Back Story

Latest microbiome results are in. Seems like my microbiome is stubborn and stuck these last few tests. Wondering if I should just use this test suggestions exclusively or combine with my prior Thorne test 

We have two sample – one via BiomeSight and one via Thorne. This post is going to do two things:

• Look at Suggestions – by combining both sets of suggestions using the Uber Consensus
• Look at the differences between the reports.

We also review “which is better”. My focus is clinical application to individuals — not research papers; answer at bottom.

Uber Consensus

The process has become very simple — “Just give me Suggestions!” on both samples and then going to uber consensus as illustrated by the video below.

The result was excellent agreement on suggestion between each set of results. The CSV files are attached below.

Differences between Reports

I compared two things between the reports:

• Percentage of the bacteria in the microbiome
• Percentile of the bacteria in the microbiome

At the Phylum Level

Items less than 100 should be ignored (accuracy of measurement limits). There are a few dramatic differences.

Bacteria Name	Thorne Count	BiomeSight Count
Firmicutes	396799	529540
Actinobacteria	60610	2100
Bacteroidetes	461289	448230
Proteobacteria	6095	18150
Chlorobi	36	429
Acidobacteria	35	100
Cyanobacteria	83	20
Spirochaetes	85	30
Verrucomicrobia	59	10
Chloroflexi	77	50
Tenericutes	54	30
Deinococcus-Thermus	48	30
Fibrobacteres	4	10
Synergistetes	17	20

Looking at Percentiles next

Bacteria Name	Thorne %ile	BiomeSight %ile
Chlorobi	25	84
Actinobacteria	85	33
Acidobacteria	34	81
Spirochaetes	81	36
Cyanobacteria	31	1
Deinococcus-Thermus	55	29
Firmicutes	14	37
Chloroflexi	67	50
Verrucomicrobia	14	1
Tenericutes	13	2
Proteobacteria	10	18
Synergistetes	6	4
Bacteroidetes	55	56
Fibrobacteres	1	0

We have Bacteroidetes in agreement with both — but for the rest…

At the genus level

Bacteria Name	Thorne Count	BiomeSight Count
Bacteroides	180054	397640
Blautia	16470	107220
Roseburia	16793	73640
Faecalibacterium	109196	152890
Corynebacterium	43413	820
Ruminococcus	9177	44170
Phocaeicola	223209	199669
Parabacteroides	11855	31940
Phascolarctobacterium	6101	23980
Dorea	36	13000
Sutterella	16	11339
Oscillospira	0	8250
Coprococcus	6120	12589
Eggerthella	6491	760
Pseudobutyrivibrio	149	5790
Lachnospira	11593	6230
Prevotella	954	4260
Anaerostipes	9303	6310
Clostridium	2039	4960
Pedobacter	46	2410
Odoribacter	4077	2060
Bifidobacterium	2783	1019
Escherichia	75	1610
Porphyromonas	1372	150
Mediterraneibacter	14831	13629
Bilophila	6	1110
Veillonella	75	1160
Desulfovibrio	1900	1250
Streptococcus	1477	840
Acetivibrio	33	470
Chlorobaculum	6	429
Finegoldia	1339	920
Gemella	17	400
Enterococcus	585	220
Paenibacillus	376	20
Mogibacterium	39	370
Acetobacterium	15	340
Serratia	47	350
Eubacterium	517	240
Megasphaera	35	290
Selenomonas	52	290
Bacillus	248	10
Caldicellulosiruptor	11	240
Campylobacter	235	10
Slackia	16	240
Sphingobacterium	48	270
Caloramator	10	190
Staphylococcus	181	10
Hathewaya	8	170
Peptoniphilus	656	800
Peptostreptococcus	6	150
Microbacterium	125	10
Adlercreutzia	525	620
Rhodothermus	6	90
Erysipelothrix	12	90
Acidaminococcus	12	90
Hymenobacter	80	10
Negativicoccus	115	50
Collinsella	74	10
Rhodococcus	67	10
Dialister	25	80
Anaerococcus	336	390
Pseudoclostridium	8	60
Moorella	9	60
Vibrio	60	10
Caldilinea	1	50
Brochothrix	2	50
Mycobacterium	67	20
Neisseria	57	10
Pectinatus	7	50
Thermoclostridium	16	50
Alkaliphilus	9	40
Shewanella	31	60
Lactobacillus	57	30
Leptospira	4	30
Deinococcus	35	10
Tetragenococcus	5	30
Ethanoligenens	34	10
Weissella	10	30
Gulosibacter	1	20
Pseudoclavibacter	2	20
Kocuria	28	10
Meiothermus	2	20
Stenotrophomonas	28	10
Symbiobacterium	3	20
Devosia	4	20
Dysgonomonas	34	20
Azoarcus	21	10
Leuconostoc	9	20
Glaciecola	1	10
Turicibacter	21	30
Pelotomaculum	1	10
Parascardovia	2	10
Lentibacillus	2	10
Actinopolyspora	2	10
Kitasatospora	2	10
MLOs	3	10
Ochrobactrum	3	10
Rickettsia	3	10
Luteibacter	3	10
Fibrobacter	4	10
Pediococcus	14	20
Halanaerobium	6	10
Dyadobacter	14	10
Mycoplasma	17	20
Thauera	9	10
Lysobacter	11	10

