The process is very simple, for a condition like ME/CFS, we compute the expected number of samples reporting this bacteria (based on people without Long COVID) and compare it to the actual number seen. This can be used to compute a statistical value called Chi-Square (χ²), This is then used to compute the chance of it happening at random. This is possible because we have over 3600 samples from some labs and thus able to detect things better.

Actual example:

• Prevotella denticola – Species reported by Ombre
  • Expected to see 3.6
  • Actually seen 27
• In other words almost 4x more common than expected. The probability is
  • 4.9E-34
  • or 1 chance in 2,000,000,000,000,000,000,000,000,000,000,000 of happening at random.
• This suggests that we should reduce it to remedy ME/CFS [with the other bacteria involved]

Biomesight and Ombre identifies bacteria using different methodologies so often give different names and amounts. For background on this lack of standardization, see The taxonomy nightmare before Christmas…

The data below is for samples marked with “Official Diagnosis: Chronic Fatigue Syndrome (CFS/ME)“

For Long COVID, see this post.

Unlike some conditions shown below, it is not just one bacteria involved but combinations.

• Peptic ulcer disease: Helicobacter pylori
• Tetanus: Clostridium tetani
• Typhoid fever: Salmonella typhi
• Diphtheria: Corynebacterium diphtheriae
• Syphilis: Treponema pallidum
• Cholera: Vibrio cholerae
• Leprosy: Mycobacterium leprae
• Tuberculosis: Mycobacterium tuberculosis
• Sinusitis: Corynebacterium tuberculostearicum

Ombre Data

We have 10 bacteria that are too low and some 67 that are too high. Six of the 10 that are too low should be available as probiotics (conceptually), but only B.Bifidum is.

• Bifidobacterium asteroides
• Bifidobacterium bifidum
• Bifidobacterium bombi
• Bifidobacterium commune
• Bifidobacterium magnum
• Bifidobacterium thermacidophilum

In the too high group are some familiar names: Rickettsia (Cecile Jadin’s protocol) and spotted fever group.

