CoChat
Sign Up
Cited by 36Nutrition, Genetics, and DiseaseMetabolomics and Mass Spectrometry StudiesNutrition, Health, and Society Studies

21st century toolkit for optimizing population health through precision nutrition

Open access
O'Sullivan, Aifric, Henrick, Bethany, Dixon, Bonnie, Barile, Daniela, Zivkovic, Angela, Smilowitz, Jennifer, Lemay, Danielle, Martin, William +2 more·2018·Critical Reviews in Food Science and Nutrition
DOI 10.1080/10408398.2017.1348335PMC PMC6309929
Open this paper in CoChat — free account

Abstract

Scientific, technological, and economic progress over the last 100 years all but eradicated problems of widespread food shortage and nutrient deficiency in developed nations. But now society is faced with a new set of nutrition problems related to energy imbalance and metabolic disease, which require new kinds of solutions. Recent developments in the area of new analytical tools enable us to systematically study large quantities of detailed and multidimensional metabolic and health data, providing the opportunity to address current nutrition problems through an approach called Precision Nutrition. This approach integrates different kinds of "big data" to expand our understanding of the complexity and diversity of human metabolism in response to diet. With these tools, we can more fully elucidate each individual's unique phenotype, or the current state of health, as determined by the interactions among biology, environment, and behavior. The tools of precision nutrition include genomics, metabolomics, microbiomics, phenotyping, high-throughput analytical chemistry techniques, longitudinal tracking with body sensors, informatics, data science, and sophisticated educational and behavioral interventions. These tools are enabling the development of more personalized and predictive dietary guidance and interventions that have the potential to transform how the public makes food choices and greatly improve population health.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/10408398.2017.1348335

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 83

  1. Aldredge DL, Geronimo MR, Hua S, (2013). Annotation and structural elucidation of bovine milk oligosaccharides and determination of novel fucosylated structures. Glycobiology 23: 664– 676. doi: 10.1093/glycob/cwt007 23436288 PMC3641798 · DOI · Paper page
  2. Bakker GC, van Erk MJ, Pellis L, (2010). An anti-inflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: a nutrigenomics approach. Am J Clin Nutr. 91: 1044– 1059. doi: 10.3945/ajcn.2009.28822 20181810 · DOI · Paper page
  3. Baranowski T, Frankel L (2012). Let’s get technical! Gaming and technology for weight control and health promotion in children. Childhood Obesity 8: 34– 37. doi: 10.1089/chi.2011.0103 22799477 · DOI
  4. Barlow P, Reeves A, McKee M, (2016). Unhealthy diets, obesity and time discounting: asystematic literature review and network analysis. Obesity Reviews. 17: 810– 819. doi: 10.1111/obr.12431 27256685 PMC4988386 · DOI
  5. Bier DM, German JB, Lönnerdal B (eds): Personalized Nutrition for the Diverse Needs of Infants and Children Nestec Ltd., Vevey/S. Karger AG, Basel, 2008 Nestlé Nutr Workshop Ser Pediatr Program, vol 62, pp 1– 261.
  6. Brown JM, Hazen SL (2014) Metaorganismal nutrient metabolism as a basis of cardiovascular disease. Curr Opin Lipidol 25: 48– 53. doi: 10.1097/MOL.0000000000000036 24362355 PMC4018574 · DOI · Paper page
  7. Byrne CS, Chambers ES, Morrison DJ, Frost G (2015). The role of short chain fatty acids in appetite regulation and energy homeostasis. Int J Obes (Lond) 39: 1331– 1338. doi: 10.1038/ijo.2015.84 25971927 PMC4564526 · DOI · Paper page
  8. Carpenter A, Pencharz P, Mouzaki M (2015a). Accurate estimation of energy requirements of young patients. J Pediatr Gastroenterol Nutr. 60: 4– 10. doi: 10.1097/MPG.0000000000000572 25238120 · DOI
  9. Carpenter D, Dhar S, Mitchell LM, (2015b). Obesity, starch digestion and amylase: association between copy number variants at human salivary (AMY1) and pancreatic (AMY2) amylase genes. Human Molecular Genetics 24: 3472– 3480. doi: 10.1093/hmg/ddv098 25788522 PMC4498156 · DOI
  10. Catford J (2011) The new social learning: Connect better for better health. Health Promotion International 26(2): 133– 135. 21555449
  11. Carter Ching C & Schaefer S (2014). Identities in motion, identities at rest: Engaging bodies and minds in fitness gaming research and design In: Learning technologies and the body: Integration and implementation in formal and informal learning environments. Routledge, New York, pp 201– 219.
