|
1
|
Zeisel SH: A conceptual framework for
studying and investing in precision nutrition. Front Genet.
10(200)2019.PubMed/NCBI View Article : Google Scholar
|
|
2
|
Matusheski NV, Caffrey A, Christensen L,
Mezgec S, Surendran S, Hjorth MF, McNulty H, Pentieva K, Roager HM,
Seljak BK, et al: Diets, nutrients, genes and the microbiome:
recent advances in personalised nutrition. Br J Nutr.
126:1489–1497. 2021.PubMed/NCBI View Article : Google Scholar
|
|
3
|
Livingstone KM, Ramos-Lopez O, Pérusse L,
Kato H, Ordovas JM and Martínez JA: Precision nutrition: A review
of current approaches and future endeavors. Trends Food Sci
Technol. 128:253–264. 2022.
|
|
4
|
De Toro-Martín J, Arsenault BJ, Després JP
and Vohl MC: Precision nutrition: A review of personalized
nutritional approaches for the prevention and management of
metabolic syndrome. Nutrients. 9(913)2017.PubMed/NCBI View Article : Google Scholar
|
|
5
|
Cuyàs E, Verdura S, Martin-Castillo B,
Alarcón T, Lupu R, Bosch-Barrera J and Menendez JA: Tumor
cell-intrinsic immunometabolism and precision nutrition in cancer
immunotherapy. Cancers (Basel). 12(1757)2020.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Ramos-Lopez O, Martinez JA and Milagro FI:
Holistic integration of omics tools for precision nutrition in
health and disease. Nutrients. 14(4074)2022.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Morin-Bernier J, De Toro-Martín J, Barbe
V, San-Cristobal R, Lemieux S, Rudkowska I, Couture P, Barbier O
and Vohl MC: Revisiting multi-omics-based predictors of the plasma
triglyceride response to an omega-3 fatty acid supplementation.
Front Nutr. 11(1327863)2024.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Walker ME, Song RJ, Xu X, Gerszten RE, Ngo
D, Clish CB, Corlin L, Ma J, Xanthakis V, Jacques PF and Vasan RS:
Proteomic and metabolomic correlates of healthy dietary patterns:
The framingham heart study. Nutrients. 12(1476)2020.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Aldubayan MA, Pigsborg K, Gormsen SMO,
Serra F, Palou M, Mena P, Wetzels M, Calleja A, Caimari A, Del Bas
J, et al: Empowering consumers to PREVENT diet-related diseases
through OMICS sciences (PREVENTOMICS): Protocol for a parallel
double-blinded randomised intervention trial to investigate
biomarker-based nutrition plans for weight loss. BMJ Open.
12(e051285)2022.PubMed/NCBI View Article : Google Scholar
|
|
10
|
Ďásková N, Modos I, Krbcová M, Kuzma M,
Pelantová H, Hradecký J, Heczková M, Bratová M, Videňská P,
Šplíchalová P, et al: Multi-omics signatures in new-onset diabetes
predict metabolic response to dietary inulin: Findings from an
observational study followed by an interventional trial. Nutr
Diabetes. 13(7)2023.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Li L, Li P and Xu L: Assessing the effects
of inulin-type fructan intake on body weight, blood glucose, and
lipid profile: A systematic review and meta-analysis of randomized
controlled trials. Food Sci Nutr. 9:4598–4616. 2021.PubMed/NCBI View Article : Google Scholar
|
|
12
|
Zhang L, Ye Y, Tu H, Hildebrandt MA, Zhao
L, Heymach JV, Roth JA and Wu X: MicroRNA-related genetic variants
in iron regulatory genes, dietary iron intake, microRNAs and lung
cancer risk. Ann Oncol. 28:1124–1129. 2017.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Roberts MC, Holt KE, Del Fiol G,
Baccarelli AA and Allen CG: Precision public health in the era of
genomics and big data. Nat Med. 30:1865–1873. 2024.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Mathur S and Sutton J: Personalized
medicine could transform healthcare. Biomed Rep. 7:3–5.
2017.PubMed/NCBI View Article : Google Scholar
|
|
15
|
Karczewski KJ and Snyder MP: Integrative
omics for health and disease. Nat Rev Genet. 19:299–310.
2018.PubMed/NCBI View Article : Google Scholar
|
|
16
|
Theodore Armand TP, Nfor KA, Kim JI and
Kim HC: Applications of artificial intelligence, machine learning,
and deep learning in nutrition: A systematic review. Nutrients.
