4.7 Article

An altered microbiota pattern precedes Type 2 diabetes mellitus development: From the CORDIOPREV study

期刊

JOURNAL OF ADVANCED RESEARCH
卷 35, 期 -, 页码 99-108

出版社

ELSEVIER
DOI: 10.1016/j.jare.2021.05.001

关键词

Intestinal microbiota; Type 2 diabetes mellitus; Predictive model; Coronary heart disease; CORDIOPREV

资金

  1. Ministerio de Economia y Competitividad [PIE14/00005, 14/00031, AGL2012/39615, AGL2015-67896-P, FIS PI13/00023, PI16/01777, FIS PI19/00299, DTS19/00007, CP14/00114]
  2. Fundacion Patrimonio Comunal Olivarero, Diputaciones de Jaen y Cordoba, Junta de Andalucia (Consejeria de Salud, Consejeria de Innovacion, Ciencia y Empresa Consejeria de Agricultura y Pesca,) , Ministerio de Medio Ambiente, Medio Rural y Marino, Gobierno
  3. Consejeria de Innovacion, Ciencia y Empresa
  4. Centro de Excelencia en Investigacion sobre Aceite de Oliva y Salud
  5. Junta de Andalucia (Proyectos de Investigacion de Excelencia) [CVI-7450]
  6. US Department of Agriculture [8050-51000-098-00D]

向作者/读者索取更多资源

The study identified a gut microbiome profile associated with the development of type 2 diabetes, and combining microbiome data with clinical parameters improved the prediction accuracy. The results suggest that the microbiome profile is linked to the development of T2DM and could be used for preventive measures.
Introduction: A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). Objectives: We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods: All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results: A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion: These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Cairo University.

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