Looking at the percentile rankings — the absolute numbers may vary greatly, but what about relative percentiles?

Bacteria Name	Thorne %ile	Biomesight %ile
Ochrobactrum	2	2
Actinopolyspora	1	1
Halanaerobium	1	1
MLOs	1	1
Glaciecola	1	1
Lentibacillus	1	1
Pelotomaculum	1	1
Parascardovia	1	1
Luteibacter	1	1
Phocaeicola	89	89
Rickettsia	1	0
Pediococcus	10	9
Fibrobacter	2	0
Mycoplasma	5	3
Alkaliphilus	1	3
Finegoldia	85	88
Kitasatospora	3	0
Thauera	5	1
Streptococcus	55	50
Turicibacter	12	17
Peptoniphilus	64	58
Hathewaya	1	8
Clostridium	18	11
Desulfovibrio	61	69
Eubacterium	38	46
Symbiobacterium	1	9
Enterococcus	88	79
Sphingobacterium	13	23
Pseudoclavibacter	1	11
Anaerococcus	72	83
Eggerthella	98	86
Gulosibacter	0	12
Lactobacillus	23	11
Bifidobacterium	55	43
Leuconostoc	2	14
Shewanella	35	47
Prevotella	50	63
Corynebacterium	99	86
Collinsella	13	0
Oscillospira	0	16
Faecalibacterium	49	65
Meiothermus	1	17
Caloramator	1	19
Coprococcus	39	57
Lysobacter	18	0
Odoribacter	81	63
Adlercreutzia	63	81
Pedobacter	13	31
Dyadobacter	20	1
Dysgonomonas	24	4
Mediterraneibacter	69	90
Devosia	1	22
Acetivibrio	5	27
Thermoclostridium	9	32
Ethanoligenens	25	1
Dialister	11	35
Veillonella	16	41
Pectinatus	1	27
Porphyromonas	88	62
Moorella	1	28
Negativicoccus	66	39
Lachnospira	51	21
Rhodothermus	1	32
Tetragenococcus	1	32
Acetobacterium	3	34
Anaerostipes	65	96
Bilophila	1	33
Ruminococcus	14	47
Weissella	2	35
Parabacteroides	42	75
Acidaminococcus	4	39
Pseudoclostridium	1	37
Leptospira	1	42
Serratia	34	75
Slackia	4	45
Phascolarctobacterium	56	97
Erysipelothrix	4	46
Sutterella	1	46
Bacteroides	39	87
Roseburia	43	91
Escherichia	28	77
Selenomonas	21	73
Deinococcus	54	1
Megasphaera	18	72
Brochothrix	1	56
Kocuria	58	2
Mogibacterium	17	74
Stenotrophomonas	63	3
Azoarcus	61	0
Caldilinea	0	61
Caldicellulosiruptor	2	64
Mycobacterium	87	24
Hymenobacter	68	1
Blautia	5	73
Paenibacillus	87	19
Neisseria	69	0
Pseudobutyrivibrio	25	95
Campylobacter	75	1
Gemella	4	82
Peptostreptococcus	1	81
Chlorobaculum	1	84
Staphylococcus	85	0
Vibrio	91	2
Bacillus	92	1
Rhodococcus	91	0
Dorea	1	93
Microbacterium	94	1

Bottom Line

I have never had much belief in the absolute accuracy of the bacteria named or the count. Why? Simple, I understand the statistical process being used and its weakness. I will leave arguments over “which is better” and “which is accurate” to others.

My focus and concern is to improve the microbiome. With sparse data and the great complexity involved, I am actually very pleased that the suggestions are in agreement. The suggestions are computed using fuzzy logic expert systems. The noise in the data and the statistical processes involved seem to be smoothed out by this Artificial Intelligence engine approach.

Score: Labs: -2, Microbiome Prescription 2

Which is better?. My focus is clinical application to individuals, both give similar suggestions using the Fuzzy Logic Expert System. There is no difference in that sense.