16(1073)2024.PubMed/NCBI View Article : Google Scholar
|
|
17
|
Andrews S: FastQC: A Quality Control Tool
for High Throughput Sequence Data, 2010. http://www.bioinformatics.babraham.ac.uk/projects/fastqc.
|
|
18
|
Bolger AM, Lohse M and Usadel B:
Trimmomatic: A flexible trimmer for Illumina sequence data.
Bioinformatics. 30:2114–2120. 2014.PubMed/NCBI View Article : Google Scholar
|
|
19
|
Boratyn GM, Thierry-Mieg J, Thierry-Mieg
D, Busby B and Madden TL: Magic-BLAST, an accurate RNA-seq aligner
for long and short reads. BMC Bioinformatics.
20(405)2019.PubMed/NCBI View Article : Google Scholar
|
|
20
|
Dobin A, Davis CA, Schlesinger F, Drenkow
J, Zaleski C, Jha S, Batut P, Chaisson M and Gingeras TR: STAR:
Ultrafast universal RNA-seq aligner. Bioinformatics. 29:15–21.
2013.PubMed/NCBI View Article : Google Scholar
|
|
21
|
Danecek P, Bonfield JK, Liddle J, Marshall
J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM
and Li H: Twelve years of SAMtools and BCFtools. GigaScience.
10(giab008)2021.PubMed/NCBI View Article : Google Scholar
|
|
22
|
R Core Team: R: A language and environment
for statistical computing. R Foundation for Statistical Computing,
Vienna, 2020. https://www.R-project.org/.
|
|
23
|
Love MI, Huber W and Anders S: Moderated
estimation of fold change and dispersion for RNA-seq data with
DESeq2. Genome Biol. 15(550)2014.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Robinson MD, McCarthy DJ and Smyth GK:
edgeR : A Bioconductor package for differential expression analysis
of digital gene expression data. Bioinformatics. 26:139–140.
2010.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW,
Shi W and Smyth GK: limma powers differential expression analyses
for RNA-sequencing and microarray studies. Nucleic Acids Res.
43(e47)2015.PubMed/NCBI View Article : Google Scholar
|
|
26
|
Wickham H, Averick M, Bryan J, Chang W,
McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, et
al: Welcome to the Tidyverse. J Open Source Softw. 4(1686)2019.
|
|
27
|
Whickham H, François R, Lionel H, Müller K
and Vaughan D: dplyr: A Grammar of Data Manipulation. Version
1.1.0, 2023. https://github.com/tidyverse/dplyr.
|
|
28
|
Stekhoven DJ and Bühlmann P:
MissForest-non-parametric missing value imputation for mixed-type
data. Bioinformatics. 28:112–118. 2012.PubMed/NCBI View Article : Google Scholar
|
|
29
|
Buuren SV and Groothuis-Oudshoorn K: Mice
: Multivariate Imputation by Chained Equations in R. J Stat
Softw. 45:1–67. 2011.
|
|
30
|
Jin L, Bi Y, Hu C, Qu J, Shen S, Wang X
and Tian Y: A comparative study of evaluating missing value
imputation methods in label-free proteomics. Sci Rep.
11(1760)2021.PubMed/NCBI View Article : Google Scholar
|
|
31
|
Kong W, Hui HWH, Peng H and Goh WWB:
Dealing with missing values in proteomics data. Proteomics.
22(2200092)2022.PubMed/NCBI View Article : Google Scholar
|
|
32
|
Wei R, Wang J, Su M, Jia E, Chen S, Chen T
and Ni Y: Missing value imputation approach for mass
spectrometry-based metabolomics Data. Sci Rep.
8(663)2018.PubMed/NCBI View Article : Google Scholar
|
|
33
|
Stamoula E, Sarantidi E, Dimakopoulos V,
Ainatzoglou A, Dardalas I, Papazisis G, Kontopoulou K and
Anagnostopoulos AK: Serum Proteome Signatures of Anti-SARS-CoV-2
Vaccinated Healthcare Workers in Greece Associated with Their Prior
Infection Status. Int J Mol Sci. 23(10153)2022.PubMed/NCBI View Article : Google Scholar
|
|
34
|
Ashburner M, Ball CA, Blake JA, Botstein
D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT,
et al: Gene Ontology: Tool for the unification of biology. The Gene
Ontology Consortium. Nat Genet. 25:25–29. 2000.PubMed/NCBI View
Article : Google Scholar
|
|
35
|
The Gene Ontology Consortium. Aleksander
SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, Ebert D, Feuermann
M, Gaudet P, Harris NL, et al: The Gene Ontology knowledgebase in
2023. Genetics. 224(iyad031)2023.PubMed/NCBI View Article : Google Scholar
|
|
36
|
Kanehisa M and Goto S: KEGG: Kyoto
encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30.