Bacteria Name	Rank	Expected	Observed	Shift	Prob
Anaerococcus murdochii	species	22.1	40	Too High	0.000134
Anaerococcus octavius	species	23.2	36	Too High	0.00785
Anaerococcus senegalensis	species	21.6	34	Too High	0.007766
Bacteroides fluxus	species	82.2	111	Too High	0.001504
Bacteroides thetaiotaomicron	species	95.6	122	Too High	0.006947
Bifidobacterium asteroides	species	50.3	28	Too Low	0.004406
Bifidobacterium bifidum	species	76.3	49	Too Low	0.002295
Bifidobacterium bombi	species	80.8	44	Too Low	0.000136
Bifidobacterium commune	species	56.8	30	Too Low	0.000581
Bifidobacterium magnum	species	73.0	46	Too Low	0.002755
Bifidobacterium thermacidophilum	species	53.8	31	Too Low	0.004352
Campylobacter	genus	54.0	74	Too High	0.006445
Campylobacter hominis	species	26.8	43	Too High	0.001707
Campylobacteraceae	family	54.8	76	Too High	0.004245
Campylobacterales	order	61.6	83	Too High	0.006328
Desulfarculaceae	family	24.9	40	Too High	0.002442
Desulfarculales	order	24.9	40	Too High	0.002442
Desulfarculia	class	26.2	40	Too High	0.00692
Desulfocarbo	genus	14.8	26	Too High	0.003442
Desulfocarbo indianensis	species	14.6	26	Too High	0.002722
Desulfoglaeba	genus	27.8	42	Too High	0.007259
Desulfoglaeba alkanexedens	species	27.4	42	Too High	0.005339
Desulfovibrio legallii	species	32.5	48	Too High	0.006443
Desulfovibrio piger	species	35.9	53	Too High	0.004287
Dialister pneumosintes	species	14.8	27	Too High	0.001445
Emticicia	genus	19.3	33	Too High	0.001758
Emticicia sediminis	species	8.4	21	Too High	1.52E-05
Enorma	genus	22.4	35	Too High	0.007472
Enorma massiliensis	species	22.4	35	Too High	0.007472
Enterorhabdus	genus	69.6	45	Too Low	0.004671
Epsilonproteobacteria	class	62.7	84	Too High	0.007016
Ezakiella	genus	44.7	63	Too High	0.006216
Flavobacteriaceae	family	75.1	98	Too High	0.008138
Halanaerobiales	order	78.4	54	Too Low	0.005779
Hallella	genus	34.4	60	Too High	1.30E-05
Hallella bergensis	species	17.5	39	Too High	2.77E-07
Hallella multisaccharivorax	species	26.2	45	Too High	0.000235
Holdemania massiliensis	species	55.2	75	Too High	0.00788
Hoylesella buccalis	species	75.1	105	Too High	0.000552
Hoylesella loescheii	species	34.3	51	Too High	0.004268
Hoylesella marshii	species	15.6	30	Too High	0.000268
Hoylesella nanceiensis	species	30.4	48	Too High	0.001467
Humidesulfovibrio	genus	29.9	49	Too High	0.000497
Humidesulfovibrio idahonensis	species	27.8	47	Too High	0.000281
Hungateiclostridiaceae	family	78.0	106	Too High	0.001492
Insolitispirillum	genus	31.0	46	Too High	0.007226
Insolitispirillum peregrinum	species	30.4	46	Too High	0.004822
Leptolinea	genus	14.4	26	Too High	0.002277
Leptolinea tardivitalis	species	13.5	24	Too High	0.004247
Parabacteroides johnsonii	species	51.0	70	Too High	0.007925
Paraprevotella xylaniphila	species	25.0	42	Too High	0.000677
Parvimonas	genus	29.7	52	Too High	4.33E-05
Parvimonas micra	species	29.7	52	Too High	4.33E-05
Pedobacter	genus	36.3	53	Too High	0.005472
Peptoniphilus lacrimalis	species	34.6	51	Too High	0.005246
Persicobacteraceae	family	12.1	24	Too High	0.000587
Phocaeicola salanitronis	species	105.9	136	Too High	0.003396
Porphyromonas asaccharolytica	species	33.8	51	Too High	0.003157
Porphyromonas endodontalis	species	25.7	40	Too High	0.004944
Prevotella dentasini	species	6.8	29	Too High	1.25E-17
Prevotella denticola	species	4.2	27	Too High	1.35E-28
Prolixibacteraceae	family	20.7	33	Too High	0.006663
Propioniferax	genus	23.2	42	Too High	9.45E-05
Propioniferax innocua	species	22.8	42	Too High	5.61E-05
Rickettsia	genus	21.0	36	Too High	0.001101
Rickettsia honei	species	14.8	28	Too High	0.000569
Segatella bryantii	species	24.5	39	Too High	0.003287
Segatella maculosa	species	33.2	55	Too High	0.000161
Segatella oris	species	22.2	38	Too High	0.000807
Segatella paludivivens	species	32.9	54	Too High	0.000245
Senegalimassilia anaerobia	species	54.0	34	Too Low	0.006868
Slackia piriformis	species	56.5	29	Too Low	0.00048
spotted fever group	species group	17.9	32	Too High	0.000907
Tindallia	genus	14.8	26	Too High	0.003442
unclassified Burkholderiales	family	20.9	33	Too High	0.008034
unclassified Clostridiales	family	77.1	106	Too High	0.000984

Biomesight Data

We have more data from Biomesight which means better (more) detection of significant bacteria.

As above, we have 12 bacteria that are too low and 116 bacteria that are too high. We have only 2 Bifidobacterium identified with only one available as a probiotic

• Bifidobacterium adolescentis
• Bifidobacterium cuniculi

We see that Lactobacillus is too high. This agrees with brain fog being caused by over production of d-lactic acid.

