Anyone who regularly reads peer-reviewed medical studies on the microbiome will notice findings reported as bacteria being “too high” or “too low,” with phrases like “trending” when statistical significance isn’t reached. Frankly, my reaction to 95% of these papers is an eye-roll, as the statistical methods used are often inappropriate for the data at hand. With multiple degrees in statistics, professional memberships, and experience, I’m acutely aware of both best practices and common pitfalls.

Microbiome data distributions frequently display extreme skewness—often greater than 20. In such cases, computing mean and standard deviation is simply incorrect.  My friend “Perplexity” writes Mean and standard deviation become inappropriate measures for computing significance if the distribution’s skewness is substantial—specifically, when the absolute skewness exceeds ±2.

Despite this, using these metrics remains standard in high school statistics and unfortunately persists in many life science studies. This “comfort zone” approach does nothing but cloud true findings in microbiome science.

My alternative methodology uses a much larger, highly annotated dataset—over 4,290 unique samples generously donated to Microbiome Prescription., most transferred from Biomesight.com. Importantly, these samples are uniformly processed and richly annotated with symptoms rather than diagnoses, yielding superior analytical clarity.

My Natural Questions to ask

Natural for a statistician that is.

• For people with a symptom or diagnosis
  • What are the significant bacteria associating (and likely causing) the symptom
  • What is the threshold levels for these bacteria
    • I use levels and not level because I have observed the same symptom may occur with a bacteria outside of a specific range. That is, too high or too low. I have also encountered this reported in a few studies, often hidden under a term like “altered microbiome”.
    • There is a dangerous assumption in the literature that significant bacteria must be either too high or too low. I unfortunately know Kierkegaard’s “Either/Or” well.
    • There are no universal threshold for all symptoms, each has its own
• For people without a symptom with a statistical model but with dysbiosis
  • How do you determine which bacteria are significant?
  • What is the threshold levels for these bacteria

Over the last decade, these are important questions because they lead directly to treatment suggestions.

They are also significant in evaluating progress. At present I have a forecasting algorithm that has a high prediction rate for symptoms from a microbiome. The forecasting algorithm also is useful for evaluating progress, an example for a recent sample the donor asked me to review.

Prediction

The checks indicates that the donor agrees that they have this symptom.

Monitoring

The person above followed the suggestions and the subsequent test results are shown below.

What are the most common bacteria associated with symptoms?

This is a generic question that is useful for health practitioners to know. For example, Kristina Mitts, of Mind Mood Microbiome and who I frequently correspond with, or Dr. Jason Hawrelak.  

Using more appropriate statistical methods on our sample of 4,292 distinct different samples; we found significant bacteria identified over 327 symptoms resulting in the following statistical significances.

Significance: P <	Count
0.05	13,855
0.01	12,411
0.001	7,614
0.0001	5,532

So what are the top one for each of these significance?

Overall Significance

Taxon	name	rank	Instances
820	Bacteroides uniformis	species	165
35833	Bilophila wadsworthia	species	142
35832	Bilophila	genus	139
818	Bacteroides thetaiotaomicron	species	137
1426	Parageobacillus thermoglucosidasius	species	133
118884	Gammaproteobacteria incertae sedis	no rank	125
871324	Bacteroides stercorirosoris	species	124
120580	Symbiobacterium toebii	species	122
53244	Desulfonatronovibrio	genus	122
543349	Symbiobacteriaceae	family	122
2733	Symbiobacterium	genus	122
1498	Hathewaya histolytica	species	122
454155	Paraprevotella xylaniphila	species	120

P < 0.05

Taxon	name	rank	Instances
2950010	Salidesulfovibrio	genus	47
221711	Salidesulfovibrio brasiliensis	species	46
658623	Chelonobacter	genus	45
69224	Erwinia psidii	species	44
213462	Syntrophobacterales	order	44
3024408	Syntrophobacteria	class	44
31977	Veillonellaceae	family	44
1843489	Veillonellales	order	43
550	Enterobacter cloacae	species	43
35832	Bilophila	genus	42
841	Roseburia	genus	41
53244	Desulfonatronovibrio	genus	41
871324	Bacteroides stercorirosoris	species	41
1260	Finegoldia magna	species	41
1498	Hathewaya histolytica	species	40

