Bayesian statistics – American ME and CFS Society

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

Abstract:

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power.

We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as “prior information” into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels.

Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients.

Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Source: Brydges C, Che X, Lipkin WI, Fiehn O. Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts. Metabolites. 2023 Aug 31;13(9):984. doi: 10.3390/metabo13090984. PMID: 37755264; PMCID: PMC10535181. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535181/ (Full text)

The aetiopathogenesis of fatigue: unpredictable, complex and persistent

Abstract:

BACKGROUND: Chronic fatigue syndrome is a common condition characterized by severe fatigue with post-exertional malaise, impaired cognitive ability, poor sleep quality, muscle pain, multi-joint pain, tender lymph nodes, sore throat or headache. Its defining symptom, fatigue is common to several diseases.

AREAS OF AGREEMENT: Research has established a broad picture of impairment across autonomic, endocrine and inflammatory systems though progress seems to have reached an impasse.

AREAS OF CONTROVERSY: The absence of a clear consensus view of the pathophysiology of fatigue suggests the need to switch from a focus on abnormalities in one system to an experimental and clinical approach which integrates findings across multiple systems and their constituent parts and to consider multiple environmental factors.

GROWING POINTS: We discuss this with reference to three key factors, non-determinism, non-reductionism and self-organization and suggest that an approach based on these principles may afford a coherent explanatory framework for much of the observed phenomena in fatigue and offers promising avenues for future research.

AREAS TIMELY FOR DEVELOPING RESEARCH: By adopting this approach, the field can examine issues regarding aetiopathogenesis and treatment, with relevance for future research and clinical practice.

Source: Clark JE, Fai Ng W, Watson S, Newton JL. The aetiopathogenesis of fatigue: unpredictable, complex and persistent. Br Med Bull. 2016 Mar;117(1):139-48. doi: 10.1093/bmb/ldv057. Epub 2016 Feb 12. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4782751/ (Full article)

Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome

Abstract:

BACKGROUND: The current practice of using only a few strongly associated genetic markers in regression models results in generally low power in prediction or accounting for heritability of complex human traits.

PURPOSE: We illustrate here a Bayesian joint estimation of single nucleotide polymorphism (SNP) effects principle to improve prediction of phenotype status from pathway-focused sets of SNPs. Chronic fatigue syndrome (CFS), a complex disease of unknown etiology with no laboratory methods for diagnosis, was chosen to demonstrate the power of this Bayesian method. For CFS, such a genetic predictive model in combination with clinical evidence might lead to an earlier diagnosis than one based solely on clinical findings.

METHODS: One of our goals is to model disease status using Bayesian statistics which perform variable selection and parameter estimation simultaneously and which can induce the sparseness and smoothness of the SNP effects. Smoothness of the SNP effects is obtained by explicit modeling of the covariance structure of the SNP effects.

RESULTS: The Bayesian model achieved perfect goodness of fit when tested within the sampled data. Tenfold cross-validation resulted in 80% accuracy, one of the best so far for CFS in comparison to previous prediction models. Model reduction aspects were investigated in a computationally feasible manner. Additionally, genetic variation estimates provided by the model identified specific genetic markers for their biological role in the disease pathophysiology.

CONCLUSIONS: This proof-of-principle study provides a powerful approach combining Bayesian methods, SNPs representing multiple pathways and rigorous case ascertainment for accurate genetic risk prediction modeling of complex diseases like CFS and other chronic diseases.

Source: Bhattacharjee M, Rajeevan MS, Sillanpää MJ. Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome. Hum Genomics. 2015 Jun 11;9:8. doi: 10.1186/s40246-015-0030-6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479222/ (Full article)