• Lactobacillus acidophilus
• Lactobacillus iners

We also see these two are high that are commonly associated to issues:

• Streptococcus agalactiae
• Streptococcus mutans

Tax_Name	Tax_Rank	Expected	Observed	Shift	Probability
[Actinobacillus] rossii	species	119.1	89	Too Low	0.005858
[Pasteurella] aerogenes-[Pasteurella] mairii-[Actinobacillus] rossii complex	species group	119.1	89	Too Low	0.005858
Absiella	genus	29.8	56	Too High	1.52E-06
Acetobacteraceae	family	103.7	131	Too High	0.007414
Acholeplasma hippikon	species	73.9	110	Too High	2.63E-05
Actinobacillus	genus	176.6	134	Too Low	0.004701
Actinobacillus pleuropneumoniae	species	61.6	35	Too Low	0.00299
Actinobacillus porcinus	species	133.9	99	Too Low	0.003018
Adlercreutzia equolifaciens	species	189.7	227	Too High	0.006755
Aggregatibacter	genus	82.3	49	Too Low	0.000912
Alcanivoracaceae	family	37.3	54	Too High	0.006427
Alcanivorax	genus	37.3	54	Too High	0.006427
Amedibacillus	genus	196.8	235	Too High	0.006495
Amedibacillus dolichus	species	197.1	235	Too High	0.006912
Anaerococcus	genus	126.4	157	Too High	0.006444
Anaerococcus hydrogenalis	species	39.1	58	Too High	0.002475
Anaerococcus prevotii	species	21.1	36	Too High	0.001188
Anaerococcus tetradius	species	29.6	59	Too High	6.37E-08
Anaerococcus vaginalis	species	63.6	87	Too High	0.003269
Anaerofustis	genus	54.3	76	Too High	0.003316
Anaerofustis stercorihominis	species	53.0	74	Too High	0.003981
Arthrobacter	genus	27.7	43	Too High	0.003655
Bifidobacterium adolescentis	strain	102.4	63	Too Low	0.001744
Bifidobacterium cuniculi	species	64.9	38	Too Low	0.007829
Bifidobacterium ruminantium	species	14.8	30	Too High	7.46E-05
Caloramator viterbiensis	species	19.0	37	Too High	3.60E-05
Campylobacter ureolyticus	species	38.0	56	Too High	0.003481
Cetobacterium	genus	17.7	29	Too High	0.007331
Cetobacterium ceti	species	13.7	28	Too High	0.000115
Chlorobaculum limnaeum	species	8.6	22	Too High	4.66E-06
Clostridium cadaveris	species	106.5	140	Too High	0.001155
Collinsella tanakaei	species	43.8	62	Too High	0.005945
Corynebacteriaceae	family	133.0	164	Too High	0.007125
Corynebacterium	genus	133.0	164	Too High	0.007125
Corynebacterium amycolatum	species	20.6	36	Too High	0.000675
Corynebacterium glucuronolyticum	species	10.4	21	Too High	0.001029
Corynebacterium pyruviciproducens	species	12.7	23	Too High	0.003812
Corynebacterium xerosis	species	14.8	25	Too High	0.007807
Dehalobacterium	genus	124.4	159	Too High	0.001898
Dehalococcoidaceae	family	11.9	23	Too High	0.00124
Dehalococcoidales	order	11.9	23	Too High	0.00124
Dehalococcoidia	class	11.9	23	Too High	0.00124
Dehalogenimonas	genus	11.9	23	Too High	0.00124
Dehalogenimonas lykanthroporepellens	species	11.3	23	Too High	0.000539
Desulfonatronovibrio	genus	44.2	62	Too High	0.007398
Desulfonatronovibrionaceae	family	43.3	62	Too High	0.004437
Desulfovibrio psychrotolerans	species	19.8	37	Too High	0.000109
Dethiobacteraceae	family	47.8	77	Too High	2.50E-05
Dethiobacterales	order	47.8	77	Too High	2.50E-05
Dethiobacteria	class	47.8	77	Too High	2.50E-05
Dyadobacter	genus	77.6	103	Too High	0.003878
Eggerthella sinensis	species	133.2	167	Too High	0.003441
Erysipelothrix inopinata	species	42.5	62	Too High	0.002739
Ethanoligenens	genus	158.8	205	Too High	0.000248
Eubacterium limosum	species	27.2	42	Too High	0.00458
Facklamia	genus	21.2	34	Too High	0.005361
Filifactor alocis	species	35.1	54	Too High	0.001384
Finegoldia magna	species	112.9	143	Too High	0.004643
Halanaerobiaceae	family	56.2	78	Too High	0.003557
Halanaerobiales	order	61.2	83	Too High	0.005272
