Clinical and metabolomic predictors of regression to normoglycemia in a population at intermediate cardiometabolic risk

被引:14
|
作者
Sevilla-Gonzalez, Magdalena del Rocio [1 ,2 ,3 ,4 ,5 ,6 ]
Merino, Jordi [2 ,3 ,5 ,7 ,8 ]
Moreno-Macias, Hortensia [9 ]
Rojas-Martinez, Rosalba [10 ]
Gomez-Velasco, Donaji Veronica [6 ]
Manning, Alisa K. [1 ,2 ,3 ,5 ]
机构
[1] Massachusetts Gen Hosp, Clin & Translat Epidemiol Unit, 100 Cambridge, Boston, MA 02114 USA
[2] Broad Inst MIT & Harvard, Program Metab, Cambridge, MA 02142 USA
[3] Broad Inst MIT & Harvard, Program Med & Populat Genet, Cambridge, MA 02142 USA
[4] Univ Nacl Autonoma Mexico, Doctoral Program Hlth Sci, Mexico City, DF, Mexico
[5] Harvard Med Sch, Dept Med, Boston, MA 02115 USA
[6] Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Unidad Invest Enfermedades Metab, Mexico City, DF, Mexico
[7] Massachusetts Gen Hosp, Diabet Unit, Boston, MA 02114 USA
[8] Massachusetts Gen Hosp, Ctr Genom Med, Boston, MA 02114 USA
[9] Univ Autonoma Metropolitana, Mexico City, DF, Mexico
[10] Inst Nacl Salud Publ, Mexico City, DF, Mexico
基金
欧盟地平线“2020”; 美国国家卫生研究院;
关键词
Dysglycemia; Regression to normoglycemia; Metabolomics; Cardiometabolic risk; NORMAL GLUCOSE REGULATION; IMPAIRED FASTING GLUCOSE; DIABETES PREVENTION; CARDIOVASCULAR-DISEASE; TYPE-2; OVERWEIGHT; COMPLICATIONS; PARTICIPANTS; METAANALYSIS; METABOLITES;
D O I
10.1186/s12933-021-01246-1
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
BackgroundImpaired fasting glucose (IFG) is a prevalent and potentially reversible intermediate stage leading to type 2 diabetes that increases risk for cardiometabolic complications. The identification of clinical and molecular factors associated with the reversal, or regression, from IFG to a normoglycemia state would enable more efficient cardiovascular risk reduction strategies. The aim of this study was to identify clinical and biological predictors of regression to normoglycemia in a non-European population characterized by high rates of type 2 diabetes.MethodsWe conducted a prospective, population-based study among 9637 Mexican individuals using clinical features and plasma metabolites. Among them, 491 subjects were classified as IFG, defined as fasting glucose between 100 and 125 mg/dL at baseline. Regression to normoglycemia was defined by fasting glucose less than 100 mg/dL in the follow-up visit. Plasma metabolites were profiled by Nuclear Magnetic Resonance. Multivariable cox regression models were used to examine the associations of clinical and metabolomic factors with regression to normoglycemia. We assessed the predictive capability of models that included clinical factors alone and models that included clinical factors and prioritized metabolites.ResultsDuring a median follow-up period of 2.5 years, 22.6% of participants (n=111) regressed to normoglycemia, and 29.5% progressed to type 2 diabetes (n=145). The multivariate adjusted relative risk of regression to normoglycemia was 1.10 (95% confidence interval [CI] 1.25 to 1.32) per 10 years of age increase, 0.94 (95% CI 0.91-0.98) per 1 SD increase in BMI, and 0.91 (95% CI 0.88-0.95) per 1 SD increase in fasting glucose. A model including information from age, fasting glucose, and BMI showed a good prediction of regression to normoglycemia (AUC=0.73 (95% CI 0.66-0.78). The improvement after adding information from prioritized metabolites (TG in large HDL, albumin, and citrate) was non-significant (AUC=0.74 (95% CI 0.68-0.80), p value=0.485).ConclusionIn individuals with IFG, information from three clinical variables easily obtained in the clinical setting showed a good prediction of regression to normoglycemia beyond metabolomic features. Our findings can serve to inform and design future cardiovascular prevention strategies.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Clinical and metabolomic predictors of regression to normoglycemia in a population at intermediate cardiometabolic risk
    Magdalena del Rocío Sevilla-González
    Jordi Merino
    Hortensia Moreno-Macias
    Rosalba Rojas-Martínez
    Donají Verónica Gómez-Velasco
    Alisa K. Manning
    [J]. Cardiovascular Diabetology, 20
  • [2] BIOMARKERS OF THE FUTURE: METABOLOMIC PREDICTORS OF CARDIOMETABOLIC DISEASE
    Cheng, S.
    [J]. CARDIOLOGY, 2016, 134 : 405 - 405
  • [3] Predictors of Cardiometabolic Risk
    不详
    [J]. FINLAY, 2015, 5 (02): : 81 - 82
  • [4] Linking of metabolomic biomarkers with cardiometabolic health in Chinese population
    Sun, Liang
    Li, Huaixing
    Lin, Xu
    [J]. JOURNAL OF DIABETES, 2019, 11 (04) : 280 - 291
  • [5] The Efficacy of Clinical Predictors for Patients with Intermediate Risk of Choledocholithiasis
    Kang, Jingu
    Paik, Kyu-hyun
    Lee, Jong-chan
    Kim, Hyoung Woo
    Lee, Jongchan
    Hwang, Jin-Hyeok
    Kim, Jaihwan
    [J]. DIGESTION, 2016, 94 (02) : 100 - 105
  • [6] Cardiometabolic predictors of high-risk CCTA phenotype in a diverse patient population
    Kuno, Toshiki
    Arce, Javier
    Fattouh, Michael
    Sarkar, Sharmila
    Skendelas, John P.
    Daich, Jonathan
    Schenone, Aldo L.
    Zhang, Lili
    Rodriguez, Carlos J.
    Virani, Salim S.
    Slomka, Piotr J.
    Shaw, Leslee J.
    Williamson, Eric E.
    Berman, Daniel S.
    Garcia, Mario J.
    Dey, Damini
    Slipczuk, Leandro
    [J]. AMERICAN JOURNAL OF PREVENTIVE CARDIOLOGY, 2023, 15
  • [7] On Cardiometabolic Risk Predictors: a Necessary Explanation
    Cedeno Morales, Raul
    [J]. FINLAY, 2015, 5 (03): : 145 - 146
  • [8] Cardiometabolic predictors of quantitative high-risk plaque features in a diverse patient population
    Arce, J.
    Kuno, T.
    Fattouh, M.
    Sarkar, S.
    Skendelas, J.
    Daich, J.
    Schenone, A.
    Zhang, L.
    Slomka, P. J.
    Shaw, L. J.
    Williamson, E.
    Berman, D. S.
    Garcia, M. J.
    Dey, D.
    Slipczuk, L.
    [J]. EUROPEAN HEART JOURNAL, 2022, 43 : 215 - 215
  • [9] Intermediate-risk pulmonary embolism: echocardiography predictors of clinical deterioration
    Weekes, Anthony J.
    Fraga, Denise N.
    Belyshev, Vitaliy
    Bost, William
    Gardner, Christopher A.
    O'Connell, Nathaniel S.
    [J]. CRITICAL CARE, 2022, 26 (01)
  • [10] Intermediate-risk pulmonary embolism: echocardiography predictors of clinical deterioration
    Anthony J. Weekes
    Denise N. Fraga
    Vitaliy Belyshev
    William Bost
    Christopher A. Gardner
    Nathaniel S. O’Connell
    [J]. Critical Care, 26