  12. Choe EK, Consolvo S, Watson NF, Kientz JA (2011). Opportunities for computing technologies to support healthy sleep behaviors. ACM Press, New York, pp 3053– 3011.
  13. Claud EC, Walker WA (2001). Hypothesis: inappropriate colonization of the premature intestine can cause neonatal necrotizing enterocolitis. FASEB J 15: 1398– 1403. 11387237
  14. Descamps OS, Tenoutasse S, Stephenne X, (2011). Management of familial hypercholesterolemia in children and young adults: Consensus paper developed by a panel of lipidologists, cardiologists, paediatricians, nutritionists, gastroenterologists, general practitioners and a patient organization. Atherosclerosis 218: 272– 280. doi: 10.1016/j.atherosclerosis.2011.06.016 21762914 · DOI
  15. Deshpande G, Rao S, Patole S, Bulsara M (2010). Updated meta-analysis of probiotics for preventing necrotizing enterocolitis in preterm neonates. Pediatrics 125: 921– 930. doi: 10.1542/peds.2009-1301 20403939 · DOI
  16. Draper E, DeCourcey J, Higgins SC, (2014). Conjugated linoleic acid suppresses dendritic cell activation and subsequent Th17 responses. J Nutr Biochem. 25: 741– 749. doi: 10.1016/j.jnutbio.2014.03.004 24794016 · DOI
  17. Dunstan DW, Howard B, Healy GN, Owen N (2012). Too much sitting - a health hazard. Diabetes Res Clin Pract. 97: 368– 376. doi: 10.1016/j.diabres.2012.05.020 22682948 · DOI
  18. el-Mohandes AE, Picard MB, Simmens SJ, Keiser JF (1997). Use of human milk in the intensive care nursery decreases the incidence of nosocomial sepsis. J Perinatol. 17: 130– 134. 9134512
  19. Eriksson N, Macpherson JM, Tung JY, (2010). Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS Genet. 6: e1000993– 20. doi: 10.1371/journal.pgen.1000993 20585627 PMC2891811 · DOI
  20. Fechner A, Kiehntopf M, Jahreis G (2014). The formation of short-chain fatty acids is positively associated with the blood lipid-lowering effect of lupin kernel fiber in moderately hypercholesterolemic adults. J Nutr. 144: 599– 607. doi: 10.3945/jn.113.186858 24572041 · DOI
  21. Frankenfeld CL, McTiernan A, Thomas WK, (2006). Postmenopausal bone mineral density in relation to soy isoflavone-metabolizing phenotypes. Maturitas 53: 315– 324. doi: 10.1016/j.maturitas.2005.05.016 16019168 · DOI
  22. Frye RE (2015). Metabolic and mitochondrial disorders associated with epilepsy in children with autism spectrum disorder. Epilepsy & Behavior 47: 147– 157. doi: 10.1016/j.yebeh.2014.08.134 25440829 · DOI · Paper page
  23. Furbank RT, Tester M (2011). Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16: 635– 644. doi: 10.1016/j.tplants.2011.09.005 22074787 · DOI
  24. Gaullier JM, Halse J, Høye K, (2004). Conjugated linoleic acid supplementation for 1 y reduces body fat mass in healthy overweight humans. Am J Clin Nutr. 79: 1118– 1125. 15159244
  25. German JB, Zivkovic AM, Dallas DC, Smilowitz JT (2011). Nutrigenomics and personalized diets: what will they mean for food? Annu Rev Food Sci Technol. 2: 97– 123. doi: 10.1146/annurev.food.102308.124147 22129377 PMC4414021 · DOI
  26. Gibbs EM, Stock JL, McCoid SC, (1995). Glycemic improvement in diabetic db/db mice by overexpression of the human insulin-regulatable glucose transporter (GLUT4). J Clin Invest. 95: 1512– 1518. doi: 10.1172/JCI117823 7706456 PMC295634 · DOI
