A Diet-Dependent Microbiota Profile Associated with Incident Type 2 Diabetes: From the CORDIOPREV Study

被引:10
作者
Camargo, Antonio [1 ,2 ,3 ,4 ]
Vals-Delgado, Cristina [1 ,2 ,3 ,4 ]
Alcala-Diaz, Juan F. [1 ,2 ,3 ,4 ]
Villasanta-Gonzalez, Alejandro [1 ,2 ,3 ,4 ]
Gomez-Delgado, Francisco [1 ,2 ,3 ,4 ]
Haro, Carmen [5 ]
Leon-Acuna, Ana [1 ,2 ,3 ,4 ]
Cardelo, Magdalena P. [1 ,2 ,3 ,4 ]
Torres-Pena, Jose D. [1 ,2 ,3 ,4 ]
Guler, Ipek [6 ]
Malagon, Maria M. [2 ,4 ,7 ]
Ordovas, Jose M. [8 ,9 ,10 ]
Perez-Martinez, Pablo [1 ,2 ,3 ,4 ]
Delgado-Lista, Javier [1 ,2 ,3 ,4 ]
Lopez-Miranda, Jose [1 ,2 ,3 ,4 ]
机构
[1] Reina Sofia Univ Hosp, Lipids & Atherosclerosis Res Unit, Internal Med Unit, Cordoba 14004, Spain
[2] Maimonides Biomed Res Inst Cordoba IMIBIC, Cordoba, Spain
[3] Univ Cordoba, Dept Med, Cordoba 14004, Spain
[4] Inst Salud Carlos III, CIBER Fisiopatol Obesidad & Nutr CIBEROBN, Madrid 28029, Spain
[5] CSIC, Inst Sustainable Agr IAS, Cordoba 14080, Spain
[6] Maimonides Biomed Res Inst Cordoba IMIBIC, Unit Biostat, Cordoba 14004, Spain
[7] Univ Cordoba, Dept Cell Biol Physiol & Immunol, Cordoba 14071, Spain
[8] Tufts Univ, Nutr & Genom Lab, JM US Dept Agr Human Nutr Res Ctr, Boston, MA 02111 USA
[9] IMDEA Food Inst, Madrid 28049, Spain
[10] Spanish Natl Ctr Cardiovasc Res CNIC, Spain 28029, Spain
关键词
CORDIOPREV; diet; intestinal microbiota; predictive model; type; 2; diabetes; GUT MICROBIOTA; INSULIN-RESISTANCE; INTESTINAL MICROBIOTA; INTERVENTION; SENSITIVITY; PREVENTION; METAGENOME;
D O I
10.1002/mnfr.202000730
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Scope The differences between the baseline gut microbiota of patients who developed type 2 diabetes (T2D) consuming a low-fat (LF) or a Mediterranean (Med) diet are explored and risk scores are developed to predict the individual risk of developing T2D associated with the consumption of LF or Med diet. Methods and Results All the patients from the CORDIOPREV study without T2D at baseline (n = 462) whose fecal sample are available, are included. Gut microbiota is analyzed by 16S sequencing and the risk of T2D after a median follow-up of 60 months assessed by Cox analysis. Linear discriminant analysis effect size (LEfSe) analysis shows a different baseline gut microbiota in patients who developed T2D consuming LF and Med diets. A higher abundance of Paraprevotella, and lower Gammaproteobacteria and B. uniformis are associated with T2D risk when an LF diet is consumed. In contrast, higher abundances of Saccharibacteria, Betaproteobacteria, and Prevotella are associated with T2D risk when a Med diet is consumed. Conclusion The results suggest that different interactions between the microbiome and dietary patterns may partially determine the risk of T2D development, which may be used for selecting personalized dietary models to prevent T2D.
引用
收藏
页数:13
相关论文
共 48 条
  • [1] Amer Diabet Assoc, 2011, DIABETES CARE, V34, pS11, DOI [10.2337/dc11-S011, 10.2337/dc12-s064, 10.2337/dc13-S011, 10.2337/dc11-S062, 10.2337/dc13-S067, 10.2337/dc14-S081, 10.2337/dc10-S011, 10.2337/dc10-S062, 10.2337/dc12-s011]
  • [2] Human postprandial responses to food and potential for precision nutrition
    Berry, Sarah E.
    Valdes, Ana M.
    Drew, David A.
    Asnicar, Francesco
    Mazidi, Mohsen
    Wolf, Jonathan
    Capdevila, Joan
    Hadjigeorgiou, George
    Davies, Richard
    Al Khatib, Haya
    Bonnett, Christopher
    Ganesh, Sajaysurya
    Bakker, Elco
    Hart, Deborah
    Mangino, Massimo
    Merino, Jordi
    Linenberg, Inbar
    Wyatt, Patrick
    Ordovas, Jose M.
    Gardner, Christopher D.
    Delahanty, Linda M.
    Chan, Andrew T.
