Metagenomic estimation of dietary intake from human stool

0
Metagenomic estimation of dietary intake from human stool
  • Harding, J. E., Cormack, B. E., Alexander, T., Alsweiler, J. M. & Bloomfield, F. H. Advances in nutrition of the newborn infant. Lancet 389, 1660–1668 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • de Ridder, D., Kroese, F., Evers, C., Adriaanse, M. & Gillebaart, M. Healthy diet: health impact, prevalence, correlates, and interventions. Psychol. Health 32, 907–941 (2017).

    PubMed 

    Google Scholar 

  • Clark, M., Hill, J. & Tilman, D. The diet, health, and environment trilemma. Annu. Rev. Environ. Resour. 43, 109–134 (2018).

    Google Scholar 

  • David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    CAS 
    PubMed 

    Google Scholar 

  • Wang, D. D. et al. The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk. Nat. Med. 27, 333–343 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gu, Y., Nieves, J. W., Stern, Y., Luchsinger, J. A. & Scarmeas, N. Food combination and Alzheimer disease risk: a protective diet. Arch. Neurol. 67, 699–706 (2010).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Mente, A. et al. Diet, cardiovascular disease, and mortality in 80 countries. Eur. Heart J. 44, 2560–2579 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Magkos, F., Hjorth, M. F. & Astrup, A. Diet and exercise in the prevention and treatment of type 2 diabetes mellitus. Nat. Rev. Endocrinol. 16, 545–555 (2020).

    PubMed 

    Google Scholar 

  • Key, T. J., Allen, N. E., Spencer, E. A. & Travis, R. C. The effect of diet on risk of cancer. Lancet 360, 861–868 (2002).

    CAS 
    PubMed 

    Google Scholar 

  • Ludwig, D. S., Ebbeling, C. B. & Heymsfield, S. B. Improving the quality of dietary research. JAMA 322, 1549–1550 (2019).

    PubMed 

    Google Scholar 

  • Molag, M. L. et al. Design characteristics of food frequency questionnaires in relation to their validity. Am. J. Epidemiol. 166, 1468–1478 (2007).

    PubMed 

    Google Scholar 

  • Timon, C. M. et al. A review of the design and validation of web- and computer-based 24-h dietary recall tools. Nutr. Res. Rev. 29, 268–280 (2016).

    PubMed 

    Google Scholar 

  • Conway, J. M., Ingwersen, L. A. & Moshfegh, A. J. Accuracy of dietary recall using the USDA five-step multiple-pass method in men: an observational validation study. J. Am. Diet. Assoc. 104, 595–603 (2004).

    PubMed 

    Google Scholar 

  • Abu-Saad, K., Shahar, D. R., Vardi, H. & Fraser, D. Importance of ethnic foods as predictors of and contributors to nutrient intake levels in a minority population. Eur. J. Clin. Nutr. 64, S88–S94 (2010).

    PubMed 

    Google Scholar 

  • Mozaffarian, D. & Forouhi, N. G. Dietary guidelines and health—Is nutrition science up to the task? Brit. Med. J. 360, k822 (2018).

    PubMed 

    Google Scholar 

  • Taubes, G. Epidemiology faces its limits. Science 269, 164–169 (1995).

    CAS 
    PubMed 

    Google Scholar 

  • Young, S. S. & Karr, A. Deming, data and observational studies. Signif. (Oxf.) 8, 116–120 (2011).

    Google Scholar 

  • Sturgeon, C. M. et al. National Academy of Clinical Biochemistry laboratory medicine practice guidelines for use of tumor markers in testicular, prostate, colorectal, breast, and ovarian cancers. Clin. Chem. 54, e11–e79 (2008).

    CAS 
    PubMed 

    Google Scholar 

  • Mundi, S. et al. Endothelial permeability, LDL deposition, and cardiovascular risk factors—a review. Cardiovasc. Res. 114, 35–52 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • Zuppinger, C. et al. Performance of the digital dietary assessment tool MyFoodRepo. Nutrients 14, 635 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mohanty, S. P. et al. The food recognition benchmark: using deep learning to recognize food in images. Front. Nutr. 9, 875143 (2022).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Mortazavi, B. J. & Gutierrez-Osuna, R. A review of digital innovations for diet monitoring and precision nutrition. J. Diabetes Sci. Technol. 17, 217–223 (2023).

    PubMed 

    Google Scholar 

  • Hassannejad, H. et al. Automatic diet monitoring: a review of computer vision and wearable sensor-based methods. Int. J. Food Sci. Nutr. 68, 656–670 (2017).

    PubMed 

    Google Scholar 

  • West, K. A., Schmid, R., Gauglitz, J. M., Wang, M. & Dorrestein, P. C. foodMASST a mass spectrometry search tool for foods and beverages. NPJ Sci. Food 6, 22 (2022).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Dorrestein, P. Metabolomics technologies for defining diet influences on brain metabolome and in Alzheimer’s disease. Alzheimers Dement. 18, e067277 (2022).

    Google Scholar 

  • Petrone, B. L. et al. Diversity of plant DNA in stool is linked to dietary quality, age, and household income. Proc. Natl Acad. Sci. USA 120, e2304441120 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Deagle, B. E., Thomas, A. C., Shaffer, A. K., Trites, A. W. & Jarman, S. N. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: Which counts count? Mol. Ecol. Resour. 13, 620–633 (2013).

    CAS 
    PubMed 

    Google Scholar 

  • Integrative HMP (iHMP) Research Network Consortium. The Integrative Human Microbiome Project. Nature 569, 641–648 (2019).

