The aforementioned treatments do show potential. However not enough research has been done on any of them, and unfortunately while there are a lot of small studies on GMBD, a more detailed analysis is needed. Existing data may provide additional insights through pooled analysis. The data may also be potentially be useful in the construction of a diagnostic tool to identify dysbiosis and its related conditions. However, given that it has been fairly well established that dysbiosis is something which needs treating, additional clinical studies on potential treatments need to be conducted.
Not only are new studies with more uniform methodology and larger samples called for, but meta-analyses of existing studies may provide additional insight. Narrow meta-analyses do exist. One example is a meta analysis combining studies on gut microbes associated with obesity and IBD (Walters, Xu, & Knight 2014), but such a limited scope is not enough. Studies looking into the relationship between GMBD and illnesses, like the ones discussed in this paper, can be pooled together by placing all illnesses into a single category of “negative health.” This would create two large sample groups, one “healthy” and the other “unhealthy.” This kind of study is different from a simple meta-analysis of very similar papers that look GMBD with respect to the same condition. GMB dysbiosis would be treated as a binary explanatory variable and whether or not there was any improvement in health condition would be the binary outcome.
If using the pooled data set mentioned above, one if the most straightforward analyses that could be conducted is to see if there is a general relationship between the level of intragut diversity and health outcomes. One of the simplest meta-analyses that could be conducted on a combined data set is to identify the Shannon index for healthy and unhealthy individuals and see what kind of relationship, if any, exists. Alternatively, or in addition, to the first method discussed, PCoA and ANOSIM could be used to determine if distinct signatures in microbial composition exist for different conditions, much the same way they have been used to identify bacterial signatures in different phyla of bees and the lack of distinct signatures in geographic clusters of bees (Kwong et al. 2017).
In order to add more weight to the idea that GMBD can be used at least in diagnosis, we could rely on machine learning. A neural network could be trained with genomic data and health outcomes and tested for predictive power. If the predictive power is high, that would introduce a new powerful tool for diagnosing GMBD its associated health problems. Neural networks are useful because they can identify highly non-linear relationships between input and output variables. In this case, the inputs would be the OTU level data and the output nodes would be the health issues. Once trained, if there is a strong relationship between specific health outcomes and dysbiosis, the neural network should be able to identify whether a person is healthy or afflicted with a given outcome, simply by inputting the OTU level data. There are enough studies available to form a reasonable data set. Lambeth et al. (2015), Rebolledo et al. (2017), Li et al. (2017), and Vogt et al. (2017) should have accessible biodiversity data. However these studies are not consistent in their methodologies. Pains would have to be taken to make sure that the data sets can be integrated into a single data pool. This problem should already be solved by the end of the meta-analysis. The basic model used will be a basic feed forward neural network with the number of input nodes equal to the total number of identified OTUs, the output layer equal to the number of identified related conditions, plus one additional output node to indicate healthy or unhealthy, and the number of hidden nodes equal to the average of the input and output nodes. To protect against over-fitting, k-fold cross validation will be utilized, where each partition will be generated using simple random sampling.
Finally, because multiple herbal supplements have been indicated as being able to shift the GMB, further analysis into combined treatment, including a phase III clinical trial involving placebo control and double blinding should be performed to identify how curcumin, capsaicin, and allicin interact to affect the gut microbiome. All of these treatments, on their own, appear to have relatively few negative health effects, though capsaicin can be an irritant in higher quantities (Aggarwal et al. 2013, “Final report on the safety assessment of capsicum, ” 2007, Bayan et al. 2014). They also seem to have positive effects on restoring the GMB. However, it is not known however, whether these treatments can impede or improve the effects of each other. A proposed trial would separate dysbiotic individuals into eight groups: factorial trials with two levels—treatment vs no treatment—and k factors results in 2k arms (Baker et al., 2017). One group would receive only placebo pills, three groups would receive one of the active components and two placebo pills, three groups would receive two of the active components and one placebo pill, and one group would receive all three treatments and no placebo. This method would allow for the comparison of each proposed treatment individually, and would give insight into synergistic and antagonistic interactions. The reason for using placebo pills in each arm is that it should eliminate the risk of compounding placebo effects skewing the results of the study. This decision also allows for full blinding, which would not be possible if the number of pills differed between trial arms.
