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

Area of Science:

• Biochemistry
• Metabolomics
• Spectroscopy

Background:

• Metabolomic profiling, integrating Nuclear Magnetic Resonance (NMR) spectroscopy and statistical analysis, offers insights into disease mechanisms.
• Understanding the metabolic alterations in porphyrias is crucial for disease prediction and hypothesis generation.

Purpose of the Study:

• To perform urinary metabolic profiling in individuals with porphyrias.
• To predict different porphyria types and explore novel pathophysiological hypotheses.

Main Methods:

• Compared urine 1H-NMR spectra from 73 patients with asymptomatic acute intermittent porphyria (aAIP) and porphyria cutanea tarda (PCT).
• Utilized a supervised rule-mining algorithm and logistic regression for spectral analysis.
• Extracted NMR spectrum buckets corresponding to predictive rules.

Main Results:

• The rule-mining algorithm yielded results consistent with partial least square discriminant analysis (PLS-DA), demonstrating significant predictive performance.
• Identified metabolites involved in glycolysis and energy metabolism, such as acetate, citrate, and pyruvate, at higher concentrations in aAIP urine compared to PCT.
• Metabolic profiling could not differentiate between familial (fPCT) and sporadic (sPCT) porphyria cutanea tarda.

Conclusions:

• Metabolic reprogramming is evident in asymptomatic aAIP individuals.
• These findings support a link between heme synthesis and mitochondrial energy metabolism in porphyrias.