Journal
AMERICAN JOURNAL OF CLINICAL NUTRITION
Volume 109, Issue 3, Pages 626-634Publisher
OXFORD UNIV PRESS
DOI: 10.1093/ajcn/nqy262
Keywords
metabolomics; insulin resistance; type 2 diabetes; prediction; PREDIMED
Categories
Funding
- NIH [R01-DK-102896, F31-DK114938]
- official funding agency for biomedical research of the Spanish government
- Instituto de Salud Carlos III [RTIC G03/140, RTIC RD 06/0045]
- Instituto de Salud Carlos III through the Centro de Investigacion Biomedica en Red de Fisiopatologia de la Obesidad y Nutricion
- Centro Nacional de Investigaciones Cardiovasculares [CNIC 06/2007]
- Fondo de Investigacion Sanitaria Fondo Europeo de Desarrollo Regional [PI04-2239, PI 05/2584, CP06/00100, PI07/0240, PI07/1138, PI07/0954, PI07/0473, PI10/01407, PI10/02658, PI11/01647, P11/02505, PI13/00462]
- Ministerio de Ciencia e Innovacion [AGL-2009-13906-C02, SAF2016-80532-R, AGL2010-22319-C03]
- Fundacion Mapfre
- Consejeria de Salud de la Junta de Andalucia [PI0105/2007]
- Public Health Division of the Department of Health of the Autonomous Government of Catalonia
- Generalitat Valenciana [ACOMP06109, GVA-COMP2010-181, GVACOMP2011-151, CS2010-AP-111, PROMETEO17/2017, CS2011-AP-042]
- Regional Government of Navarra [P27/2011]
- Autonomous Government of Catalonia (PERIS 2016-2020 Incorporacio de Cientifics I Tecnolegs) [SLT002/0016/00428]
- American Heart Association [16POST31100031]
- European Foundation
Ask authors/readers for more resources
Background: Insulin resistance is a complex metabolic disorder and is often associated with type 2 diabetes (T2D). Objectives: The aim of this study was to test whether baseline metabolites can additionally improve the prediction of insulin resistance beyond classical risk factors. Furthermore, we examined whether a multimetabolite model predicting insulin resistance in nondiabetics can also predict incident T2D. Methods: We used a case-cohort study nested within the Prevencion con Dieta Mediterranea (PREDIMED) trial in subsets of 700, 500, and 256 participants without T2D at baseline and 1 and 3 y. Fasting plasma metabolites were semiquantitatively profiled with liquid chromatography-tandem mass spectrometry. We assessed associations between metabolite concentrations and the homeostasis model of insulin resistance (HOMA-IR) through the use of elastic net regression analysis. We subsequently examined associations between the baseline HOMA-IR-related multimetabolite model and T2D incidence through the use of weighted Cox proportional hazard models. Results: We identified a set of baseline metabolites associated with HOMA-IR. One-year changes in metabolites were also significantly associated with HOMA-IR. The area under the curve was significantly greater for the model containing the classical risk factors and metabolites together compared with classical risk factors alone at baseline [0.81 (95% CI: 0.79, 0.84) compared with 0.69 (95% CI: 0.66, 0.73)] and during a 1-y period [0.69 (95% CI: 0.66, 0.72) compared with 0.57 (95% CI: 0.53, 0.62)]. The variance in HOMA-IR explained by the combination of metabolites and classical risk factors was also higher in all time periods. The estimated HRs for incident T2D in the multimetabolite score (model 3) predicting high HOMA-IR (median value or higher) or HOMA-IR (continuous) at baseline were 2.00 (95% CI: 1.58, 2.55) and 2.24 (95% CI: 1.72, 2.90), respectively, after adjustment for T2D risk factors. Conclusions: The multimetabolite model identified in our study notably improved the predictive ability for HOMA-IR beyond classical risk factors and significantly predicted the risk of T2D.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available