Halanaerobium	genus	56.2	78	Too High	0.003557
Haploplasma	genus	13.9	31	Too High	4.32E-06
Haploplasma cavigenitalium	species	13.9	31	Too High	4.32E-06
Heliorestis baculata	species	17.4	30	Too High	0.002634
Hungateiclostridiaceae	family	45.4	71	Too High	0.000143
Hungateiclostridium	genus	45.2	71	Too High	0.000124
Hyphomicrobium	genus	39.8	58	Too High	0.004009
Hyphomicrobium aestuarii	species	34.0	51	Too High	0.003635
Hyphomonadaceae	family	16.9	29	Too High	0.003196
Hyphomonadales	order	16.9	29	Too High	0.003196
Isoalcanivorax	genus	35.5	52	Too High	0.005689
Isoalcanivorax indicus	species	31.1	52	Too High	0.000184
Lactobacillus acidophilus	species	18.8	32	Too High	0.002356
Lactobacillus iners	species	17.3	32	Too High	0.000436
Legionella shakespearei	species	67.0	91	Too High	0.003402
Levilactobacillus	genus	12.2	25	Too High	0.000263
Listeriaceae	family	178.9	215	Too High	0.006915
Luteolibacter	genus	127.8	159	Too High	0.005844
Luteolibacter algae	species	126.9	158	Too High	0.005805
Mannheimia	genus	134.7	102	Too Low	0.004859
Mannheimia caviae	species	128.7	97	Too Low	0.005142
Meiothermus	genus	78.6	103	Too High	0.005964
Meiothermus granaticius	species	77.3	101	Too High	0.007138
Mobiluncus	genus	31.7	52	Too High	0.0003
Mogibacterium vescum	species	57.6	94	Too High	1.64E-06
Nannocystineae	suborder	23.0	36	Too High	0.006465
Olivibacter terrae	species	12.1	22	Too High	0.004168
Paenibacillus filicis	species	22.9	38	Too High	0.001632
Pasteurellaceae incertae sedis	no rank	119.1	89	Too Low	0.005858
Pediococcus	genus	166.2	208	Too High	0.00119
Pediococcus argentinicus	species	17.9	31	Too High	0.002047
Peptoniphilus asaccharolyticus	species	116.8	148	Too High	0.003873
Peptostreptococcus stomatis	species	52.0	73	Too High	0.003541
Porphyromonas asaccharolytica	species	70.2	101	Too High	0.000234
Prosthecobacter	genus	32.7	54	Too High	0.000194
Prosthecobacter fluviatilis	species	18.1	33	Too High	0.00045
Rhizobiaceae	family	80.9	105	Too High	0.007378
Roseomonas	genus	22.3	38	Too High	0.000867
Roseospira	genus	54.9	78	Too High	0.0018
Ruminiclostridium cellulolyticum	species	107.1	135	Too High	0.007053
Segatella paludivivens	species	72.9	45	Too Low	0.001097
Slackia faecicanis	species	98.6	128	Too High	0.003086
Sporosarcina	genus	47.9	69	Too High	0.00235
Sporosarcina pasteurii	species	46.9	69	Too High	0.001278
Streptococcus agalactiae	species	11.9	23	Too High	0.00124
Streptococcus mutans	species	31.7	51	Too High	0.0006
Thauera terpenica	species	20.6	33	Too High	0.006179
Thermaceae	family	94.7	125	Too High	0.001859
Thermales	order	94.7	125	Too High	0.001859
Thermoanaerobacter	genus	78.4	102	Too High	0.007565
Thermodesulfatator	genus	22.7	42	Too High	5.03E-05
Thermodesulfatator atlanticus	species	22.7	42	Too High	5.03E-05
Thermodesulfatatoraceae	family	27.5	42	Too High	0.005627
Thermodesulfobacteria	class	22.7	42	Too High	5.03E-05
Thermodesulfobacteriaceae	family	22.7	42	Too High	5.03E-05
Thermodesulfobacteriales	order	22.7	42	Too High	5.03E-05
Thermosediminibacteraceae	family	14.2	26	Too High	0.001847
Thermovenabulum	genus	14.2	26	Too High	0.001847
Thermovenabulum ferriorganovorum	species	14.2	26	Too High	0.001847
Thermus	genus	28.5	43	Too High	0.006576
Thiocapsa	genus	75.7	104	Too High	0.001154
unclassified Burkholderiales	family	15.2	29	Too High	0.000429
Vagococcus teuberi	species	47.0	66	Too High	0.005467
Varibaculum	genus	47.5	73	Too High	0.000214
Varibaculum cambriense	species	19.3	32	Too High	0.003695
Winkia	genus	11.0	23	Too High	0.000324
Winkia neuii	species	11.0	23	Too High	0.000324

