with up to 19 heavy (non-hydrogen) atoms, a size for which there are trillions of possible structures. Without relying on any prior chemical knowledge such as the molecular formula, we show that our approach predicts the exact molecule 69.6% of the time within the first 15 predictions, reducing the search space by up to 11 orders of magnitude.

Published
2024

2. Energy Rank Alignment: Using Preference Optimization to Search Chemical Space at Scale

Author

Chennakesavalu, Shriram, Hu, Frank, Ibarraran, Sebastian, and Rotskoff, Grant M.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Physics - Chemical Physics, Quantitative Biology - Quantitative Methods
Abstract

Searching through chemical space is an exceptionally challenging problem because the number of possible molecules grows combinatorially with the number of atoms. Large, autoregressive models trained on databases of chemical compounds have yielded powerful generators, but we still lack robust strategies for generating molecules with desired properties. This molecular search problem closely resembles the "alignment" problem for large language models, though for many chemical tasks we have a specific and easily evaluable reward function. Here, we introduce an algorithm called energy rank alignment (ERA) that leverages an explicit reward function to produce a gradient-based objective that we use to optimize autoregressive policies. We show theoretically that this algorithm is closely related to proximal policy optimization (PPO) and direct preference optimization (DPO), but has a minimizer that converges to an ideal Gibbs-Boltzmann distribution with the reward playing the role of an energy function. Furthermore, this algorithm is highly scalable, does not require reinforcement learning, and performs well relative to DPO when the number of preference observations per pairing is small. We deploy this approach to align molecular transformers to generate molecules with externally specified properties and find that it does so robustly, searching through diverse parts of chemical space. While our focus here is on chemical search, we also obtain excellent results on an AI supervised task for LLM alignment, showing that the method is scalable and general.

Published
2024

3. Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes

Author

Mei, Zhendong, Wang, Fenglei, Bhosle, Amrisha, Dong, Danyue, Mehta, Raaj, Ghazi, Andrew, Zhang, Yancong, Liu, Yuxi, Rinott, Ehud, Ma, Siyuan, Rimm, Eric B., Daviglus, Martha, Willett, Walter C., Knight, Rob, Hu, Frank B., Qi, Qibin, Chan, Andrew T., Burk, Robert D., Stampfer, Meir J., Shai, Iris, Kaplan, Robert C., Huttenhower, Curtis, and Wang, Dong D.

