Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
08 May 2024
08 May 2024
Historique:
received:
18
10
2023
accepted:
26
03
2024
medline:
8
5
2024
pubmed:
8
5
2024
entrez:
7
5
2024
Statut:
aheadofprint
Résumé
Large variability exists in people's responses to foods. However, the efficacy of personalized dietary advice for health remains understudied. We compared a personalized dietary program (PDP) versus general advice (control) on cardiometabolic health using a randomized clinical trial. The PDP used food characteristics, individual postprandial glucose and triglyceride (TG) responses to foods, microbiomes and health history, to produce personalized food scores in an 18-week app-based program. The control group received standard care dietary advice (US Department of Agriculture Guidelines for Americans, 2020-2025) using online resources, check-ins, video lessons and a leaflet. Primary outcomes were serum low-density lipoprotein cholesterol and TG concentrations at baseline and at 18 weeks. Participants (n = 347), aged 41-70 years and generally representative of the average US population, were randomized to the PDP (n = 177) or control (n = 170). Intention-to-treat analysis (n = 347) between groups showed significant reduction in TGs (mean difference = -0.13 mmol l
Identifiants
pubmed: 38714898
doi: 10.1038/s41591-024-02951-6
pii: 10.1038/s41591-024-02951-6
doi:
Banques de données
ClinicalTrials.gov
['NCT05273268']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
Références
Micha, R. et al. Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and diabetes: systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE). PLoS ONE 12, e0175149 (2017).
pubmed: 28448503
pmcid: 5407851
Murray, C. J. L. et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1223–1249 (2020).
Berry, S. E. et al. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 26, 964–973 (2020).
pubmed: 32528151
pmcid: 8265154
Gardner, C. D. et al. Effect of low-fat vs low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion: the DIETFITS randomized clinical trial. JAMA 319, 667–679 (2018).
pubmed: 29466592
pmcid: 5839290
What We Eat in America, National Health and Nutrition Examination Survey (2017–2018) (National Center for Health Statistics, 2021); https://www.ars.usda.gov/ARSUserFiles/80400530/pdf/1718/Key%20Points%20Using%20WWEIA%20NHANES%202017-2018.pdf
Scheelbeek, P. et al. Health impacts and environmental footprints of diets that meet the Eatwell Guide recommendations: analyses of multiple UK studies. BMJ Open 10, e037554 (2020).
pubmed: 32847945
pmcid: 7451532
Ordovas, J.M., Ferguson, L. R., Tai, E. S. & Mathers, J. C. Personalised nutrition and health. BMJ 361, bmj.k2173 (2018).
pubmed: 29898881
pmcid: 6081996
Betts, J. A. & Gonzalez, J. T. Personalised nutrition: what makes you so special? Nutr. Bull. 41, 353–359 (2016).
Jinnette, R. et al. Does personalized nutrition advice improve dietary intake in healthy adults? A systematic review of randomized controlled trials. Adv. Nutr. 12, 657–669 (2021).
pubmed: 33313795
Celis-Morales, C., Lara, J. & Mathers, J. C. Personalising nutritional guidance for more effective behaviour change. Proc. Nutr. Soc. 74, 130–138 (2015).
pubmed: 25497396
Ben-Yacov, O. et al. Personalized postprandial glucose response-targeting diet versus Mediterranean diet for glycemic control in prediabetes. Diabetes Care 44, 1980–1991 (2021).
pubmed: 34301736
Li, M., Gong, W., Wang, S. & Li, Z. Trends in body mass index, overweight and obesity among adults in the USA, the NHANES from 2003 to 2018: a repeat cross-sectional survey. BMJ Open 12, e065425 (2022).
pubmed: 36526312
pmcid: 9764609
Asnicar, F. et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat. Med. 27, 321–332 (2021).
pubmed: 33432175
pmcid: 8353542
Thomas, A. M. et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25, 667–678 (2019).
pubmed: 30936548
pmcid: 9533319
Chawla, S., Tessarolo Silva, F., Amaral Medeiros, S., Mekary, R. A. & Radenkovic, D. The effect of low-fat and low-carbohydrate diets on weight loss and lipid levels: a systematic review and meta-analysis. Nutrients 12, 3774 (2020).
pubmed: 33317019
pmcid: 7763365
Livingstone, K. M. et al. Clustering of adherence to personalised dietary recommendations and changes in healthy eating index within the Food4Me study. Public Health Nutr. 19, 3296–3305 (2016).
pubmed: 27499187
pmcid: 10270865
Popp, C. J. et al. Effect of a personalized diet to reduce postprandial glycemic response vs a low-fat diet on weight loss in adults with abnormal glucose metabolism and obesity: a randomized clinical trial. JAMA Netw. Open 5, e2233760 (2022).
pubmed: 36169954
pmcid: 9520362
Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).
pubmed: 26590418
Aldubayan, M. 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: 35839545
Williamson, D. A., Bray, G. A. & Ryan, D. H. Is 5% weight loss a satisfactory criterion to define clinically significant weight loss? Obesity 23, 2319–2320 (2015).
pubmed: 26523739
Macek, P. et al. A two-year follow-up cohort study-improved clinical control over CVD risk factors through weight loss in middle-aged and older adults. J. Clin. Med. 9, 2904 (2020).
pubmed: 32911835
pmcid: 7565024
Jensen, M. D. et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Obesity Society. Circulation 129, S102–S138 (2014).