uBiome

While the company no longer exists, we have a significant number of samples from them.

As above, we have Lactobacillus crispatus being too high. We have just 2 bacteria being too low and 96 being too high.

Tax_Name	Tax_Rank	Expected	Observed	Shift	Probability
Acetitomaculum	genus	24.4	44	Too High	7.07E-05
Acholeplasmatales	order	10.0	21	Too High	0.000503
Actinomyces sp. 2002-2301122	species	14.4	25	Too High	0.00497
Aggregatibacter	genus	42.2	24	Too Low	0.005171
Alistipes indistinctus	species	62.8	90	Too High	0.000602
Alistipes sp. NML05A004	species	81.8	111	Too High	0.001227
Alistipes sp. RMA 9912	species	13.7	25	Too High	0.002304
Anaerococcus murdochii	species	30.5	56	Too High	3.93E-06
Anaeroplasmataceae	family	22.8	38	Too High	0.00148
Anaeroplasmatales	order	23.1	39	Too High	0.000914
Archaea	superkingdom	39.7	73	Too High	1.32E-07
Atopobium	genus	17.8	30	Too High	0.00374
Bacteroides eggerthii	species	30.5	52	Too High	9.98E-05
Bacteroides sp. 35AE37	species	66.0	92	Too High	0.00139
Bacteroides sp. XB12B	species	39.7	66	Too High	3.10E-05
Blautia stercoris	species	37.2	55	Too High	0.003458
Butyricimonas	genus	88.4	114	Too High	0.006407
Butyricimonas faecihominis	species	57.7	79	Too High	0.005005
Butyricimonas sp. 214-4	species	53.8	75	Too High	0.003921
Butyricimonas virosa	species	42.3	62	Too High	0.002455
Caldicoprobacter	genus	23.9	39	Too High	0.001957
Caldicoprobacteraceae	family	44.2	68	Too High	0.00034
Campylobacter hominis	species	27.9	42	Too High	0.007781
Campylobacter ureolyticus	species	24.4	41	Too High	0.000762
Catenibacterium	genus	57.9	80	Too High	0.003678
Christensenella minuta	species	23.1	36	Too High	0.007121
Cloacibacillus	genus	19.8	38	Too High	4.36E-05
Cloacibacillus evryensis	species	14.7	31	Too High	2.24E-05
Coprobacter secundus	species	32.8	53	Too High	0.000426
Corynebacterium diphtheriae	species	11.5	21	Too High	0.005334
Corynebacterium sp. 713182/2012	species	16.2	27	Too High	0.006945
Desulfovibrio	genus	72.6	97	Too High	0.004103
Desulfovibrio sp. 3_1_syn3	species	9.0	30	Too High	2.23E-12
Dialister micraerophilus	species	9.6	24	Too High	3.85E-06
Dialister propionicifaciens	species	41.0	58	Too High	0.008017
Dialister sp. S7MSR5	species	16.8	29	Too High	0.002792
Eubacterium	genus	27.2	46	Too High	0.000305
Euryarchaeota	phylum	39.7	73	Too High	1.32E-07
Fibrobacter	genus	14.9	27	Too High	0.001656
Fibrobacteraceae	family	24.1	47	Too High	3.08E-06
Fibrobacterales	order	32.6	61	Too High	6.22E-07
Fibrobacteria	class	32.6	61	Too High	6.22E-07
Fibrobacterota	phylum	32.6	61	Too High	6.22E-07
Gelria	genus	37.6	65	Too High	7.75E-06
Granulicatella adiacens	species	69.5	45	Too Low	0.006077
Herbaspirillum	genus	46.7	72	Too High	0.000218
Herbaspirillum seropedicae	species	39.0	62	Too High	0.000225
Hespellia	genus	96.4	123	Too High	0.006732