Published
2024
Full Text
View/download PDF

5. Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Author

Kentistou, Katherine A., Kaisinger, Lena R., Stankovic, Stasa, Vaudel, Marc, Mendes de Oliveira, Edson, Messina, Andrea, Walters, Robin G., Liu, Xiaoxi, Busch, Alexander S., Helgason, Hannes, Thompson, Deborah J., Santoni, Federico, Petricek, Konstantin M., Zouaghi, Yassine, Huang-Doran, Isabel, Gudbjartsson, Daniel F., Bratland, Eirik, Lin, Kuang, Gardner, Eugene J., Zhao, Yajie, Jia, Raina Y., Terao, Chikashi, Riggan, Marjorie J., Bolla, Manjeet K., Yazdanpanah, Mojgan, Yazdanpanah, Nahid, Bradfield, Jonathan P., Broer, Linda, Campbell, Archie, Chasman, Daniel I., Cousminer, Diana L., Franceschini, Nora, Franke, Lude H., Girotto, Giorgia, He, Chunyan, Järvelin, Marjo-Riitta, Joshi, Peter K., Kamatani, Yoichiro, Karlsson, Robert, Luan, Jian’an, Lunetta, Kathryn L., Mägi, Reedik, Mangino, Massimo, Medland, Sarah E., Meisinger, Christa, Noordam, Raymond, Nutile, Teresa, Concas, Maria Pina, Polašek, Ozren, Porcu, Eleonora, Ring, Susan M., Sala, Cinzia, Smith, Albert V., Tanaka, Toshiko, van der Most, Peter J., Vitart, Veronique, Wang, Carol A., Willemsen, Gonneke, Zygmunt, Marek, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Antoniou, Antonis C., Auer, Paul L., Barnes, Catriona L. K., Beckmann, Matthias W., Berrington de Gonzalez, Amy, Bogdanova, Natalia V., Bojesen, Stig E., Brenner, Hermann, Buring, Julie E., Canzian, Federico, Chang-Claude, Jenny, Couch, Fergus J., Cox, Angela, Crisponi, Laura, Czene, Kamila, Daly, Mary B., Demerath, Ellen W., Dennis, Joe, Devilee, Peter, De Vivo, Immaculata, Dörk, Thilo, Dunning, Alison M., Dwek, Miriam, Eriksson, Johan G., Fasching, Peter A., Fernandez-Rhodes, Lindsay, Ferreli, Liana, Fletcher, Olivia, Gago-Dominguez, Manuela, García-Closas, Montserrat, García-Sáenz, José A., González-Neira, Anna, Grallert, Harald, Guénel, Pascal, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hakonarson, Hakon, Hart, Roger J., Hickey, Martha, Hooning, Maartje J., Hoppe, Reiner, Hopper, John L., Hottenga, Jouke-Jan, Hu, Frank B., Huebner, Hanna, Hunter, David J., Jernström, Helena, John, Esther M., Karasik, David, Khusnutdinova, Elza K., Kristensen, Vessela N., Lacey, James V., Lambrechts, Diether, Launer, Lenore J., Lind, Penelope A., Lindblom, Annika, Magnusson, Patrik K. E., Mannermaa, Arto, McCarthy, Mark I., Meitinger, Thomas, Menni, Cristina, Michailidou, Kyriaki, Millwood, Iona Y., Milne, Roger L., Montgomery, Grant W., Nevanlinna, Heli, Nolte, Ilja M., Nyholt, Dale R., Obi, Nadia, O’Brien, Katie M., Offit, Kenneth, Oldehinkel, Albertine J., Ostrowski, Sisse R., Palotie, Aarno, Pedersen, Ole B., Peters, Annette, Pianigiani, Giulia, Plaseska-Karanfilska, Dijana, Pouta, Anneli, Pozarickij, Alfred, Radice, Paolo, Rennert, Gad, Rosendaal, Frits R., Ruggiero, Daniela, Saloustros, Emmanouil, Sandler, Dale P., Schipf, Sabine, Schmidt, Carsten O., Schmidt, Marjanka K., Small, Kerrin, Spedicati, Beatrice, Stampfer, Meir, Stone, Jennifer, Tamimi, Rulla M., Teras, Lauren R., Tikkanen, Emmi, Turman, Constance, Vachon, Celine M., Wang, Qin, Winqvist, Robert, Wolk, Alicja, Zemel, Babette S., Zheng, Wei, van Dijk, Ko W., Alizadeh, Behrooz Z., Bandinelli, Stefania, Boerwinkle, Eric, Boomsma, Dorret I., Ciullo, Marina, Chenevix-Trench, Georgia, Cucca, Francesco, Esko, Tõnu, Gieger, Christian, Grant, Struan F. A., Gudnason, Vilmundur, Hayward, Caroline, Kolčić, Ivana, Kraft, Peter, Lawlor, Deborah A., Martin, Nicholas G., Nøhr, Ellen A., Pedersen, Nancy L., Pennell, Craig E., Ridker, Paul M., Robino, Antonietta, Snieder, Harold, Sovio, Ulla, Spector, Tim D., Stöckl, Doris, Sudlow, Cathie, Timpson, Nic J., Toniolo, Daniela, Uitterlinden, André, Ulivi, Sheila, Völzke, Henry, Wareham, Nicholas J., Widen, Elisabeth, Wilson, James F., Pharoah, Paul D. P., Li, Liming, Easton, Douglas F., Njølstad, Pål R., Sulem, Patrick, Murabito, Joanne M., Murray, Anna, Manousaki, Despoina, Juul, Anders, Erikstrup, Christian, Stefansson, Kari, Horikoshi, Momoko, Chen, Zhengming, Farooqi, I. Sadaf, Pitteloud, Nelly, Johansson, Stefan, Day, Felix R., Perry, John R. B., and Ong, Ken K.

Published
2024
Full Text
View/download PDF

6. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Author

Eastman, Peter, Galvelis, Raimondas, Peláez, Raúl P., Abreu, Charlles R. A., Farr, Stephen E., Gallicchio, Emilio, Gorenko, Anton, Henry, Michael M., Hu, Frank, Huang, Jing, Krämer, Andreas, Michel, Julien, Mitchell, Joshua A., Pande, Vijay S., Rodrigues, João PGLM, Rodriguez-Guerra, Jaime, Simmonett, Andrew C., Swails, Jason, Zhang, Ivy, Chodera, John D., De Fabritiis, Gianni, and Markland, Thomas E.

Subjects
Physics - Chemical Physics, Computer Science - Machine Learning, J.2, J.3
Abstract

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost., Comment: 15 pages, 4 figures

Published
2023

7. Discovery and validation of plasma, saliva and multi-fluid plasma–saliva metabolomic scores predicting insulin resistance and diabetes progression or regression among Puerto Rican adults

Author

Haslam, Danielle E., Liang, Liming, Guo, Kai, Martínez-Lozano, Marijulie, Pérez, Cynthia M., Lee, Chih-Hao, Morou-Bermudez, Evangelia, Clish, Clary, Wong, David T. W., Manson, JoAnn E., Hu, Frank B., Stampfer, Meir J., Joshipura, Kaumudi, and Bhupathiraju, Shilpa N.

Published
2024
Full Text
View/download PDF

8. Adaptive nonequilibrium design of actin-based metamaterials: fundamental and practical limits of control

Author

Chennakesavalu, Shriram, Manikandan, Sreekanth K., Hu, Frank, and Rotskoff, Grant M.

Subjects
Condensed Matter - Statistical Mechanics
Abstract

The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials and their properties. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no systematic understanding of either how to parameterize this control or how \emph{controllable} a given material can be. Here, we demonstrate that branched actin networks can be encoded with \textit{metamaterial} properties by dynamically controlling the applied force under which they grow, and that the protocols can be selected using multi-task reinforcement learning. These actin networks have tunable responses over a large dynamic range depending on the chosen external protocol, providing a pathway to encoding ``memory'' within these structures. Interestingly, we show that encoding memory requires dissipation and the rate of encoding is constrained by the flow of entropy -- both physical and information theoretical. Taken together, these results emphasize the utility and necessity of nonequilibrium control for designing self-assembled nanostructures.