  27. Glaser C, Rzehak P, Demmelmair H, (2011). Influence of FADS polymorphisms on tracking of serum glycerophospholipid fatty acid concentrations and percentage composition in children. PLoS ONE 6: e21933– 8. doi: 10.1371/journal.pone.0021933 21818279 PMC3144869 · DOI
  28. Gollamudi SS, Topol EJ, Wineinger NE (2016). A framework for smartphone-enabled, patient-generated health data analysis. PeerJ. 4: e2284. doi: 10.7717/peerj.2284 27547580 PMC4975026 · DOI
  29. Goodacre R (2007). Metabolomics of a superorganism. J Nutr. 137: 259S– 266S. 17182837
  30. Gorissen L, Raes K, Weckx S, (2010). Production of conjugated linoleic acid and conjugated linolenic acid isomers by Bifidobacterium species. Appl Microbiol Biotechnol. 87: 2257– 2266. doi: 10.1007/s00253-010-2713-1 20556602 · DOI
  31. Gowda GN, Zhang S, Gu H, (2014). Metabolomics-based methods for early disease diagnostics. Expert Review of Molecular Diagnostics 8: 617– 633. doi: 10.1586/14737159.8.5.617 PMC3890417 18785810 · DOI
  32. Hamblin MT, Buckler ES, Jannink JL (2011). Population genetics of genomics-based crop improvement methods. Trends in Genetics 27: 98– 106. doi: 10.1016/j.tig.2010.12.003 21227531 · DOI
  33. Harper AE (1981). Dietary guidelines for Americans. Am J Clin Nutr. 34: 121– 123. 7446454
  34. Harris PA, Taylor R, Thielke R, (2009). Research electronic data capture (REDCap)— A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 42: 377– 381. doi: 10.1016/j.jbi.2008.08.010 18929686 PMC2700030 · DOI · Paper page
  35. Himsworth HP (1934). Dietetic factors influencing the glucose tolerance and the activity of insulin. J Physiol (Lond) 81: 29– 48. 16994524 PMC1394223
  36. Hood L, Flores M (2012). A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. N Biotechnol. 29: 613– 24. doi: 10.1016/j.nbt.2012.03.004. 22450380 · DOI · Paper page
  37. Ito M (2009) Foreword The Ecology of Games: Connecting Youth, Games, and Learning In: Salen K, Ed. The John D and Catherine T MacArthur Foundation Series on Digital Media and Learning. The MIT Press, pp 1– 271.
  38. Kelly O, Cusack S, Jewell C, Cashman KD (2003). The effect of polyunsaturated fatty acids, including conjugated linoleic acid, on calcium absorption and bone metabolism and composition in young growing rats. Br J Nutr 90: 743– 750. 13129442
  39. Khoury MJ, Ioannidis JP (2014) Medicine: big data meets public health. Science 346(6213): 1054– 1055. 10.1126/science.aaa2709. 25430753 PMC4684636 · DOI
  40. Khoury MJ, Iademarco MF, Riley WT (2016) Precision public health for the era of precision medicine. Am J Prev Med. 50(3): 398– 401. doi: 10.1016/j.amepre.2015.08.031 26547538 PMC4915347 · DOI
  41. Lai YS, Chen WC, Kuo TC, (2015). Mass-spectrometry-based serum metabolomics of a C57BL/6J mouse model of high-fat-diet-induced non-alcoholic fatty liver disease Development. J Agric Food Chem. 63: 7873– 7884. doi: 10.1021/acs.jafc.5b02830 26262841 · DOI
  42. Lange MC, Lemay DG, German JB (2007). A multi-ontology framework to guide agriculture and food towards diet and health. J Sci Food Agric. 87: 1427– 1434. doi: 10.1002/jsfa.2832 · DOI
  43. Lupton D (2012). M-health and health promotion: The digital cyborg and surveillance society. Social Theory & Health 10(3): 229– 244.
