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Azab SM, de Souza RJ, Lamri A, Shanmuganathan M, Kroezen Z, Schulze KM, Desai D, Williams NC, Morrison KM, Atkinson SA, Teo KK, Britz-McKibbin P, Anand SS. Metabolite profiles and the risk of metabolic syndrome in early childhood: a case-control study. BMC Med 2021; 19:292. [PMID: 34823524 PMCID: PMC8616718 DOI: 10.1186/s12916-021-02162-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 10/18/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Defining the metabolic syndrome (MetS) in children remains challenging. Furthermore, a dichotomous MetS diagnosis can limit the power to study associations. We sought to characterize the serum metabolite signature of the MetS in early childhood using high-throughput metabolomic technologies that allow comprehensive profiling of metabolic status from a biospecimen. METHODS In the Family Atherosclerosis Monitoring In earLY life (FAMILY) prospective birth cohort study, we selected 228 cases of MetS and 228 matched controls among children age 5 years. In addition, a continuous MetS risk score was calculated for all 456 participants. Comprehensive metabolite profiling was performed on fasting serum samples using multisegment injection-capillary electrophoresis-mass spectrometry. Multivariable regression models were applied to test metabolite associations with MetS adjusting for covariates of screen time, diet quality, physical activity, night sleep, socioeconomic status, age, and sex. RESULTS Compared to controls, thirteen serum metabolites were identified in MetS cases when using multivariable regression models, and using the quantitative MetS score, an additional eight metabolites were identified. These included metabolites associated with gluconeogenesis (glucose (odds ratio (OR) 1.55 [95% CI 1.25-1.93]) and glutamine/glutamate ratio (OR 0.82 [95% CI 0.67-1.00])) and the alanine-glucose cycle (alanine (OR 1.41 [95% CI 1.16-1.73])), amino acids metabolism (tyrosine (OR 1.33 [95% CI 1.10-1.63]), threonine (OR 1.24 [95% CI 1.02-1.51]), monomethylarginine (OR 1.33 [95% CI 1.09-1.64]) and lysine (OR 1.23 [95% CI 1.01-1.50])), tryptophan metabolism (tryptophan (OR 0.78 [95% CI 0.64-0.95])), and fatty acids metabolism (carnitine (OR 1.24 [95% CI 1.02-1.51])). The quantitative MetS risk score was more powerful than the dichotomous outcome in consistently detecting this metabolite signature. CONCLUSIONS A distinct metabolite signature of pediatric MetS is detectable in children as young as 5 years old and may improve risk assessment at early stages of development.
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Affiliation(s)
- Sandi M Azab
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Russell J de Souza
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.,Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON, Canada.,Population Health Research Institute, Hamilton, ON, Canada
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, ON, Canada.,Population Health Research Institute, Hamilton, ON, Canada
| | - Meera Shanmuganathan
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, ON, Canada
| | - Zachary Kroezen
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, ON, Canada
| | | | - Dipika Desai
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.,Population Health Research Institute, Hamilton, ON, Canada
| | | | - Katherine M Morrison
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON, Canada.,Population Health Research Institute, Hamilton, ON, Canada.,Department of Pediatrics, McMaster University, Hamilton, ON, Canada
| | | | - Koon K Teo
- Department of Medicine, McMaster University, Hamilton, ON, Canada.,Population Health Research Institute, Hamilton, ON, Canada
| | - Philip Britz-McKibbin
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, ON, Canada
| | - Sonia S Anand
- Department of Medicine, McMaster University, Hamilton, ON, Canada. .,Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada. .,Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON, Canada. .,Population Health Research Institute, Hamilton, ON, Canada.
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Arzeno NM, Lawson KA, Duzinski SV, Vikalo H. Designing optimal mortality risk prediction scores that preserve clinical knowledge. J Biomed Inform 2015; 56:145-56. [PMID: 26056073 DOI: 10.1016/j.jbi.2015.05.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2014] [Revised: 05/26/2015] [Accepted: 05/28/2015] [Indexed: 10/23/2022]
Abstract
Many in-hospital mortality risk prediction scores dichotomize predictive variables to simplify the score calculation. However, hard thresholding in these additive stepwise scores of the form "add x points if variable v is above/below threshold t" may lead to critical failures. In this paper, we seek to develop risk prediction scores that preserve clinical knowledge embedded in features and structure of the existing additive stepwise scores while addressing limitations caused by variable dichotomization. To this end, we propose a novel score structure that relies on a transformation of predictive variables by means of nonlinear logistic functions facilitating smooth differentiation between critical and normal values of the variables. We develop an optimization framework for inferring parameters of the logistic functions for a given patient population via cyclic block coordinate descent. The parameters may readily be updated as the patient population and standards of care evolve. We tested the proposed methodology on two populations: (1) brain trauma patients admitted to the intensive care unit of the Dell Children's Medical Center of Central Texas between 2007 and 2012, and (2) adult ICU patient data from the MIMIC II database. The results are compared with those obtained by the widely used PRISM III and SOFA scores. The prediction power of a score is evaluated using area under ROC curve, Youden's index, and precision-recall balance in a cross-validation study. The results demonstrate that the new framework enables significant performance improvements over PRISM III and SOFA in terms of all three criteria.
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Affiliation(s)
- Natalia M Arzeno
- Department of Electrical and Computer Engineering, The University of Texas at Austin, 1 University Station C0803, Austin, TX 78712, USA.
| | - Karla A Lawson
- Trauma Services, Dell Children's Medical Center of Central Texas, 4900 Mueller Blvd., Austin, TX 78723, USA.
| | - Sarah V Duzinski
- Trauma Services, Dell Children's Medical Center of Central Texas, 4900 Mueller Blvd., Austin, TX 78723, USA.
| | - Haris Vikalo
- Department of Electrical and Computer Engineering, The University of Texas at Austin, 1 University Station C0803, Austin, TX 78712, USA.
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