Published
2023

9. Publisher Correction: Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Author

Kentistou, Katherine A., Kaisinger, Lena R., Stankovic, Stasa, Vaudel, Marc, Mendes de Oliveira, Edson, Messina, Andrea, Walters, Robin G., Liu, Xiaoxi, Busch, Alexander S., Helgason, Hannes, Thompson, Deborah J., Santoni, Federico, Petricek, Konstantin M., Zouaghi, Yassine, Huang-Doran, Isabel, Gudbjartsson, Daniel F., Bratland, Eirik, Lin, Kuang, Gardner, Eugene J., Zhao, Yajie, Jia, Raina Y., Terao, Chikashi, Riggan, Marjorie J., Bolla, Manjeet K., Yazdanpanah, Mojgan, Yazdanpanah, Nahid, Bradfield, Jonathan P., Broer, Linda, Campbell, Archie, Chasman, Daniel I., Cousminer, Diana L., Franceschini, Nora, Franke, Lude H., Girotto, Giorgia, He, Chunyan, Järvelin, Marjo-Riitta, Joshi, Peter K., Kamatani, Yoichiro, Karlsson, Robert, Luan, Jian’an, Lunetta, Kathryn L., Mägi, Reedik, Mangino, Massimo, Medland, Sarah E., Meisinger, Christa, Noordam, Raymond, Nutile, Teresa, Concas, Maria Pina, Polašek, Ozren, Porcu, Eleonora, Ring, Susan M., Sala, Cinzia, Smith, Albert V., Tanaka, Toshiko, van der Most, Peter J., Vitart, Veronique, Wang, Carol A., Willemsen, Gonneke, Zygmunt, Marek, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Antoniou, Antonis C., Auer, Paul L., Barnes, Catriona L. K., Beckmann, Matthias W., Berrington de Gonzalez, Amy, Bogdanova, Natalia V., Bojesen, Stig E., Brenner, Hermann, Buring, Julie E., Canzian, Federico, Chang-Claude, Jenny, Couch, Fergus J., Cox, Angela, Crisponi, Laura, Czene, Kamila, Daly, Mary B., Demerath, Ellen W., Dennis, Joe, Devilee, Peter, De Vivo, Immaculata, Dörk, Thilo, Dunning, Alison M., Dwek, Miriam, Eriksson, Johan G., Fasching, Peter A., Fernandez-Rhodes, Lindsay, Ferreli, Liana, Fletcher, Olivia, Gago-Dominguez, Manuela, García-Closas, Montserrat, García-Sáenz, José A., González-Neira, Anna, Grallert, Harald, Guénel, Pascal, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hakonarson, Hakon, Hart, Roger J., Hickey, Martha, Hooning, Maartje J., Hoppe, Reiner, Hopper, John L., Hottenga, Jouke-Jan, Hu, Frank B., Huebner, Hanna, Hunter, David J., Jernström, Helena, John, Esther M., Karasik, David, Khusnutdinova, Elza K., Kristensen, Vessela N., Lacey, James V., Lambrechts, Diether, Launer, Lenore J., Lind, Penelope A., Lindblom, Annika, Magnusson, Patrik K. E., Mannermaa, Arto, McCarthy, Mark I., Meitinger, Thomas, Menni, Cristina, Michailidou, Kyriaki, Millwood, Iona Y., Milne, Roger L., Montgomery, Grant W., Nevanlinna, Heli, Nolte, Ilja M., Nyholt, Dale R., Obi, Nadia, O’Brien, Katie M., Offit, Kenneth, Oldehinkel, Albertine J., Ostrowski, Sisse R., Palotie, Aarno, Pedersen, Ole B., Peters, Annette, Pianigiani, Giulia, Plaseska-Karanfilska, Dijana, Pouta, Anneli, Pozarickij, Alfred, Radice, Paolo, Rennert, Gad, Rosendaal, Frits R., Ruggiero, Daniela, Saloustros, Emmanouil, Sandler, Dale P., Schipf, Sabine, Schmidt, Carsten O., Schmidt, Marjanka K., Small, Kerrin, Spedicati, Beatrice, Stampfer, Meir, Stone, Jennifer, Tamimi, Rulla M., Teras, Lauren R., Tikkanen, Emmi, Turman, Constance, Vachon, Celine M., Wang, Qin, Winqvist, Robert, Wolk, Alicja, Zemel, Babette S., Zheng, Wei, van Dijk, Ko W., Alizadeh, Behrooz Z., Bandinelli, Stefania, Boerwinkle, Eric, Boomsma, Dorret I., Ciullo, Marina, Chenevix-Trench, Georgia, Cucca, Francesco, Esko, Tõnu, Gieger, Christian, Grant, Struan F. A., Gudnason, Vilmundur, Hayward, Caroline, Kolčić, Ivana, Kraft, Peter, Lawlor, Deborah A., Martin, Nicholas G., Nøhr, Ellen A., Pedersen, Nancy L., Pennell, Craig E., Ridker, Paul M., Robino, Antonietta, Snieder, Harold, Sovio, Ulla, Spector, Tim D., Stöckl, Doris, Sudlow, Cathie, Timpson, Nic J., Toniolo, Daniela, Uitterlinden, André, Ulivi, Sheila, Völzke, Henry, Wareham, Nicholas J., Widen, Elisabeth, Wilson, James F., Pharoah, Paul D. P., Li, Liming, Easton, Douglas F., Njølstad, Pål R., Sulem, Patrick, Murabito, Joanne M., Murray, Anna, Manousaki, Despoina, Juul, Anders, Erikstrup, Christian, Stefansson, Kari, Horikoshi, Momoko, Chen, Zhengming, Farooqi, I. Sadaf, Pitteloud, Nelly, Johansson, Stefan, Day, Felix R., Perry, John R. B., and Ong, Ken K.