Hoylesella timonensis	species	23.4	40	Too High	0.000577
Hungateiclostridiaceae	family	26.4	43	Too High	0.001246
Hydrogenoanaerobacterium	genus	40.6	64	Too High	0.000246
Lactobacillus crispatus	species	26.1	40	Too High	0.006759
Lentisphaeria	class	53.6	75	Too High	0.003432
Lentisphaerota	phylum	53.6	75	Too High	0.003432
Methanobacteria	class	37.4	73	Too High	6.10E-09
Methanobacteriaceae	family	37.4	73	Too High	6.10E-09
Methanobacteriales	order	37.4	73	Too High	6.10E-09
Methanobrevibacter	genus	36.9	71	Too High	2.03E-08
Methanobrevibacter smithii	species	36.4	66	Too High	9.34E-07
Methanomada group	clade	37.4	73	Too High	6.10E-09
Mollicutes	class	30.0	49	Too High	0.00052
Murdochiella	genus	42.2	65	Too High	0.000434
Mycoplasmatota	phylum	30.0	49	Too High	0.00052
Opitutia	class	16.4	33	Too High	4.20E-05
Oxalobacteraceae	family	47.7	73	Too High	0.000257
Parabacteroides johnsonii	species	18.8	34	Too High	0.000451
Parasporobacterium paucivorans	species	16.2	29	Too High	0.001388
Parvibacter	genus	14.1	33	Too High	4.82E-07
Parvibacter caecicola	species	12.3	29	Too High	1.95E-06
Peptococcus	genus	71.8	100	Too High	0.000867
Peptoniphilus lacrimalis	species	30.3	46	Too High	0.004193
Porphyromonas sp. 2024b	species	12.7	29	Too High	4.76E-06
Propionibacteriaceae	family	18.8	31	Too High	0.004859
Propionibacteriales	order	18.8	31	Too High	0.004859
Puniceicoccales	order	16.2	33	Too High	2.76E-05
Robinsoniella	genus	30.3	50	Too High	0.00033
Robinsoniella sp. KNHs210	species	22.6	37	Too High	0.002366
Staphylococcaceae	family	38.5	57	Too High	0.002786
Staphylococcus	genus	38.2	57	Too High	0.00235
Synergistaceae	family	41.8	72	Too High	2.96E-06
Synergistales	order	41.8	72	Too High	2.96E-06
Synergistia	class	41.8	72	Too High	2.96E-06
Synergistota	phylum	41.8	72	Too High	2.96E-06
Thermoanaerobacteraceae	family	37.7	65	Too High	8.62E-06
Thermoanaerobacterales	order	39.7	66	Too High	3.10E-05
unclassified Butyricimonas	no rank	53.1	77	Too High	0.001019
unclassified Desulfovibrio	no rank	12.6	43	Too High	8.86E-18
unclassified Finegoldia	no rank	31.8	47	Too High	0.006982
unclassified Phascolarctobacterium	no rank	11.5	23	Too High	0.000738
unclassified Porphyromonas	no rank	24.4	38	Too High	0.005801
unclassified Prevotella	no rank	9.6	21	Too High	0.000258
unclassified Robinsoniella	no rank	22.6	37	Too High	0.002366
unclassified Staphylococcus	no rank	26.4	43	Too High	0.001246
Varibaculum	genus	48.5	70	Too High	0.001966
Varibaculum sp. CCUG 45114	species	36.1	54	Too High	0.002986
Victivallaceae	family	53.6	75	Too High	0.003432
Victivallales	order	53.6	75	Too High	0.003432
Victivallis	genus	53.1	75	Too High	0.002607

Bottom Line

In terms of probiotics, there are two that should be considered:

• Bif. Bifidum
• Bif. Adolescentis

And all Lactobacillus probiotics avoided.