  44. Lemay DG, Zivkovic AM, German JB (2007). Building the bridges to bioinformatics in nutrition research. Am J Clin Nutr. 86: 1261– 1269. 17991634
  45. Lin HY, Chang JH, Chung MY, Lin HC (2014). Prevention of necrotizing enterocolitis in preterm very low birth weight infants: Is it feasible? J Formosan Med Assoc. 113: 490– 497. doi: 10.1016/j.jfma.2013.03.010 23701837 · DOI
  46. Lin PW, Stoll BJ (2006). Necrotising enterocolitis. Lancet 368: 1271– 1283. doi: 10.1016/S0140-6736(06)69525-1 17027734 · DOI
  47. Longo VD, Mattson MP (2014). Fasting: molecular mechanisms and clinical applications. Cell Metabolism 19: 181– 192. doi: 10.1016/j.cmet.2013.12.008 24440038 PMC3946160 · DOI · Paper page
  48. Lyalina S, Percha B, LePendu P, (2013). Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records. J Am Med Inform Assoc. 20: e297– 305. doi: 10.1136/amiajnl-2013-001933. 23956017 PMC3861917 · DOI
  49. McLeod RS, LeBlanc AM, Langille MA, (2004). Conjugated linoleic acids, atherosclerosis, and hepatic very-low-density lipoprotein metabolism. Am J Clin Nutr. 79: 1169S– 1174S. 15159253
  50. Montmayeur JP, le Coutre J, Leonard WR, (2010). Evolutionary Perspectives on Fat Ingestion and Metabolism in Humans. Fat Detection - Taste, Texture, and Post Ingestive Effects Frontiers in Neuroscience. Montmayeur JP. le Coutre J (Eds). Boca Raton (FL): CRC Press/Taylor & Francis.
  51. Neufeld EB, Stonik JA, Demosky SJ, (2004). The ABCA1 Transporter modulates late endocytic trafficking: Insights from the correction of the genetic defect in Tangier Disease. J Biol Chem. 279: 15571– 15578. doi: 10.1074/jbc.M314160200 14747463 · DOI
  52. Niñonuevo M, An H, Yin H, (2005). Nanoliquid chromatography-mass spectrometry of oligosaccharides employing graphitized carbon chromatography on microchip with a highaccuracy mass analyzer. Electrophoresis 26: 3641– 3649. doi: 10.1002/elps.200500246 16196105 · DOI
  53. Niñonuevo MR, Lebrilla CB (2009). Mass spectrometric methods for analysis of oligosaccharides in human milk. Nutr Rev. 67: S216– S226. doi: 10.1111/j.1753-4887.2009.00243.x 19906226 · DOI
  54. Niñonuevo MR, Perkins PD, Francis J, (2008). Daily variations in oligosaccharides of human milk determined by microfluidic chips and mass spectrometry. J Agric Food Chem. 56: 618– 626. doi: 10.1021/jf071972u 18088092 · DOI
  55. O’Sullivan A, Armstrong P, Schuster GU, (2014). Habitual diets rich in dark-green vegetables are associated with an increased response to ω−3 fatty acid supplementation in Americans of African ancestry. J Nutr. 144: 123– 131. doi: 10.3945/jn.113.181875 24259553 PMC3901419 · DOI
  56. O’Sullivan A, Gibney MJ, Connor AO, (2011). Biochemical and metabolomic phenotyping in the identification of a vitamin D responsive metabotype for markers of the metabolic syndrome. Mol Nutr Food Res. 55: 679– 690. doi: 10.1002/mnfr.201000458 21240901 · DOI
  57. Parr EB, Camera DM, Burke LM, (2016). Circulating MicroRNA responses between ‘high’ and “low” responders to a 16-wk diet and exercise weight loss intervention. PLoS ONE 11: e0152545. doi: 10.1371/journal.pone.0152545 27101373 PMC4839646 · DOI
  58. Piccolo BD, Keim NL, Fiehn O, (2015). Habitual physical activity and plasma metabolomic patterns distinguish individuals with low vs. high weight loss during controlled energy restriction. J Nutr 145: 681– 690. doi: 10.3945/jn.114.201574 25833772 PMC4381764 · DOI