Published
2024
Full Text
View/download PDF

10. Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease

Author

Margara-Escudero, Hernando J., Paz-Graniel, Indira, García-Gavilán, Jesús, Ruiz-Canela, Miguel, Sun, Qi, Clish, Clary B., Toledo, Estefania, Corella, Dolores, Estruch, Ramón, Ros, Emilio, Castañer, Olga, Arós, Fernando, Fiol, Miquel, Guasch-Ferré, Marta, Lapetra, José, Razquin, Cristina, Dennis, Courtney, Deik, Amy, Li, Jun, Gómez-Gracia, Enrique, Babio, Nancy, Martínez-González, Miguel A., Hu, Frank B., and Salas-Salvadó, Jordi

Published
2024
Full Text
View/download PDF

13. Semiempirical Hamiltonians learned from data can have accuracy comparable to Density Functional Theory

Author

Hu, Frank, He, Francis, and Yaron, David J.

Subjects
Physics - Chemical Physics
Abstract

Quantum chemistry provides chemists with invaluable information, but the high computational cost limits the size and type of systems that can be studied. Machine learning (ML) has emerged as a means to dramatically lower cost while maintaining high accuracy. However, ML models often sacrifice interpretability by using components, such as the artificial neural networks of deep learning, that function as black boxes. These components impart the flexibility needed to learn from large volumes of data but make it difficult to gain insight into the physical or chemical basis for the predictions. Here, we demonstrate that semiempirical quantum chemical (SEQC) models can learn from large volumes of data without sacrificing interpretability. The SEQC model is that of Density Functional based Tight Binding (DFTB) with fixed atomic orbital energies and interactions that are one-dimensional functions of interatomic distance. This model is trained to ab initio data in a manner that is analogous to that used to train deep learning models. Using benchmarks that reflect the accuracy of the training data, we show that the resulting model maintains a physically reasonable functional form while achieving an accuracy, relative to coupled cluster energies with a complete basis set extrapolation (CCSD(T)*/CBS), that is comparable to that of density functional theory (DFT). This suggests that trained SEQC models can achieve low computational cost and high accuracy without sacrificing interpretability. Use of a physically-motivated model form also substantially reduces the amount of ab initio data needed to train the model compared to that required for deep learning models.

Published
2022

14. Healthy dietary patterns are associated with the gut microbiome in the Hispanic Community Health Study/Study of Latinos.

Author

Peters, Brandilyn, Xing, Jiaqian, Chen, Guo-Chong, Usyk, Mykhaylo, Wang, Zheng, McClain, Amanda, Thyagarajan, Bharat, Daviglus, Martha, Sotres-Alvarez, Daniela, Hu, Frank, Burk, Robert, Kaplan, Robert, Qi, Qibin, and Knight, Robin

Subjects
Hispanic, Latino, Mediterranean diet, cardiometabolic health, diet, dietary pattern, gut microbiome, healthy eating index, Humans, Cardiovascular Diseases, Cross-Sectional Studies, Gastrointestinal Microbiome, Hispanic or Latino, Public Health, Diet, Healthy
Abstract

BACKGROUND: Dietary patterns high in healthy minimally processed plant foods play an important role in modulating the gut microbiome and promoting cardiometabolic health. Little is known on the diet-gut microbiome relationship in US Hispanics/Latinos, who have a high burden of obesity and diabetes. OBJECTIVE: In a cross-sectional analysis, we sought to examine the relationships of 3 healthy dietary patterns-the alternate Mediterranean diet (aMED), the Healthy Eating Index (HEI)-2015, and the healthful plant-based diet index (hPDI)-with the gut microbiome in US Hispanic/Latino adults, and to study the association of diet-related species with cardiometabolic traits. METHODS: The Hispanic Community Health Study/Study of Latinos is a multi-site community-based cohort. At baseline (2008-2011), diet was assessed by using 2, 24-hour recalls. Shotgun sequencing was performed on stool samples collected in 2014-17 (n = 2444). Analysis of Compositions of Microbiomes 2 (ANCOM2) was used to identify the associations of dietary pattern scores with gut microbiome species and functions, adjusting for sociodemographic, behavioral, and clinical covariates. RESULTS: Better diet quality according to multiple healthy dietary patterns was associated with a higher abundance of species from class Clostridia, including [Eubacterium] eligens, Butyrivibrio crossotus, and Lachnospiraceae bacterium TF01-11, but functions related to better diet quality differed for the dietary patterns (e.g., aMED with pyruvate:ferredoxin oxidoreductase, hPDI with L-arabinose/lactose transport). Poorer diet quality was associated with a higher abundance of Acidaminococcus intestini and with functions of manganese/iron transport, adhesin protein transport, and nitrate reduction. Some healthy diet pattern-enriched Clostridia species were related to more favorable cardiometabolic traits such as lower triglycerides and waist-to-hip ratio. CONCLUSIONS: Healthy dietary patterns in this population are associated with a higher abundance of fiber-fermenting Clostridia species in the gut microbiome, consistent with previous studies in other racial/ethnic groups. Gut microbiota may be involved in the beneficial effect of higher diet quality on cardiometabolic disease risk.