    Google Scholar 

  • Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J. & Segata, N. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833–844 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Blanco-Míguez, A. et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat. Biotechnol. 41, 1633–1644 (2023).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Brent, M. R. How does eukaryotic gene prediction work? Nat. Biotechnol. 25, 883–885 (2007).

    CAS 
    PubMed 

    Google Scholar 

  • Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ounit, R., Wanamaker, S., Close, T. J. & Lonardi, S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genomics 16, 236 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Shen, W. et al. KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping. Bioinformatics 39, btac845 (2023).

    CAS 
    PubMed 

    Google Scholar 

  • Gihawi, A. et al. Major data analysis errors invalidate cancer microbiome findings. Mbio 14, e0160723 (2023).

    PubMed 

    Google Scholar 

  • Breitwieser, F. P., Baker, D. N. & Salzberg, S. L. KrakenUniq: confident and fast metagenomics classification using unique k-mer counts. Genome Biol. 19, 198 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, e104 (2017).

    Google Scholar 

  • Srivastava, A. et al. Alignment and mapping methodology influence transcript abundance estimation. Genome Biol. 21, 239 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sun, Z. et al. Challenges in benchmarking metagenomic profilers. Nat. Methods 18, 618–626 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Corbin, K. D. et al. Host–diet–gut microbiome interactions influence human energy balance: a randomized clinical trial. Nat. Commun. 14, 3161 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Thompson, S. V. et al. Avocado consumption alters gastrointestinal bacteria abundance and microbial metabolite concentrations among adults with overweight or obesity: a randomized controlled trial. J. Nutr. 151, 753–762 (2021).

    CAS 
    PubMed 

    Google Scholar 

  • Asnicar, F. et al. Original research: blue poo: impact of gut transit time on the gut microbiome using a novel marker. Gut 70, 1665 (2021).

    CAS 
    PubMed 

    Google Scholar 

  • Duan, Y., Pi, Y., Li, C. & Jiang, K. An optimized procedure for detection of genetically modified DNA in refined vegetable oils. Food Sci. Biotechnol. 30, 129–135 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Scollo, F. et al. Absolute quantification of olive oil DNA by droplet digital-PCR (ddPCR): comparison of isolation and amplification methodologies. Food Chem. 213, 388–394 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • Baumann-Dudenhoeffer, A. M., D’Souza, A. W., Tarr, P. I., Warner, B. B. & Dantas, G. Infant diet and maternal gestational weight gain predict early metabolic maturation of gut microbiomes. Nat. Med. 24, 1822–1829 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Manore, M. M. Exercise and the Institute of Medicine recommendations for nutrition. Curr. Sports Med. Rep. 4, 193–198 (2005).

    PubMed 

    Google Scholar 

  • Fromentin, S. et al. Microbiome and metabolome features of the cardiometabolic disease spectrum. Nat. Med. 28, 303–314 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Thomas, M. S., Calle, M. & Fernandez, M. L. Healthy plant-based diets improve dyslipidemias, insulin resistance, and inflammation in metabolic syndrome. A narrative review. Adv. Nutr. 14, 44–54 (2023).

    PubMed 

    Google Scholar 

  • Neuenschwander, M. et al. Substitution of animal-based with plant-based foods on cardiometabolic health and all-cause mortality: a systematic review and meta-analysis of prospective studies. BMC Medicine 21, 404 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Embleton, N. D. Optimal protein and energy intakes in preterm infants. Early Hum. Dev. 83, 831–837 (2007).

    CAS 
    PubMed 

    Google Scholar 

  • Uauy, R., Mena, P. & Valenzuela, A. Essential fatty acids as determinants of lipid requirements in infants, children and adults. Eur. J. Clin. Nutr. 53, S66–S77 (1999).

    PubMed 

    Google Scholar 

  • Neis, F. A., de Costa, F., de Araújo, A. T. Jr., Fett, J. P. & Fett-Neto, A. G. Multiple industrial uses of non-wood pine products. Ind. Crops Prod. 130, 248–258 (2019).

    CAS 

    Google Scholar 

  • Wallick, D. Cellulose polymers in microencapsulation of food additives. In Microencapsulation in the Food Industry (eds Gaonkar A. et al.) 181–193 (Elsevier, 2014).

  • Li, N., Simon, J. E. & Wu, Q. Development of a scalable, high-anthocyanin and low-acidity natural red food colorant from Hibiscus sabdariffa L. Food Chem. 461, 140782 (2024).

    CAS 
    PubMed 

    Google Scholar 

  • Ruxton, C. H. S., Gardner, E. J. & McNulty, H. M. Is sugar consumption detrimental to health? A review of the evidence 1995–2006. Crit. Rev. Food Sci. Nutr. 50, 1–19 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • Crovetto, M. et al. Effect of healthy and unhealthy habits on obesity: a multicentric study. Nutrition 54, 7–11 (2018).

    PubMed 

    Google Scholar 

  • Gibbons, S. M. et al. Perspective: leveraging the gut microbiota to predict personalized responses to dietary, prebiotic, and probiotic interventions. Adv. Nutr. 13, 1450–1461 (2022).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Lovegrove, J. A., Hodson, L., Sharma, S. & Lanham-New S. A. Nutrition Research Methodologies (John Wiley & Sons, 2015).

  • Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Di Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35, 316–319 (2017).

    PubMed 

    Google Scholar 

  • Corbin, K. D. et al. Integrative and quantitative bioenergetics: design of a study to assess the impact of the gut microbiome on host energy balance. Contemp. Clin. Trials Commun. 19, 100646 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  • link

    Leave a Reply

    Your email address will not be published. Required fields are marked *