2000.PubMed/NCBI View Article : Google Scholar
|
|
37
|
Subramanian A, Tamayo P, Mootha VK,
Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub
TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A
knowledge-based approach for interpreting genome-wide expression
profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005.PubMed/NCBI View Article : Google Scholar
|
|
38
|
Wickham H: ggplot2: Elegant Graphics for
Data Analysis [Internet]. Springer-Verlag, New York, NY, 2016.
https://ggplot2.tidyverse.org.
|
|
39
|
Sarkar D: Lattice: Multivariate Data
Visualization with R [Internet]. Springer, New York, NY, 2008.
http://link.springer.com/10.1007/978-0-387-75969-2.
|
|
40
|
Vavilis T, Petre ML, Vatsellas G,
Ainatzoglou A, Stamoula E, Sachinidis A, Lamprinou M, Dardalas I,
Vamvakaris IN, Gkiozos I, et al: Lung cancer proteogenomics:
Shaping the future of clinical investigation. Cancers (Basel).
16(1236)2024.PubMed/NCBI View Article : Google Scholar
|
|
41
|
Ulusoy-Gezer HG and Rakıcıoğlu N: The
future of obesity management through precision nutrition: Putting
the individual at the center. Curr Nutr Rep. 13:455–477.
2024.PubMed/NCBI View Article : Google Scholar
|
|
42
|
Morgenstern JD, Rosella LC, Costa AP, De
Souza RJ and Anderson LN: Perspective: Big data and machine
learning could help advance nutritional epidemiology. Adv Nutr.
12:621–631. 2021.PubMed/NCBI View Article : Google Scholar
|
|
43
|
Rapaport F, Khanin R, Liang Y, Pirun M,
Krek A, Zumbo P, Mason CE, Socci ND and Betel D: Comprehensive
evaluation of differential gene expression analysis methods for
RNA-seq data. Genome Biol. 14(3158)2013.PubMed/NCBI View Article : Google Scholar
|
|
44
|
Peng H, Wang H, Kong W, Li J and Goh WWB:
Optimizing differential expression analysis for proteomics data via
high-performing rules and ensemble inference. Nat Commun.
15(3922)2024.PubMed/NCBI View Article : Google Scholar
|
|
45
|
Huang Z and Wang C: A review on
differential abundance analysis methods for mass spectrometry-based
metabolomic data. Metabolites. 12(305)2022.PubMed/NCBI View Article : Google Scholar
|
|
46
|
Rohart F, Gautier B, Singh A and Lê Cao
KA: mixOmics: An R package for ‘omics feature selection and
multiple data integration. PLoS Comput Biol.
13(e1005752)2017.PubMed/NCBI View Article : Google Scholar
|
|
47
|
Vadiveloo MK, Juul F, Sotos-Prieto M and
Parekh N: Perspective: Novel approaches to evaluate dietary
quality: Combining methods to enhance measurement for dietary
surveillance and interventions. Adv Nutr. 13:1009–1015.
2022.PubMed/NCBI View Article : Google Scholar
|
|
48
|
Singh VK, Hu XH, Singh AK, Solanki MK,
Vijayaraghavan P, Srivastav R, Joshi NK, Kumari M, Singh SK, Wang Z
and Kumar A: Precision nutrition-based strategy for management of
human diseases and healthy aging: Current progress and challenges
forward. Front Nutr. 11(1427608)2024.PubMed/NCBI View Article : Google Scholar
|
|
49
|
Voruganti VS: Precision nutrition: Recent
advances in obesity. Physiology (Bethesda). 38(0)2023.PubMed/NCBI View Article : Google Scholar
|
|
50
|
Fiocchi C: Omics and Multi-Omics in IBD:
No Integration, No Breakthroughs. Int J Mol Sci.