Published
2023

15. A healthy plant–based diet is favorably associated with cardiometabolic risk factors among participants of South Asian ancestry

Author

Bhupathiraju, Shilpa N, Sawicki, Caleigh M, Goon, Shatabdi, Gujral, Unjali P, Hu, Frank B, Kandula, Namratha R, and Kanaya, Alka M

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Health Disparities, Dietary Supplements, Obesity, Cardiovascular, Nutrition, Clinical Research, Diabetes, Prevention, Metabolic and endocrine, Oral and gastrointestinal, Cancer, Stroke, Adiponectin, Atherosclerosis, Cardiometabolic Risk Factors, Cholesterol, LDL, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Diet, Diet, Vegetarian, Glucose, Glycated Hemoglobin, Humans, Risk Factors, United States, plant-based diets, cardiovascular disease, diabetes, South Asians, Asian Indians, cardiometabolic, diet, epidemiology, Engineering, Medical and Health Sciences, Nutrition & Dietetics, Clinical sciences, Nutrition and dietetics
Abstract

BackgroundPlant-based diets are recommended for chronic disease prevention, yet there has been little focus on plant-based diet quality among participants of South Asian ancestry who consume a predominantly plant-based diet.ObjectivesWe evaluated cross-sectional and prospective associations between plant-based diet quality and cardiometabolic risks among participants of South Asian ancestry who are living in the United States.MethodsWe included 891 participants of South Asian ancestry who completed the baseline visit in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study. The prospective analysis included 735 participants who completed exam 2 (∼5 years after baseline). The plant-based diet quality was assessed using 3 indices: an overall plant-based diet index (PDI) that summarizes the consumption of plant foods, a healthy PDI (hPDI) that measures consumption of healthy plant foods, and an unhealthy PDI (uPDI) that reflects consumption of less healthy plant foods.ResultsAt baseline, the PDI score was inversely associated with fasting glucose. We observed inverse associations between PDI and hPDI scores and HOMA-IR, LDL cholesterol, weight, and BMI (all P values

Published
2022

16. Correction: Discovery and validation of plasma, saliva and multi-fluid plasma–saliva metabolomic scores predicting insulin resistance and diabetes progression or regression among Puerto Rican adults

Author

Haslam, Danielle E., Liang, Liming, Guo, Kai, Martínez-Lozano, Marijulie, Pérez, Cynthia M., Lee, Chih-Hao, Morou-Bermudez, Evangelia, Clish, Clary, Wong, David T. W., Manson, JoAnn E., Hu, Frank B., Stampfer, Meir J., Joshipura, Kaumudi, and Bhupathiraju, Shilpa N.

Published
2024
Full Text
View/download PDF

23. Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies

Author

Qi, Qibin, Li, Jun, Yu, Bing, Moon, Jee-Young, Chai, Jin C, Merino, Jordi, Hu, Jie, Ruiz-Canela, Miguel, Rebholz, Casey, Wang, Zheng, Usyk, Mykhaylo, Chen, Guo-Chong, Porneala, Bianca C, Wang, Wenshuang, Nguyen, Ngoc Quynh, Feofanova, Elena V, Grove, Megan L, Wang, Thomas J, Gerszten, Robert E, Dupuis, Josée, Salas-Salvadó, Jordi, Bao, Wei, Perkins, David L, Daviglus, Martha L, Thyagarajan, Bharat, Cai, Jianwen, Wang, Tao, Manson, JoAnn E, Martínez-González, Miguel A, Selvin, Elizabeth, Rexrode, Kathryn M, Clish, Clary B, Hu, Frank B, Meigs, James B, Knight, Rob, Burk, Robert D, Boerwinkle, Eric, and Kaplan, Robert C

Subjects
Obesity, Human Genome, Prevention, Genetics, Nutrition, Diabetes, 2.2 Factors relating to the physical environment, Aetiology, Metabolic and endocrine, Oral and gastrointestinal, Bacteria, Cohort Studies, Diabetes Mellitus, Type 2, Diet, Gastrointestinal Microbiome, Humans, Kynurenine, Lactase, Tryptophan, diabetes mellitus, dietary factors, genetics, Clinical Sciences, Paediatrics and Reproductive Medicine, Gastroenterology & Hepatology
Abstract