  59. Pintus S, Murru E, Carta G, (2013). Sheep cheese naturally enriched in α-linolenic, conjugated linoleic and vaccenic acids improves the lipid profile and reduces anandamide in the plasma of hypercholesterolaemic subjects. Br J Nutr 109: 1453– 1462. doi: 10.1017/S0007114512003224 22917075 · DOI
  60. Puustjarvi J, Puustjarvi L (2011). Personal Health Ontology: towards the interoperation of e-health tools. Int J Electron Healthc. 6: 62– 75. doi: 10.1504/IJEH.2011.039059 21406352 · DOI
  61. Rasmiena AA, Ng TW, Meikle PJ (2013). Metabolomics and ischaemic heart disease. Clin Sci 124: 289– 306. doi: 10.1042/CS20120268 23157406 · DOI
  62. Rasmussen SK, Ingvardsen CR, Torp AM (2010). Mutations in genes controlling the biosynthesis and accumulation of inositol phosphates in seeds. Biochem Soc Trans 38: 689– 694. doi: 10.1042/BST0380689 20298244 · DOI
  63. Robertson DG, Reily MD (2012). The current status of metabolomics in drug discovery and development. Drug Dev Res 73: 535– 546. doi: 10.1002/ddr.21047 · DOI
  64. Ross MK, Wei W, Ohno-Machado L (2014). ―Big Data‖ and the Electronic Health Record. Yearb Med Inform. 9: 97– 104. doi: 10.15265/IY-2014-0003 25123728 PMC4287068 · DOI
  65. Schaefer SE, Camacho-Gomez R, Sadeghi B, (2015). Assessing child obesity and physical activity in a hard-to-reach population in California’s central valley, 2012–2013. Prev Chronic Dis 12: E117. doi: 10.5888/pcd12.140577 26203815 PMC4515914 · DOI
  66. Schaefer SE, Ching CC, Breen H, German JB (2016). Wearing, thinking, and moving: Testing the feasibility of fitness tracking with urban youth. Amer J Health Educ 7: 8– 16. doi: 10.1080/19325037.2015.1111174 · DOI · Paper page
  67. Singh LP, Gill SS, Tuteja N (2011). Unraveling the role of fungal symbionts in plant abiotic stress tolerance. Plant Signal Behav 6: 175– 191. doi: 10.4161/psb.6.2.14146 21512319 PMC3121976 · DOI
  68. Smilowitz JT, Zivkovic AM, Wan YJY, (2013). Nutritional lipidomics: Molecular metabolism, analytics, and diagnostics. Mol Nutr Food Res 57: 1319– 1335. doi: 10.1002/mnfr.201200808 23818328 PMC3806310 · DOI
  69. Stoll BJ, Hansen NI, Bell EF, (2010). Neonatal outcomes of extremely preterm infants from the NICHD neonatal research network. Pediatrics 126: 443– 456. doi: 10.1542/peds.2009-2959 20732945 PMC2982806 · DOI
  70. Tao N, DePeters EJ, German JB, (2009). Variations in bovine milk oligosaccharides during early and middle lactation stages analyzed by high-performance liquid chromatography-chip/mass spectrometry. J Dairy Sci 92: 2991– 3001. doi: 10.3168/jds.20081642 19528576 · DOI
  71. Thomas EL, Parkinson JR, Frost GS, (2009). The Missing Risk: MRI and MRS phenotyping of abdominal adiposity and ectopic fat. Obesity 20: 76– 87. doi: 10.1038/oby.2011.142 21660078 · DOI
  72. Topol EJ (2010). Transforming medicine via digital innovation. Sci Transl Med 2: 16cm4– 16cm4. doi: 10.1126/scitranslmed.3000484 PMC3756088 20371472 · DOI
  73. Turnbaugh PJ, Ley RE, Mahowald MA, (2006). An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444: 1027– 131. doi: 10.1038/nature05414 17183312 · DOI · Paper page
  74. Tweet MS, Gulati R, Aase LA, Hayes SN (2011). Spontaneous coronary artery dissection: a disease-specific, social networking community-initiated study. Mayo Clin Proc 86: 845– 850. doi: 10.4065/mcp.2011.0312 21878595 PMC3257995 · DOI
