The unmapped chemical complexity of our diet

被引:198
作者
Barabasi, Albert-Laszlo [1 ,2 ,3 ,4 ,5 ]
Menichetti, Giulia [1 ,2 ]
Loscalzo, Joseph [3 ,4 ]
机构
[1] Northeastern Univ, Network Sci Inst, Boston, MA 02115 USA
[2] Northeastern Univ, Dept Phys, Boston, MA 02115 USA
[3] Harvard Med Sch, Brigham & Womens Hosp, Channing Div Network Med, Boston, MA 02115 USA
[4] Harvard Med Sch, Brigham & Womens Hosp, Dept Med, Boston, MA 02115 USA
[5] Cent European Univ, Dept Network & Data Sci, Budapest, Hungary
来源
NATURE FOOD | 2020年 / 1卷 / 01期
关键词
GREEN TEA; DISEASE; RISK;
D O I
10.1038/s43016-019-0005-1
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Advances such as machine learning may enable the full biochemical spectrum of food to be studied systematically. Uncovering the 'dark matter' of nutrition could open new avenues for a greater understanding of the composition of what we eat and how it relates to health and disease Our understanding of how diet affects health is limited to 150 key nutritional components that are tracked and catalogued by the United States Department of Agriculture and other national databases. Although this knowledge has been transformative for health sciences, helping unveil the role of calories, sugar, fat, vitamins and other nutritional factors in the emergence of common diseases, these nutritional components represent only a small fraction of the more than 26,000 distinct, definable biochemicals present in our food-many of which have documented effects on health but remain unquantified in any systematic fashion across different individual foods. Using new advances such as machine learning, a high-resolution library of these biochemicals could enable the systematic study of the full biochemical spectrum of our diets, opening new avenues for understanding the composition of what we eat, and how it affects health and disease.
引用
收藏
页码:33 / 37
页数:5
相关论文
共 56 条
[1]  
[Anonymous], 2007, P 24 INT C MACH LEAR
[2]  
[Anonymous], 2018, BMJ BRIT MED J, DOI DOI 10.1136/BMJ.K2392
[3]  
[Anonymous], 2018, Taxonomy, V2016
[4]  
[Anonymous], DIARY
[5]   An incremental clustering method based on the boundary profile [J].
Bao, Junpeng ;
Wang, Wenqing ;
Yang, Tianshe ;
Wu, Guan .
PLOS ONE, 2018, 13 (04)
[6]   Network medicine: a network-based approach to human disease [J].
Barabasi, Albert-Laszlo ;
Gulbahce, Natali ;
Loscalzo, Joseph .
NATURE REVIEWS GENETICS, 2011, 12 (01) :56-68
[7]   Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology [J].
Bennett, Derrick A. ;
Landry, Denise ;
Little, Julian ;
Minelli, Cosetta .
BMC MEDICAL RESEARCH METHODOLOGY, 2017, 17
[8]   Foodomics: a new comprehensive approach to food and nutrition [J].
Capozzi, Francesco ;
Bordoni, Alessandra .
GENES AND NUTRITION, 2013, 8 (01) :1-4
[9]   Trimethylamine-N-oxide (TMAO) response to animal source foods varies among healthy young men and is influenced by their gut microbiota composition: A randomized controlled trial [J].
Cho, Clara E. ;
Taesuwan, Siraphat ;
Malysheva, Olga V. ;
Bender, Erica ;
Tulchinsky, Nathan F. ;
Yan, Jian ;
Sutter, Jessica L. ;
Caudill, Marie A. .
MOLECULAR NUTRITION & FOOD RESEARCH, 2017, 61 (01)
[10]  
Dagnino S., 2019, Unraveling The Exposome: A Practical View, DOI 10.1007/978-3-319-89321-1