ObjectiveTryptophan can be catabolised to various metabolites through host kynurenine and microbial indole pathways. We aimed to examine relationships of host and microbial tryptophan metabolites with incident type 2 diabetes (T2D), host genetics, diet and gut microbiota.MethodWe analysed associations between circulating levels of 11 tryptophan metabolites and incident T2D in 9180 participants of diverse racial/ethnic backgrounds from five cohorts. We examined host genome-wide variants, dietary intake and gut microbiome associated with these metabolites.ResultsTryptophan, four kynurenine-pathway metabolites (kynurenine, kynurenate, xanthurenate and quinolinate) and indolelactate were positively associated with T2D risk, while indolepropionate was inversely associated with T2D risk. We identified multiple host genetic variants, dietary factors, gut bacteria and their potential interplay associated with these T2D-relaetd metabolites. Intakes of fibre-rich foods, but not protein/tryptophan-rich foods, were the dietary factors most strongly associated with tryptophan metabolites. The fibre-indolepropionate association was partially explained by indolepropionate-associated gut bacteria, mostly fibre-using Firmicutes. We identified a novel association between a host functional LCT variant (determining lactase persistence) and serum indolepropionate, which might be related to a host gene-diet interaction on gut Bifidobacterium, a probiotic bacterium significantly associated with indolepropionate independent of other fibre-related bacteria. Higher milk intake was associated with higher levels of gut Bifidobacterium and serum indolepropionate only among genetically lactase non-persistent individuals.ConclusionHigher milk intake among lactase non-persistent individuals, and higher fibre intake were associated with a favourable profile of circulating tryptophan metabolites for T2D, potentially through the host-microbial cross-talk shifting tryptophan metabolism toward gut microbial indolepropionate production.

Published
2022

24. Trans Fatty Acid Biomarkers and Incident Type 2 Diabetes: Pooled Analysis of 12 Prospective Cohort Studies in the Fatty Acids and Outcomes Research Consortium (FORCE).

Author

Lai, Heidi TM, Imamura, Fumiaki, Korat, Andres V Ardisson, Murphy, Rachel A, Tintle, Nathan, Bassett, Julie K, Chen, Jiaying, Kröger, Janine, Chien, Kuo-Liong, Senn, Mackenzie, Wood, Alexis C, Forouhi, Nita G, Schulze, Matthias B, Harris, William S, Vasan, Ramachandran S, Hu, Frank, Giles, Graham G, Hodge, Allison, Djousse, Luc, Brouwer, Ingeborg A, Qian, Frank, Sun, Qi, Wu, Jason HY, Marklund, Matti, Lemaitre, Rozenn N, Siscovick, David S, Fretts, Amanda M, Shadyab, Aladdin H, Manson, JoAnn E, Howard, Barbara V, Robinson, Jennifer G, Wallace, Robert B, Wareham, Nick J, Chen, Yii-Der Ida, Rotter, Jerome I, Tsai, Michael Y, Micha, Renata, and Mozaffarian, Dariush

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Diabetes, Clinical Research, Metabolic and endocrine, Adolescent, Adult, Biomarkers, Cohort Studies, Diabetes Mellitus, Type 2, Fatty Acids, Humans, Outcome Assessment, Health Care, Prospective Studies, Risk Factors, Trans Fatty Acids, Fatty Acids and Outcomes Research Consortium, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

ObjectiveTrans fatty acids (TFAs) have harmful biologic effects that could increase the risk of type 2 diabetes (T2D), but evidence remains uncertain. We aimed to investigate the prospective associations of TFA biomarkers and T2D by conducting an individual participant-level pooled analysis.Research design and methodsWe included data from an international consortium of 12 prospective cohorts and nested case-control studies from six nations. TFA biomarkers were measured in blood collected between 1990 and 2008 from 25,126 participants aged ≥18 years without prevalent diabetes. Each cohort conducted de novo harmonized analyses using a prespecified protocol, and findings were pooled using inverse-variance weighted meta-analysis. Heterogeneity was explored by prespecified between-study and within-study characteristics.ResultsDuring a mean follow-up of 13.5 years, 2,843 cases of incident T2D were identified. In multivariable-adjusted pooled analyses, no significant associations with T2D were identified for trans/trans-18:2, relative risk (RR) 1.09 (95% CI 0.94-1.25); cis/trans-18:2, 0.89 (0.73-1.07); and trans/cis-18:2, 0.87 (0.73-1.03). Trans-16:1n-9, total trans-18:1, and total trans-18:2 were inversely associated with T2D (RR 0.81 [95% CI 0.67-0.99], 0.86 [0.75-0.99], and 0.84 [0.74-0.96], respectively). Findings were not significantly different according to prespecified sources of potential heterogeneity (each P ≥ 0.1).ConclusionsCirculating individual trans-18:2 TFA biomarkers were not associated with risk of T2D, while trans-16:1n-9, total trans-18:1, and total trans-18:2 were inversely associated. Findings may reflect the influence of mixed TFA sources (industrial vs. natural ruminant), a general decline in TFA exposure due to policy changes during this period, or the relatively limited range of TFA levels.

Published
2022

25. Knowledge Gaps, Challenges, and Opportunities in Health and Prevention Research for Asian Americans, Native Hawaiians, and Pacific Islanders: A Report From the 2021 National Institutes of Health Workshop.

