Patterns of 'Analytical Irreproducibility' in Multimodal Diseases

Abstract

Background: Multimodal diseases are those in which affected individuals can be divided into subtypes (or ‘data modes’); for instance, ‘mild’ vs. ‘severe’, based on (unknown) modifiers of disease severity exemplified in the majority of microbiome-mediated human diseases. Studies have shown that despite the inclusion of a large number of subjects, the causal role of the microbiome in human diseases remains uncertain. The role of the microbiome in multimodal diseases has been studied in animals; however, findings are often deemed irreproducible, or unreasonably biased, with pathogenic roles in 95% of reports. As a solution to repeatability, investigators have been recently recommended to seek funds to increase the number of human-microbiome donors (N) to increase the reproducibility of animal studies. Herein, we outline the constraints of such recommendation. Results: Using published (observed) mean±SD microbiome data from human gut microbiota (hGM)-associated rodent studies, we illustrate through a series of simulations, that increasing N will not uniformly/universally enable the identification of consistent statistical differences (patterns of analytical irreproducibility), due to random sampling from a population with ample variability in disease and the presence of ‘disease data subtypes’ (or modes). To visualize data distribution, we used kernel-density-violin plots (rarely used in rodent studies; 0%, 0/38, 95%CI=6.9e-18,9.1) as a method to identify ‘disease data subtypes’. We also found that hGM preclinical rodent studies do not use cluster statistics when needed (97.4%, 37/38, 95%CI=86.5,99.5), and that scientists who increased N, concurrently reduced the number of mice/donor ( y =-0.21 x, R 2 =0.24 ; and vice versa), indicating that statistically, scientists replace the disease variance in mice by the variance of human disease in their studies. Conclusion: Instead of assuming that increasing N will solve reproducibility and identify clinically-predictive findings on causality in preclinical microbiome studies, we propose the visualization of data distribution using kernel-density’-violin plots to identify ‘disease data subtypes’ to self-correct, guide and promote the personalized investigation of disease subtype mechanisms.

Autologous fecal microbiota transplantation for the treatment of inflammatory bowel disease

“…However, the causal connection between the gut microbiome and the pathogenesis of human diseases remains uncertain, as pathogenicity studies with animal models have been deemed implausibly biased, "exaggerating causality." 2 Irrespective of the complexity of understanding of the role of the gut microbiome in complex multimodal diseases, 3 fecal microbiota transplantation (FMT), where feces from a 'healthy" donor are transplanted into a patient (''heterologous-FMT"; h-FMT), has been increasingly investigated as a treatment option to help restore the functional balance between pro-and antiinflammatory microbial communities. In clinical practice, h-FMT has proven highly successful for the treatment of recurrent Clostridioides difficile infection (CDI), with cure rates of up to 90%.…”

“…Although a-FMT holds therapeutic potential, it is possible that with the multi-factorial nature of IBD, a patients' own stool would not be able to effectively treat 100% of cases. IBD is a multimodal disease 3 (not infectious, and more predictable and responsive to FMT as with CDI) for which the complex nature of disease severity, including the person-specific temporal and phenotypic variability in gut microbiota, are analytically challenging to study and interpret. We have extensively discussed the multimodality of disease subtypes underlying IBD, from analytical perspective, elsewhere.…”

“…We have extensively discussed the multimodality of disease subtypes underlying IBD, from analytical perspective, elsewhere. 3…”

Gut Microbiota Metabolite Fights Against Dietary Polysorbate 80-Aggravated Radiation Enteritis

“…The statistical tests were clarified in related legends. Kernel-density-violin plots were used to determine the sample sizes and data distribution ( Basson et al, 2020a ).…”

Germ-Free Mice Under Two-Layer Textiles Are Fully Protected From Bacteria in Sprayed Microdroplets: A Functional in vivo Test Method of Facemask/Filtration Materials

“…Protocols on animal handling, study designs, and housing were approved by the IACUC at CWRU in accordance with the National Research Council Guide for the Care and Use of Laboratory Animals ( 26 ). To promote rigor and analytical reproducibility ( 24 ), GF animals were individually caged, eliminating the need to control for cyclical bias ( 23 ) or cage-clustered data ( 27 ).…”