24(14912)2023.PubMed/NCBI View Article : Google Scholar
|
|
51
|
Anagnostopoulos AK, Gaitanis A, Gkiozos I,
Athanasiadis EI, Chatziioannou SN, Syrigos KN, Thanos D,
Chatziioannou AN and Papanikolaou N: Radiomics/Radiogenomics in
Lung Cancer: Basic principles and initial clinical results. Cancers
(Basel). 14(1657)2022.PubMed/NCBI View Article : Google Scholar
|
|
52
|
Aleksandrova K, Egea Rodrigues C, Floegel
A and Ahrens W: Omics Biomarkers in Obesity: Novel etiological
insights and targets for precision prevention. Curr Obes Rep.
9:219–230. 2020.PubMed/NCBI View Article : Google Scholar
|
|
53
|
Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z,
Chandak P, Liu S, Van Katwyk P, Deac A, et al: Scientific discovery
in the age of artificial intelligence. Nature. 620:47–60.
2023.PubMed/NCBI View Article : Google Scholar
|
|
54
|
Topol EJ: High-performance medicine: The
convergence of human and artificial intelligence. Nat Med.
25:44–56. 2019.PubMed/NCBI View Article : Google Scholar
|
|
55
|
Verma M, Hontecillas R, Tubau-Juni N,
Abedi V and Bassaganya-Riera J: Challenges in Personalized
Nutrition and Health. Front Nutr. 5(117)2018.PubMed/NCBI View Article : Google Scholar
|
|
56
|
Kohlmeier M, De Caterina R, Ferguson LR,
Görman U, Allayee H, Prasad C, Kang JX, Nicoletti CF and Martinez
JA: Guide and position of the international society of
nutrigenetics/nutrigenomics on personalized nutrition: Part 2 -
ethics, challenges and endeavors of precision nutrition. J
Nutrigenet Nutrigenomics. 9:28–46. 2016.PubMed/NCBI View Article : Google Scholar
|
|
57
|
World Health Organization (WHO): WHO
Guideline: Recommendations on digital interventions for health
system strengthening. WHO, Geneva, 2019.
|
|
58
|
Lee BY, Ordovás JM, Parks EJ, Anderson CA,
Barabási AL, Clinton SK, de la Haye K, Duffy VB, Franks PW, Ginexi
EM, et al: Research gaps and opportunities in precision nutrition:
An NIH workshop report. Am J Clin Nutr. 116:1877–1900.
2022.PubMed/NCBI View Article : Google Scholar
|
|
59
|
Röttger-Wirtz S and De Boer A:
Personalised nutrition: The EU's fragmented legal landscape and the
overlooked implications of EU food law. Eur J Risk Regul.
12:212–235. 2021.
|
|
60
|
Aldubayan MA, Pigsborg K, Gormsen SMO,
Serra F, Palou M, Galmés S, Palou-March A, Favari C, Wetzels M,
Calleja A, et al: A double-blinded, randomized, parallel
intervention to evaluate biomarker-based nutrition plans for weight
loss: The PREVENTOMICS study. Clin Nutr. 41:1834–1844.
2022.PubMed/NCBI View Article : Google Scholar
|
|
61
|
Sawicki C, Haslam D and Bhupathiraju S:
Utilising the precision nutrition toolkit in the path towards
precision medicine. Proc Nutr Soc. 82:359–369. 2023.PubMed/NCBI View Article : Google Scholar
|
|
62
|
Berciano S, Figueiredo J, Brisbois TD,
Alford S, Koecher K, Eckhouse S, Ciati R, Kussmann M, Ordovas JM,
Stebbins K and Blumberg JB: Precision nutrition: Maintaining
scientific integrity while realizing market potential. Front Nutr.
9(979665)2022.PubMed/NCBI View Article : Google Scholar
|
|
63
|
Mohr AE, Ortega-Santos CP, Whisner CM,
Klein-Seetharaman J and Jasbi P: Navigating challenges and
opportunities in multi-omics integration for personalized
healthcare. Biomedicines. 12(1496)2024.PubMed/NCBI View Article : Google Scholar
|
|
64
|
Wu Y, Perng W and Peterson KE: Precision
nutrition and childhood obesity: A scoping review. Metabolites.
10(235)2020.PubMed/NCBI View Article : Google Scholar
|
|
65
|
Özdemir V and Kolker E: Precision
nutrition 4.0: A big data and ethics foresight analysis-convergence
of agrigenomics, nutrigenomics, nutriproteomics, and
nutrimetabolomics. OMICS J Integr Biol. 20:69–75. 2016.PubMed/NCBI View Article : Google Scholar
|