Author

Kanaya, Alka M, Hsing, Ann W, Panapasa, Sela V, Kandula, Namratha R, Araneta, Maria Rosario G, Shimbo, Daichi, Wang, Paul, Gomez, Scarlett L, Lee, Jinkook, Narayan, KM Venkat, Mau, Marjorie KL Mala, Bose, Sonali, Daviglus, Martha L, Hu, Frank B, Islam, Nadia, Jackson, Chandra L, Kataoka-Yahiro, Merle, Kauwe, John SK, Liu, Simin, Ma, Grace X, Nguyen, Tung, Palaniappan, Latha, Setiawan, V Wendy, Trinh-Shevrin, Chau, Tsoh, Janice Y, Vaidya, Dhananjay, Vickrey, Barbara, Wang, Thomas J, Wong, Nathan D, Coady, Sean, and Hong, Yuling

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Cardiovascular, Good Health and Well Being, Asian, Hawaii, Health Promotion, Humans, National Institutes of Health (U.S.), Native Hawaiian or Other Pacific Islander, United States, Public Health and Health Services
Abstract

Asian Americans (AsA), Native Hawaiians, and Pacific Islanders (NHPI) comprise 7.7% of the U.S. population, and AsA have had the fastest growth rate since 2010. Yet the National Institutes of Health (NIH) has invested only 0.17% of its budget on AsA and NHPI research between 1992 and 2018. More than 40 ethnic subgroups are included within AsA and NHPI (with no majority subpopulation), which are highly diverse culturally, demographically, linguistically, and socioeconomically. However, data for these groups are often aggregated, masking critical health disparities and their drivers. To address these issues, in March 2021, the National Heart, Lung, and Blood Institute, in partnership with 8 other NIH institutes, convened a multidisciplinary workshop to review current research, knowledge gaps, opportunities, barriers, and approaches for prevention research for AsA and NHPI populations. The workshop covered 5 domains: 1) sociocultural, environmental, psychological health, and lifestyle dimensions; 2) metabolic disorders; 3) cardiovascular and lung diseases; 4) cancer; and 5) cognitive function and healthy aging. Two recurring themes emerged: Very limited data on the epidemiology, risk factors, and outcomes for most conditions are available, and most existing data are not disaggregated by subgroup, masking variation in risk factors, disease occurrence, and trajectories. Leveraging the vast phenotypic differences among AsA and NHPI groups was identified as a key opportunity to yield novel clues into etiologic and prognostic factors to inform prevention efforts and intervention strategies. Promising approaches for future research include developing collaborations with community partners, investing in infrastructure support for cohort studies, enhancing existing data sources to enable data disaggregation, and incorporating novel technology for objective measurement. Research on AsA and NHPI subgroups is urgently needed to eliminate disparities and promote health equity in these populations.

Published
2022

28. Effect of 1-year lifestyle intervention with energy-reduced Mediterranean diet and physical activity promotion on the gut metabolome and microbiota: a randomized clinical trial

Author

García-Gavilán, Jesús F, Atzeni, Alessandro, Babio, Nancy, Liang, Liming, Belzer, Clara, Vioque, Jesús, Corella, Dolores, Fitó, Montserrat, Vidal, Josep, Moreno-Indias, Isabel, Torres-Collado, Laura, Coltell, Oscar, Toledo, Estefanía, Clish, Clary, Hernando, Javier, Yun, Huan, Hernández-Cacho, Adrián, Jeanfavre, Sarah, Dennis, Courtney, Gómez-Pérez, Ana M., Martínez, Maria Angeles, Ruiz-Canela, Miguel, Tinahones, Francisco J., Hu, Frank B., and Salas-Salvadó, Jordi

Published
2024
Full Text
View/download PDF

29. Olive oil consumption, plasma metabolites, and risk of type 2 diabetes and cardiovascular disease

Author

García-Gavilán, Jesús F., Babio, Nancy, Toledo, Estefanía, Semnani-Azad, Zhila, Razquin, Cristina, Dennis, Courtney, Deik, Amy, Corella, Dolores, Estruch, Ramón, Ros, Emilio, Fitó, Montserrat, Arós, Fernando, Fiol, Miquel, Lapetra, José, Lamuela-Raventos, Rosa, Clish, Clary, Ruiz-Canela, Miguel, Martínez-González, Miguel Ángel, Hu, Frank, Salas-Salvadó, Jordi, and Guasch-Ferré, Marta

Published
2023
Full Text
View/download PDF

31. The effect of polyphenols on DNA methylation-assessed biological age attenuation: the DIRECT PLUS randomized controlled trial

Author

Yaskolka Meir, Anat, Keller, Maria, Hoffmann, Anne, Rinott, Ehud, Tsaban, Gal, Kaplan, Alon, Zelicha, Hila, Hagemann, Tobias, Ceglarek, Uta, Isermann, Berend, Shelef, Ilan, Blüher, Matthias, Stumvoll, Michael, Li, Jun, Haange, Sven-Bastian, Engelmann, Beatrice, Rolle-Kampczyk, Ulrike, von Bergen, Martin, Hu, Frank B., Stampfer, Meir J., Kovacs, Peter, Liang, Liming, and Shai, Iris

Published
2023
Full Text
View/download PDF

32. Plasma metabolomic profiles associated with mortality and longevity in a prospective analysis of 13,512 individuals

Author

Wang, Fenglei, Tessier, Anne-Julie, Liang, Liming, Wittenbecher, Clemens, Haslam, Danielle E., Fernández-Duval, Gonzalo, Heather Eliassen, A., Rexrode, Kathryn M., Tobias, Deirdre K., Li, Jun, Zeleznik, Oana, Grodstein, Francine, Martínez-González, Miguel A., Salas-Salvadó, Jordi, Clish, Clary, Lee, Kyu Ha, Sun, Qi, Stampfer, Meir J., Hu, Frank B., and Guasch-Ferré, Marta

