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. 2015 Jul;43(7):1467-76.
doi: 10.1097/CCM.0000000000000982.

Metabolic Profiling of Children Undergoing Surgery for Congenital Heart Disease

Goncalo D S Correia  1 , Keng Wooi Ng, Anisha Wijeyesekera, Sandra Gala-Peralta, Rachel Williams, S MacCarthy-Morrogh, Beatriz Jiménez, David Inwald, Duncan Macrae, Gary Frost, Elaine Holmes, Nazima Pathan
Affiliations

Metabolic Profiling of Children Undergoing Surgery for Congenital Heart Disease

Goncalo D S Correia et al. Crit Care Med. 2015 Jul.

Abstract

Objective: Inflammation and metabolism are closely interlinked. Both undergo significant dysregulation following surgery for congenital heart disease, contributing to organ failure and morbidity. In this study, we combined cytokine and metabolic profiling to examine the effect of postoperative tight glycemic control compared with conventional blood glucose management on metabolic and inflammatory outcomes in children undergoing congenital heart surgery. The aim was to evaluate changes in key metabolites following congenital heart surgery and to examine the potential of metabolic profiling for stratifying patients in terms of expected clinical outcomes.

Design: Laboratory and clinical study.

Setting: University Hospital and Laboratory.

Patients: Of 28 children undergoing surgery for congenital heart disease, 15 underwent tight glycemic control postoperatively and 13 were treated conventionally.

Interventions: Metabolic profiling of blood plasma was undertaken using proton nuclear magnetic resonance spectroscopy. A panel of metabolites was measured using a curve-fitting algorithm. Inflammatory cytokines were measured by enzyme-linked immunosorbent assay. The data were assessed with respect to clinical markers of disease severity (Risk Adjusted Congenital heart surgery score-1, Pediatric Logistic Organ Dysfunction, inotrope score, duration of ventilation and pediatric ICU-free days).

Measurements and main results: Changes in metabolic and inflammatory profiles were seen over the time course from surgery to recovery, compared with the preoperative state. Tight glycemic control did not significantly alter the response profile. We identified eight metabolites (3-D-hydroxybutyrate, acetone, acetoacetate, citrate, lactate, creatine, creatinine, and alanine) associated with surgical and disease severity. The strength of proinflammatory response, particularly interleukin-8 and interleukin-6 concentrations, inversely correlated with PICU-free days at 28 days. The interleukin-6/interleukin-10 ratio directly correlated with plasma lactate.

Conclusions: This is the first report on the metabolic response to cardiac surgery in children. Using nuclear magnetic resonance to monitor the patient journey, we identified metabolites whose concentrations and trajectory appeared to be associated with clinical outcome. Metabolic profiling could be useful for patient stratification and directing investigations of clinical interventions.

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Conflict of interest statement

Mr. Correia is supported by the Imperial College Stratified Medicine Graduate Training Programme in Systems Medicine and Spectroscopic Profiling (STRATiGRAD). Dr. Pathan’s institution received grant support from the British Heart Foundation (research grant) and a Higher Education Funding Council for England clinical senior lecturer award. Dr. Ng received grant support from the British Heart Foundation (Researcher salary) and received support for article research from the British Heart Foundation. Dr. Jimenez consulted for Metabometrix is employed by the Imperial College London, and received support for article research from the National Institutes of Health (NIH). Her institution received grant support from the Cardiovascular Biomedical Research Institute. Dr. Macrae is employed by Royal Brompton and Harefield NHS Foundation Trust. Dr. Holmes is employed by the Imperial College London and received support for article research. Her institution received grant support from the Imperial College London (unrelated research grants in the field of metabolic medicine). The remaining authors have disclosed that they do not have any potential conflicts of interest.

Figures

Figure 1.
Figure 1.
Principal component analysis score plots for the full nuclear magnetic resonance profile (A) and cytokine measurements (B). Coordinates are colored according to time of sampling and show systematic changes in both metabolite and cytokine profiles over time. Timepoint 1 (red): preop; timepoint 2 (orange): 0 hr postop; timepoint 3 (yellow): 6 hr postop; timepoint 4 (green): 24 hr postop; timepoint 5 (purple): 48 hr postop. We draw attention to this for two patients with different clinical severities by connecting the coordinates of their plasma profile in chronological order. The patient with mild disease (blue line) has a smaller trajectory than the patient with severe disease (red), who had a more deranged trajectory that did not recover to baseline by the end of the sampling period (48 hr post surgery).
Figure 2.
Figure 2.
Principal component analysis score plots for preoperative (cyan) and postoperative (red) detailing the variability in the full nuclear magnetic resonance profile (A), cytokine concentrations (B), and quantified metabolites (C).
Figure 3.
Figure 3.
Loadings for the first principal component, projected onto the median nuclear magnetic resonance spectra for both pre- and postsurgery samples: δ 4.3–0.6 region (A) and δ 8.1–5.1 region (B).
Figure 4.
Figure 4.
Pearson correlation heat maps for the presurgery (A), immediately postsurgery (B), and combined 6–48 hr postsurgery samples (C). Cells with opaque coloring specify correlations with a value of p < 0.05 (Student t test). Red cells indicate positive correlation and blue cells negative correlation.
See this image and copyright information in PMC

References

    1. Hoffman JI, Kaplan S. The incidence of congenital heart disease. J Am Coll Cardiol. 2002;39:1890–1900. - PubMed
    1. Gormley SM, McBride WT, Armstrong MA, et al. Plasma and urinary cytokine homeostasis and renal dysfunction during cardiac surgery. Anesthesiology. 2000;93:1210–1216; discussion 5A. - PubMed
    1. Brown MD, Wernovsky G, Mussatto KA, et al. Long-term and developmental outcomes of children with complex congenital heart disease. Clin Perinatol. 2005;32:1043–1057, xi. - PubMed
    1. Snookes SH, Gunn JK, Eldridge BJ, et al. A systematic review of motor and cognitive outcomes after early surgery for congenital heart disease. Pediatrics. 2010;125:e818–e827. - PubMed
    1. Allen ML, Hoschtitzky JA, Peters MJ, et al. Interleukin-10 and its role in clinical immunoparalysis following pediatric cardiac surgery. Crit Care Med. 2006;34:2658–2665. - PubMed

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