One final concern that needs to be addressed in future research is spatial diversity in the GMB and inability to measure what species are present in different regions of the gut. For instance, at least based on studies in dogs, the microbiota found in the small intestines varies considerably from the samples available through fecal analysis (Mentula et al. 2005). An inability to get a complete picture of the GMB from fecal samples alone does limit just how well we can identify GMBD and future technological advances may provide better testing capabilities. Unfortunately suggestions on how to go about producing technology that can access information on the rest of our microbiomes is beyond the scope of this paper.
Overall, the human gut microbiome is a complicated piece of biological machinery that fulfils many functions. A failure in the GMB appears to be both the result of and a cause of many other health concerns, including diabetes, irritable bowel syndrome, colon cancer, multiple sclerosis, and even alzheimer’s disease.There are numerous potential treatments for gut dysbiosis but further research may reveal additional treatments and diagnostic tools. Given how many diseases are related to dysbiosis, significant resources should be put into conducting these studies.
FMT – Fecal material transplant
GMB – Gut microbiome
GMBD – Gut microbiome dysbiosis
OTU – Operational taxonomic unit
Baker, T., Smith, S., Bolt, D., Loh, W., Mermelstein, R., & Fiore, M. et al. (2017). Implementing Clinical Research Using Factorial Designs: A Primer. Behavior Therapy, 48(4), 567-580. http://dx.doi.org/10.1016/j.beth.2016.12.005
Bamola, V. D., Ghosh, A., Kapardar, R. K., Lal, B., Cheema, S., Sarma, P., & Chaudhry, R. (2017). Gut microbial diversity in health and disease: experience of healthy Indian subjects, and colon carcinoma and inflammatory bowel disease patients. Microbial Ecology in Health and Disease, 28(1), 1322447. http://doi.org/10.1080/16512235.2017.1322447
Cekanaviciute, E., Yoo, B., Runia, T., Debelius, J., Singh, S., & Nelson, C. et al. (2017). Gut bacteria from multiple sclerosis patients modulate human T cells and exacerbate symptoms in mouse models. Proceedings Of The National Academy Of Sciences, 114(40), 10713-10718. http://dx.doi.org/10.1073/pnas.1711235114
Day, J. A. (2017, April 26). Fecal Transplantation (Bacteriotherapy). Retrieved December 04, 2017, from https://www.hopkinsmedicine.org/gastroenterology_hepatology/clinical_services/advanced_endoscopy/fecal_transplantation.html
Kang, C., Wang, B., Kaliannan, K., Wang, X., Lang, H., & Hui, S. et al. (2017). Gut Microbiota Mediates the Protective Effects of Dietary Capsaicin against Chronic Low-Grade Inflammation and Associated Obesity Induced by High-Fat Diet. Mbio, 8(3), e00470-17. http://dx.doi.org/10.1128/mbio.00470-17
Kwong, W. K., Medina, L. A., Koch, H., Sing, K.-W., Soh, E. J. Y., Ascher, J. S., … Moran, N. A. (2017). Dynamic microbiome evolution in social bees. Science Advances, 3(3), e1600513. http://doi.org/10.1126/sciadv.1600513
Li, J., Zhao, F., Wang, Y., Chen, J., Tao, J., & Tian, G. et al. (2017). Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome, 5(1). http://dx.doi.org/10.1186/s40168-016-0222-x
Pannaraj PS, Li F, Cerini C & et al (2017). Association between breast milk bacterial communities and establishment and development of the infant gut microbiome. JAMA Pediatrics, 171, 647-654.
Rebolledo, C., Cuevas, A., Zambrano, T., Acuña, J., Jorquera, M., & Saavedra, K. et al. (2017). Bacterial Community Profile of the Gut Microbiota Differs between Hypercholesterolemic Subjects and Controls. Biomed Research International, 2017, 1-6. http://dx.doi.org/10.1155/2017/8127814
Turmeric. (2017). University of Maryland Medical Center. Retrieved 4 December 2017, from http://www.umm.edu/health/medical/altmed/herb/turmeric
Vogt, N., Kerby, R., Dill-McFarland, K., Harding, S., Merluzzi, A., & Johnson, S. et al. (2017). Gut microbiome alterations in Alzheimer’s disease. Scientific Reports, 7(1). http://dx.doi.org/10.1038/s41598-017-13601-y
Carabotti, M., Scirocco, A., Maselli, M. A., & Severi, C. (2015). The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems. Annals of Gastroenterology : Quarterly Publication of the Hellenic Society of Gastroenterology, 28(2), 203–209.