  75. Wallace M, Hashim YZHY, Wingfield M, (2010). Effects of menstrual cycle phase on metabolomic profiles in premenopausal women. Human Reproduction 25: 949– 956. doi: 10.1093/humrep/deq011 20150174 · DOI
  76. Ward RE, Ninonuevo M, Mills DA, (2006). In vitro fermentation of breast milk oligosaccharides by Bifidobacterium infantis and Lactobacillus gasseri. Appl Environ Microbiol 72: 4497– 4499. doi: 10.1128/AEM.02515-05 16751577 PMC1489581 · DOI
  77. Winnike JH, Li Z, Wright FA, (2009). Use of pharmaco-metabonomics for early prediction of acetaminophen-induced hepatotoxicity in humans. Clinical Pharmacology & Therapeutics 88: 45– 51. doi: 10.1038/clpt.2009.240 20182423 · DOI
  78. Wu S, Grimm R, German JB, Lebrilla CB (2011). Annotation and structural analysis of sialylated human milk oligosaccharides. J Proteome Res 10: 856– 868. doi: 10.1021/pr101006u 21133381 PMC3033989 · DOI
  79. Zeevi D, Korem T, Zmora N, (2015). Personalized nutrition by prediction of glycemic responses. Cell 163: 1079– 1094. doi: 10.1016/j.cell.2015.11.001 26590418 · DOI · Paper page
  80. Zeisel SH (2011). Nutritional genomics: Defining the dietary requirement and effects of choline. J Nutr 141: 531– 534. doi: 10.3945/jn.110.130369 21270363 PMC3040911 · DOI
  81. Zheng H, Clausen M, Dalsgaard T, Bertram H (2015). Metabolomics to Explore Impact of Dairy Intake. Nutrients 7: 4875– 4896. doi: 10.3390/nu7064875 26091233 PMC4488821 · DOI
  82. Zivkovic AM, Barile D (2011). Bovine milk as a source of functional oligosaccharides for improving human health. Adv Nutr 2: 284– 289. doi: 10.3945/an.111.000455 22332060 PMC3090169 · DOI
  83. Zivkovic AM, Yang J, Georgi K, (2012). Serum oxylipin profiles in IgA nephropathy patients reflect kidney functional alterations. Metabolomics 8: 1102– 1113. doi: 10.1007/s11306-012-0417-5 23833568 PMC3700377 · DOI

Cited by

Sorted by citations

Authors
O'Sullivan, A., Henrick, B., Dixon, B., Barile, D., Zivkovic, A., Smilowitz, J., Lemay, D., Martin, W., German, J. B., & Schaefer, S. E.
Affiliations
  • University College Dublin, Dublin, Ireland
  • University of California, Davis (US)
  • Western Human Nutrition Research Center (US)
Corresponding author
Schaefer, Sara Elizabeth
ORCID
Venue
Critical Reviews in Food Science and Nutrition
Publisher
Informa UK Limited
Published
2017-08-22
Volume
58
Issue
17
Pages
3004-3015
ISSN
  • 1040-8398 (ISSN-L)
  • 1549-7852
DOI
10.1080/10408398.2017.1348335
PMC
PMC6309929
OpenAlex
W2725395698
Cited by
36
References
8
Citations by year
2025: 3 · 2024: 5 · 2023: 3 · 2022: 5 · 2021: 8 · 2020: 5 · 2019: 5 · 2018: 2
FWCI
0.42
Type
journal article
Language
en
Open access
Locations
  • Critical Reviews in Food Science and Nutrition (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
National Institute of Environmental Health Sciences (P42ES004699, P01ES011269)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Nutrition, Health, and Society Studies
Keywords
personalized nutrition, population health, energy imbalance, metabolic disorders, big data, human metabolism, genomics, metabolomics, microbiomics, phenotyping, behavioral interventions, body sensors, data science
MeSH terms
Diet, Health Promotion, Humans, Nutritional Status, Nutrition Therapy, Nutritional Sciences

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review