P < 0.01

Taxon	name	rank	Instances
35833	Bilophila wadsworthia	species	51
78448	Bifidobacterium pullorum	species	50
820	Bacteroides uniformis	species	47
841	Roseburia	genus	46
818	Bacteroides thetaiotaomicron	species	44
118884	Gammaproteobacteria incertae sedis	no rank	41
1769729	Hathewaya	genus	41
1426	Parageobacillus thermoglucosidasius	species	41
112902	Propionispora	genus	40
36853	Desulfitobacterium	genus	40
386414	Hoylesella timonensis	species	40
119065	unclassified Burkholderiales	family	40
1853231	Odoribacteraceae	family	40
400091	Hymenobacter xinjiangensis	species	39
209080	Propionispora hippei	species	39
871324	Bacteroides stercorirosoris	species	39
69224	Erwinia psidii	species	39
35832	Bilophila	genus	39

P < 0.001

Taxon	name	rank	Instances
820	Bacteroides uniformis	species	50
35833	Bilophila wadsworthia	species	37
35832	Bilophila	genus	32
118884	Gammaproteobacteria incertae sedis	no rank	32
658623	Chelonobacter	genus	31
246787	Bacteroides cellulosilyticus	species	31
120580	Symbiobacterium toebii	species	31
543349	Symbiobacteriaceae	family	31
253238	Ethanoligenens	genus	31
2733	Symbiobacterium	genus	31
292833	Candidatus Rhabdochlamydia	genus	30
324707	Candidatus Rhabdochlamydia crassificans	species	30
1426	Parageobacillus thermoglucosidasius	species	30
689704	Candidatus Rhabdochlamydiaceae	family	30
70190	Chroococcus	genus	29
402401	Chroococcus minutus	species	29
1890464	Chroococcaceae	family	29
283169	Odoribacter denticanis	species	28

P < 0.0001

Taxon	name	rank	Instances
820	Bacteroides uniformis	species	40
246787	Bacteroides cellulosilyticus	species	34
818	Bacteroides thetaiotaomicron	species	30
1963360	Parachlamydiales	order	30
454155	Paraprevotella xylaniphila	species	30
1426	Parageobacillus thermoglucosidasius	species	30
2733	Symbiobacterium	genus	29
543349	Symbiobacteriaceae	family	29
120580	Symbiobacterium toebii	species	29
35832	Bilophila	genus	26
191412	Chlorobiaceae	family	25
256319	Chlorobaculum	genus	25
244127	Anaerotruncus	genus	25
189723	Prevotella micans	species	25
53244	Desulfonatronovibrio	genus	25
324707	Candidatus Rhabdochlamydia crassificans	species	24
191410	Chlorobiia	class	24
35833	Bilophila wadsworthia	species	24

Summary

This is a high level overview of Significant Bacteria. The patterns above are specific for tests done by Biomesight; a lack of standardization results in using these identifications for other tests is unsafe (legal sense). Background here. IMHO, it is a moral responsibility for labs to produce similar tables.

The key findings are:

• “Common suspects” such as bifidobacterium and lactobacillus are missing!
• Large sample sizes with the same processing is critical. The processing must be the same as used in a clinical setting.
• Appropriate statistical methods must be used

Stay tune for the next part as we drill deeper into appropriate handing of data with some specific issues like Long COVID.

The input data that I used is publicly available at: https://citizenscience.microbiomeprescription.com/

Post Script

Probiotics and the above gets interesting. Take Bacteroides uniformis which is at the top of many of these tables. If we go to my bacteria association site,

We can determine the probiotics (available or pending) that will increase this bacteria (none decreases)

Again, the “cure all” lactobacillus and bifidobacterium genus is absent (apart from Ligilactobacillus ruminis which is not currently available).