Statistical methods and resources for biomarker discovery using metabolomics.

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

The retrospective study of the metabolic patterns of BCG-vaccination in type-2 diabetic individuals in COVID-19 infection.

Background: The cross-protective nature of Bacillus Calmette-Guerin (BCG) vaccine against SARS-CoV-2 virus was previously suggested, however its effect in COVID-19 patients with type 2 diabetes (T2D) and the underlying metabolic pathways has not been addressed. This study aims to investigate the difference in the metabolomic patterns of type 2 diabetic patients with BCG vaccination showing different severity levels of COVID-19 infection. Methods: Sixty-seven COVID-19 patients were categorized into diabetic and non-diabetic individuals who had been previously vaccinated or not with BCG vaccination. Targeted metabolomics were performed from serum samples from all patients using tandem mass spectrometry. Statistical analysis included multivariate and univariate models. Results: Data suggested that while BCG vaccination may provide protection for individuals who do not have diabetes, it appears to be linked to more severe COVID-19 symptoms in T2D patients (p = 0.02). Comparing the metabolic signature of BCG vaccinated T2D individuals to non-vaccinated counterparts revealed that amino acid (sarcosine), cholesterol esters (CE 20:0, 20:1, 22:2), carboxylic acid (Aconitic acid) were enriched in BCG vaccinated T2D patients, whereas spermidine, glycosylceramides (Hex3Cer(d18:1_22:0), Hex2Cer(d18:1/22:0), HexCer(d18:1/26:1), Hex2Cer(d18:1/24:0), HexCer(d18:1/22:0) were higher in BCG vaccinated non- T2D patients. Furthermore, data indicated a decrease in sarcosine synthesis from glycine and choline and increase in spermidine synthesis in the BCG vaccinated cohort in T2D and non-T2D groups, respectively. Conclusion: This pilot study suggests increased severity of COVID-19 in BCG vaccinated T2D patients, which was marked by decreased sarcosine synthesis, perhaps via lower sarcosine-mediated removal of viral antigens.

Comparing metabolic profiles between female endurance athletes and non-athletes reveals differences in androgen and corticosteroid levels.

Endurance training is associated with physiological changes in elite athletes, but little is known about female-specific effects of endurance training. Despite the significant rise in female sports participation, findings from studies performed on male athletes are largely extrapolated to females without taking into consideration sex-specific differences in metabolism. Subsequently, this study aimed to investigate the steroid hormone profiles of elite female endurance athletes in comparison with their non-athletic counterparts. Untargeted metabolomics-based mass spectroscopy combined with ultra-high-performance liquid chromatography was performed on serum samples from 51 elite female endurance athletes and 197 non-athletic females. The results showed that, compared to non-athletic females, certain androgen, pregnenolone, and progestin steroid hormones were reduced in elite female endurance athletes, while corticosteroids were elevated. The most significantly altered steroid hormones were 5alpha-androstan-3alpha,17alpha-diol monosulfate (FDR = 1.90 × 10-05), androstenediol (3alpha, 17alpha) monosulfate (FDR = 2.93 × 10-04), and cortisol (FDR = 2.93 × 10-04). Conclusively, the present study suggests that elite female endurance athletes have a unique steroid hormone profile with implications on their general health and performance.