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. 2018;14(1):10.
doi: 10.1007/s11306-017-1305-9. Epub 2017 Dec 4.

Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm

Margaux Luck  1   2   3   4 , Caroline Schmitt  5   6   7 , Neila Talbi  5   6   7 , Laurent Gouya  5   6   7 , Cédric Caradeuc  2   3   8 , Hervé Puy  5   6   7 , Gildas Bertho  2   3   8 , Nicolas Pallet  9   10   11   12
Affiliations

Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm

Margaux Luck et al. Metabolomics. 2018.

Erratum in

Abstract

Introduction: Metabolomic profiling combines Nuclear Magnetic Resonance spectroscopy with supervised statistical analysis that might allow to better understanding the mechanisms of a disease.

Objectives: In this study, the urinary metabolic profiling of individuals with porphyrias was performed to predict different types of disease, and to propose new pathophysiological hypotheses.

Methods: Urine 1H-NMR spectra of 73 patients with asymptomatic acute intermittent porphyria (aAIP) and familial or sporadic porphyria cutanea tarda (f/sPCT) were compared using a supervised rule-mining algorithm. NMR spectrum buckets bins, corresponding to rules, were extracted and a logistic regression was trained.

Results: Our rule-mining algorithm generated results were consistent with those obtained using partial least square discriminant analysis (PLS-DA) and the predictive performance of the model was significant. Buckets that were identified by the algorithm corresponded to metabolites involved in glycolysis and energy-conversion pathways, notably acetate, citrate, and pyruvate, which were found in higher concentrations in the urines of aAIP compared with PCT patients. Metabolic profiling did not discriminate sPCT from fPCT patients.

Conclusion: These results suggest that metabolic reprogramming occurs in aAIP individuals, even in the absence of overt symptoms, and supports the relationship that occur between heme synthesis and mitochondrial energetic metabolism.

Keywords: 1H NMR; Biomarkers; Porphyrias; Subgroup discovery.

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Conflict of interest statement

Compliance with ethical standardsAll of the authors declare that they have no conflict of interest.The Paris Diderot University ethics committee (Patients Protection Committee) approved the study design in accordance with the World Medical Association ethical principles for medical research involving human subjects and its subsequent amendments (R162-16-7 and 145-15-4 French ethical agreement).Informed consent was obtained from all individual participants included in the study.

Figures

Fig. 1
Fig. 1
Representation of the 1D rules for the discovery cohort dataset. Each horizontal segment corresponds to a 1D rule characterized by its variable condition: the bucket’s name and the set of covered bins. We only show the rules corresponding to buckets for which rules could be generated for the two classes (i.e., aAIP and s/fPCT). The color scale reflects the frequency of the buckets values covered by the rules over the leave-one-out splits. The more robust the rule, the darker it will be. On the figures on the left a, the rules corresponding to the s/fPCT class and on the right figures, the rules corresponding to the aAIP class b
Fig. 2
Fig. 2
Variables of influence in the projection of the first component (VIP[1]) of the PLS-DA. The bar corresponds to the normalized mean weights of the most discriminative variables in the projection of the first component of the PLS-DA models. The standard errors of the weights are indicated on the figure
Algorithm 1
Algorithm 1
Supervised rule mining
See this image and copyright information in PMC

References

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