    Segata, Nicola
    Franks, Paul W.
    Spector, Tim D.
    [J]. NATURE MEDICINE, 2020, 26 (06) : 964 - +
  • [3] The insulin resistance phenotype (muscle or liver) interacts with the type of diet to determine changes in disposition index after 2 years of intervention: the CORDIOPREV-DIAB randomised clinical trial
    Blanco-Rojo, Ruth
    Alcala-Diaz, Juan F.
    Wopereis, Suzan
    Perez-Martinez, Pablo
    Quintana-Navarro, Gracia M.
    Marin, Carmen
    Ordovas, Jose M.
    van Ommen, Ben
    Perez-Jimenez, Francisco
    Delgado-Lista, Javier
    Lopez-Miranda, Jose
    [J]. DIABETOLOGIA, 2016, 59 (01) : 67 - 76
  • [4] Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2′s q2-feature-classifier plugin
    Bokulich, Nicholas A.
    Kaehler, Benjamin D.
    Rideout, Jai Ram
    Dillon, Matthew
    Bolyen, Evan
    Knight, Rob
    Huttley, Gavin A.
    Caporaso, J. Gregory
    [J]. MICROBIOME, 2018, 6
  • [5] Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
    Bolyen, Evan
    Rideout, Jai Ram
    Dillon, Matthew R.
    Bokulich, NicholasA.
    Abnet, Christian C.
    Al-Ghalith, Gabriel A.
    Alexander, Harriet
    Alm, Eric J.
    Arumugam, Manimozhiyan
    Asnicar, Francesco
    Bai, Yang
    Bisanz, Jordan E.
    Bittinger, Kyle
    Brejnrod, Asker
    Brislawn, Colin J.
    Brown, C. Titus
    Callahan, Benjamin J.
    Caraballo-Rodriguez, Andres Mauricio
    Chase, John
    Cope, Emily K.
    Da Silva, Ricardo
    Diener, Christian
    Dorrestein, Pieter C.
    Douglas, Gavin M.
    Durall, Daniel M.
    Duvallet, Claire
    Edwardson, Christian F.
    Ernst, Madeleine
    Estaki, Mehrbod
    Fouquier, Jennifer
    Gauglitz, Julia M.
    Gibbons, Sean M.
    Gibson, Deanna L.
    Gonzalez, Antonio
    Gorlick, Kestrel
    Guo, Jiarong
    Hillmann, Benjamin
    Holmes, Susan
    Holste, Hannes
    Huttenhower, Curtis
    Huttley, Gavin A.
    Janssen, Stefan
    Jarmusch, Alan K.
    Jiang, Lingjing
    Kaehler, Benjamin D.
    Bin Kang, Kyo
    Keefe, Christopher R.
    Keim, Paul
    Kelley, Scott T.
    Knights, Dan
    [J]. NATURE BIOTECHNOLOGY, 2019, 37 (08) : 852 - 857
  • [6] Saccharibacteria (TM7) in the Human Oral Microbiome
    Bor, B.
    Bedree, J. K.
    Shi, W.
    McLean, J. S.
    He, X.
    [J]. JOURNAL OF DENTAL RESEARCH, 2019, 98 (05) : 500 - 509
  • [7] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [8] Callahan BJ, 2016, NAT METHODS, V13, P581, DOI [10.1038/NMETH.3869, 10.1038/nmeth.3869]
  • [9] Postprandial endotoxemia may influence the development of type 2 diabetes mellitus: From the CORDIOPREV study
    Camargo, Antonio
    Jimenez-Lucena, Rosa
    Alcala-Diaz, Juan F.
    Rangel-Zuniga, Oriol A.
    Garcia-Carpintero, Sonia
    Lopez-Moreno, Javier
    Blanco-Rojo, Ruth
    Delgado-Lista, Javier
    Perez-Martinez, Pablo
    van Ommen, Ben
    Malagon, Maria M.
    Ordovas, Jose M.
    Perez-Jimenez, Francisco
    Lopez-Miranda, Jose
    [J]. CLINICAL NUTRITION, 2019, 38 (02) : 529 - 538
  • [10] Metabolic endotoxemia initiates obesity and insulin resistance
    Cani, Patrice D.
    Amar, Jacques
    Iglesias, Miguel Angel
    Poggi, Marjorie
    Knauf, Claude
    Bastelica, Delphine
    Neyrinck, Audrey M.
    Fava, Francesca
    Tuohy, Kieran M.
    Chabo, Chantal
    Waget, Aurelie
    Delmee, Evelyne
    Cousin, Beatrice
    Sulpice, Thierry
    Chamontin, Bernard
    Ferrieres, Jean
    Tanti, Jean-Francois
    Gibson, Glenn R.
    Casteilla, Louis
    Delzenne, Nathalie M.
    Alessi, Marie Christine
    Burcelin, Remy
    [J]. DIABETES, 2007, 56 (07) : 1761 - 1772