pubmed: 24222017
O’Donoghue, G., Blake, C., Cunningham, C., Lennon, O. & Perrotta, C. What exercise prescription is optimal to improve body composition and cardiorespiratory fitness in adults living with obesity? A network meta‐analysis. Obes. Rev. 22, e13137 (2021).
pubmed: 32896055
Wewege, M., van den Berg, R., Ward, R. E. & Keech, A. The effects of high‐intensity interval training vs. moderate‐intensity continuous training on body composition in overweight and obese adults: a systematic review and meta‐analysis. Obes. Rev. 18, 635–646 (2017).
pubmed: 28401638
Thorogood, A. et al. Isolated aerobic exercise and weight loss: a systematic review and meta-analysis of randomized controlled trials. Am. J. Med. 124, 747–755 (2011).
pubmed: 21787904
Schwingshackl, L., Dias, S., Strasser, B. & Hoffmann, G. Impact of different training modalities on anthropometric and metabolic characteristics in overweight/obese subjects: a systematic review and network meta-analysis. PLoS ONE 8, e82853 (2013).
pubmed: 24358230
pmcid: 3866267
Morze, J. et al. Impact of different training modalities on anthropometric outcomes in patients with obesity: a systematic review and network meta‐analysis. Obes. Rev. 22, e13218 (2021).
pubmed: 33624411
pmcid: 8244024
Ello-Martin, J. A., Roe, L. S., Ledikwe, J. H., Beach, A. M. & Rolls, B. J. Dietary energy density in the treatment of obesity: a year-long trial comparing 2 weight-loss diets. Am. J. Clin. Nutr. 85, 1465–1477 (2007).
pubmed: 17556681
Cani, P. D. Human gut microbiome: hopes, threats and promises. Gut 67, 1716–1725 (2018).
pubmed: 29934437
Talmor-Barkan, Y. et al. Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease. Nat. Med. 28, 295–302 (2022).
pubmed: 35177859
Sonnenburg, J. L. & Bäckhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).
pubmed: 27383980
pmcid: 5991619
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
pubmed: 24336217
Ben-Yacov, O. et al. Gut microbiome modulates the effects of a personalised postprandial-targeting (PPT) diet on cardiometabolic markers: a diet intervention in pre-diabetes. Gut 72, 1486–1496 (2023).
pubmed: 37137684
Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 10, e65088 (2021).
pubmed: 33944776
pmcid: 8096432
Sanders, M. E. & Marco, M. L. Food formats for effective delivery of probiotics. Annu. Rev. Food Sci. Technol. 1, 65–85 (2010).
pubmed: 22129330
Aguilera, J. M. The food matrix: implications in processing, nutrition and health. Crit. Rev. Food Sci. Nutr. 59, 3612–3629 (2019).
pubmed: 30040431
Forde, C. G. & Bolhuis, D. Interrelations between food form, texture, and matrix influence energy intake and metabolic responses. Curr. Nutr. Rep. 11, 124–132 (2022).
pubmed: 35325399
pmcid: 9174310
Tsereteli, N. et al. Impact of insufficient sleep on dysregulated blood glucose control under standardised meal conditions. Diabetologia 65, 356–365 (2022).
pubmed: 34845532
Gu, C. et al. Metabolic effects of late dinner in healthy volunteers—a randomized crossover clinical trial. J. Clin. Endocrinol. Metab. 105, 2789–2802 (2020).
pubmed: 32525525
pmcid: 7337187
Isherwood, C. M., van der Veen, D. R., Hassanin, H., Skene, D. J. & Johnston, J. D. Human glucose rhythms and subjective hunger anticipate meal timing. Curr. Biol. 33, 1321–1326 (2023).
pubmed: 36822203
Hutchison, A. T. et al. Time‐restricted feeding improves glucose tolerance in men at risk for type 2 diabetes: a randomized crossover trial. Obesity 27, 724–732 (2019).
pubmed: 31002478
Shukla, A. P. et al. The impact of food order on postprandial glycaemic excursions in prediabetes. Diabetes Obes. Metab. 21, 377–381 (2019).
pubmed: 30101510
Edinburgh, R. M., Betts, J. A., Burns, S. F. & Gonzalez, J. T. Concordant and divergent strategies to improve postprandial glucose and lipid metabolism. Nutr. Bull. 42, 113–122 (2017).
Shah, M. et al. Effect of a late afternoon/early evening bout of aerobic exercise on postprandial lipid and lipoprotein particle responses to a high-sugar meal breakfast the following day in postmenopausal women: a randomized cross-over study. J. Sports Sci. 40, 175–184 (2022).
pubmed: 34565292
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
pubmed: 22388286
pmcid: 3322381
Blanco-Míguez, A. et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat. Biotechnol. 41, 1633–1644 (2023).
pubmed: 36823356
pmcid: 10635831
Pehrsson P. et al. USDA Branded Food Products Database (USDA Agriculture Research Service, 2018). Accessed October 2022; https://data.nal.usda.gov/dataset/usda-branded-food-products-database
Schofield, W. N., Schofield, C. & James, W. P. T Basal Metabolic Rate: Review and Prediction, Together with an Annotated Bibliography of Source Material (J. Libbey, 1985) .
Reidlinger, D. P. et al. How effective are current dietary guidelines for cardiovascular disease prevention in healthy middle-aged and older men and women? A randomized controlled trial. Am. J. Clin. Nutr. 101, 922–930 (2015).
pubmed: 25787998