Published
2023
Full Text
View/download PDF

33. Plasma metabolite profiles associated with the World Cancer Research Fund/American Institute for Cancer Research lifestyle score and future risk of cardiovascular disease and type 2 diabetes

Author

Rios, Santiago, García-Gavilán, Jesús F., Babio, Nancy, Paz-Graniel, Indira, Ruiz-Canela, Miguel, Liang, Liming, Clish, Clary B, Toledo, Estefania, Corella, Dolores, Estruch, Ramón, Ros, Emilio, Fitó, Montserrat, Arós, Fernando, Fiol, Miquel, Guasch-Ferré, Marta, Santos-Lozano, José M, Li, Jun, Razquin, Cristina, Martínez-González, Miguel Ángel, Hu, Frank B, and Salas-Salvadó, Jordi

Published
2023
Full Text
View/download PDF

37. Plasma lipidome and risk of atrial fibrillation: results from the PREDIMED trial

Author

Toledo, Estefania, Wittenbecher, Clemens, Razquin, Cristina, Ruiz-Canela, Miguel, Clish, Clary B., Liang, Liming, Alonso, Alvaro, Hernández-Alonso, Pablo, Becerra-Tomás, Nerea, Arós-Borau, Fernando, Corella, Dolores, Ros, Emilio, Estruch, Ramón, García-Rodríguez, Antonio, Fitó, Montserrat, Lapetra, José, Fiol, Miquel, Alonso-Gomez, Ángel M., Serra-Majem, Luis, Deik, Amy, Salas-Salvadó, Jordi, Hu, Frank B., and Martínez-González, Miguel A.

Published
2023
Full Text
View/download PDF

38. Plasma metabolites of a healthy lifestyle in relation to mortality and longevity: Four prospective US cohort studies

Author

Tessier, Anne-Julie, Wang, Fenglei, Liang, Liming, Wittenbecher, Clemens, Haslam, Danielle E., Eliassen, A. Heather, Tobias, Deirdre K., Li, Jun, Zeleznik, Oana A., Ascherio, Alberto, Sun, Qi, Stampfer, Meir J., Grodstein, Francine, Rexrode, Kathryn M., Manson, JoAnn E., Balasubramanian, Raji, Clish, Clary B., Martínez-González, Miguel A., Chavarro, Jorge E., Hu, Frank B., and Guasch-Ferré, Marta

Published
2024
Full Text
View/download PDF

41. Plasma metabolite predictors of metabolic syndrome incidence and reversion

Author

Semnani-Azad, Zhila, Toledo, Estefanía, Babio, Nancy, Ruiz-Canela, Miguel, Wittenbecher, Clemens, Razquin, Cristina, Wang, Fenglei, Dennis, Courtney, Deik, Amy, Clish, Clary B., Corella, Dolores, Fitó, Montserrat, Estruch, Ramon, Arós, Fernando, Ros, Emilio, García-Gavilan, Jesús, Liang, Liming, Salas-Salvadó, Jordi, Martínez-González, Miguel A., Hu, Frank B., and Guasch-Ferré, Marta

Published
2024
Full Text
View/download PDF

43. Omega-3 Blood Levels and Stroke Risk: A Pooled and Harmonized Analysis of 183 291 Participants From 29 Prospective Studies

Author

O’Keefe, James H., Tintle, Nathan L., Harris, William S., O’Keefe, Evan L., Sala-Vila, Aleix, Attia, John, Garg, G. Manohar, Hure, Alexis, Bork, Christian Sørensen, Schmidt, Erik Berg, Venø, Stine Krogh, Chien, Kuo-Liong, Chen, Yun-Yu (Amelia), Egert, Sarah, Feldreich, Tobias Rudholm, Ärnlöv, Johan, Lind, Lars, Forouhi, Nita G., Geleijnse, Johanna M., Pertiwi, Kamalita, Imamura, Fumiaki, de Mello Laaksonen, Vanessa, Uusitupa, W. Matti, Tuomilehto, Jaakko, Laakso, Markku, Lankinen, Maria Anneli, Laurin, Danielle, Carmichael, Pierre-Hugues, Lindsay, Joan, Leander, Karin, Laguzzi, Federica, Swenson, Brenton R., Longstreth, William T., Manson, JoAnn E., Mora, Samia, Cook, Nancy R., Marklund, Matti, Melo van Lent, Debora, Murphy, Rachel, Gudnason, Vilmundur, Ninomiya, Toshihara, Hirakawa, Yoichiro, Qian, Frank, Sun, Qi, Hu, Frank, Ardisson Korat, Andres V., Risérus, Ulf, Lázaro, Iolanda, Samieri, Cecilia, Le Goff, Mélanie, Helmer, Catherine, Steur, Marinka, Voortman, Trudy, Ikram, M. Kamran, Tanaka, Toshiko, Das, Jayanta K., Ferrucci, Luigi, Bandinelli, Stefania, Tsai, Michael, Guan, Weihua, Garg, Parveen, Verschuren, W.M. Monique, Boer, Jolanda M.A., Biokstra, Anneke, Virtanen, Jyrki, Wagner, Michael, Westra, Jason, Albuisson, Luc, Yamagishi, Kazumasa, Siscovick, David S., Lemaitre, Rozenn N., and Mozaffarian, Dariush

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results