Casén, C., Vebø, H., Sekelja, M., Hegge, F., Karlsson, M., & Ciemniejewska, E. et al. (2015). Deviations in human gut microbiota: a novel diagnostic test for determining dysbiosis in patients with IBS or IBD. Alimentary Pharmacology & Therapeutics, 42(1), 71-83. http://dx.doi.org/10.1111/apt.13236
Jandhyala, S. M., Talukdar, R., Subramanyam, C., Vuyyuru, H., Sasikala, M., & Reddy, D. N. (2015). Role of the normal gut microbiota. World Journal of Gastroenterology : WJG, 21(29), 8787–8803. http://doi.org/10.3748/wjg.v21.i29.8787
Lambeth, S. M., Carson, T., Lowe, J., Ramaraj, T., Leff, J. W., Luo, L., … Shah, V. O. (2015). Composition, Diversity and Abundance of Gut Microbiome in Prediabetes and Type 2 Diabetes. Journal of Diabetes and Obesity, 2(3), 1–7. http://doi.org/10.15436/2376-0949.15.031
McFadden, R., Larmonier, C., Shehab, K., Midura-Kiela, M., Ramalingam, R., & Harrison, C. et al. (2015). The Role of Curcumin in Modulating Colonic Microbiota During Colitis and Colon Cancer Prevention. Inflammatory Bowel Diseases, 21(11), 2483-2494. http://dx.doi.org/10.1097/mib.0000000000000522
“S.M. O’Mahony, G. Clarke, Y.E. Borre, T.G. Dinan & J.F. Cryan” (“2015”). “Serotonin, tryptophan metabolism and the brain-gut-microbiome axis”. “Behavioural Brain Research”, “277”, “32 – 48”.
Wu, W., Panyod, S., Ho, C., Kuo, C., Wu, M., & Sheen, L. (2015). Dietary allicin reduces transformation of L-carnitine to TMAO through impact on gut microbiota. Journal Of Functional Foods, 15, 408-417. http://dx.doi.org/10.1016/j.jff.2015.04.001
Bayan, L., Koulivand, P. H., & Gorji, A. (2014). Garlic: a review of potential therapeutic effects. Avicenna Journal of Phytomedicine, 4(1), 1–14.
McFarland, L. V. (2014). Use of probiotics to correct dysbiosis of normal microbiota following disease or disruptive events: a systematic review. BMJ Open, 4(8), e005047. http://doi.org/10.1136/bmjopen-2014-005047
Sanz, Y., Olivares, M., Moya-Pérez, Á., & Agostoni, C. (2014). Understanding the role of gut microbiome in metabolic disease risk. Pediatric Research, 77(1-2), 236-244. http://dx.doi.org/10.1038/pr.2014.170
Walters, W. A., Xu, Z., & Knight, R. (2014). Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Letters, 588(22), 4223–4233. http://doi.org/10.1016/j.febslet.2014.09.039
Aggarwal, B. B., Gupta, S. C., & Sung, B. (2013). Curcumin: an orally bioavailable blocker of TNF and other pro-inflammatory biomarkers. British Journal of Pharmacology, 169(8), 1672–1692. http://doi.org/10.1111/bph.12131
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K., & Knight, R. (2012). Diversity, stability and resilience of the human gut microbiota. Nature, 489(7415), 220–230. http://doi.org/10.1038/nature11550
Final Report on the Safety Assessment of Capsicum Annuum Extract, Capsicum Annuum Fruit Extract, Capsicum Annuum Resin, Capsicum Annuum Fruit Powder, Capsicum Frutescens Fruit, Capsicum Frutescens Fruit Extract, Capsicum Frutescens Resin, and Capsaicin. (2007). International Journal Of Toxicology, 26(1_suppl), 3-106. http://dx.doi.org/10.1080/10915810601163939
Mentula, S., Harmoinen, J., Heikkilä, M., Westermarck, E., Rautio, M., Huovinen, P., & Könönen, E. (2005). Comparison between Cultured Small-Intestinal and Fecal Microbiotas in Beagle Dogs. Applied and Environmental Microbiology, 71(8), 4169–4175. http://doi.org/10.1128/AEM.71.8.4169-4175.2005
Hawrelak JA & Myers, SP (2004).The causes of intestinal dysbiosis: a review. Alternative Medicine Review, vol. 9, no. 2, pp. 180-197.