The above information will be eventually integrated into Microbiome Prescription suggestions expert system. The purpose is to first identify the bacteria of concern.

The following bacteria were reported by 2 or 3 of the above

Aggregatibacter	genus
Anaerococcus murdochii	species
Campylobacter hominis	species
Campylobacter ureolyticus	species
Halanaerobiales	order
Hungateiclostridiaceae	family
Parabacteroides johnsonii	species
Peptoniphilus lacrimalis	species
Porphyromonas asaccharolytica	species
Segatella paludivivens	species
unclassified Burkholderiales	family
Varibaculum	genus

Recently I have seen advertisements for smart watches for ME/CFS. The price would be challenging for many people. I suspect that it also has a poor return for the costs.

This post is about the watch family that my wife and I have been using for a few years. The current version is below. On occasion, I have seen the price under $20.00

People who know me, knows that I prefer to work off objective data – for example microbiome test results, or actual physical measurements. This watch has empowered me. It can do automatic measurements every 10 minutes for day long monitoring.

What do I get with this watch?

You get an overview of the many measurements on a dashboard on your smart phone.

Steps per day

Sleep

Sleep is often a challenge. My wife found that some supplements improves her sleep and others do not. This changes decision making from being subjective to objective.

Body Temperature

As is common with ME/CFS people in general, I continue to have sub-normal temperatures. I notice that there is often a temperature spike while I sleep.

Blood Pressure

As often happens with ME/CFS, there can be low blood pressure as well as POTS.

Blood Oxygen

My variant of ME/CFS had coagulation being a factor. This can result in hypoxia (low oxygen levels) causing cognitive issues.

Blood Glucose

Blood Components

Heart Rate

HRV

Heart rate variability (HRV) is a measure of how much time varies between heartbeats. It’s a physiological marker of heart health and the autonomic nervous system (ANS). HRV can be used to assess physical and mental health, and to help diagnose diseases. 

How HRV is measured 

• HRV is measured in milliseconds
• A device is needed to measure the variation in time between heartbeats
• Wearable devices can be used to monitor HRV

What HRV indicates

• A high HRV indicates a healthy heart that can respond quickly to changes in the body 
• A low HRV is associated with an increased risk of disease and death 
• HRV can indicate activation of the parasympathetic nervous system (PSNS), which is associated with relaxation and good health 
• HRV can indicate activation of the sympathetic nervous system (SNS), which is associated with stress and ill health 

ECG Details

Heart Issues are frequently seen with ME/CFS. See The Heart and Blood of the CFS Patient

Overall Report

For example, I need to drink more water, etc.

Bottom Line

For less than $30, you get a lot of good information to act upon.

Questions

• Are the measurements accurate? In terms of being absolutely accurate — no. They appear to accurate for relative measurements: for example blood pressure. Items like skin color are known to impact accuracy of most optical sensors.
• How can it measure glucose? In terms of APPROVED FDA devices. There are no such devises for sale in the US. It is made in China (using the same type of sensors used in Apple, Google, FitBit, Samsung watches) and directly imported into the US “for personal use”, thus no FDA approval is needed. The FDA approval process is skipped and features available sooner.
  • “Apple Watch’s AFib detection tool earns landmark FDA approval“
  • “FDA qualifies Apple Watch AFib feature for use in clinical trials“
• As always “buyer beware” — test it against “approved clinical grade devices” for measurements of significant concern. I have found that it seems very accurate for relative changes.

Statistics is fun because there many paths. Most studies using the microbiome uses the easy, but naïve, path of computing averages and standard deviation. As my dataset has grown, I have been travelling some less traveled path, for example: Visual Exploration of Odds Ratios, and a patent pending method termed “Kaltoft-Moltrup”.

One of the frequent decisions that I see in studies is to limit examination of bacteria that have a high frequency in the samples. This allows the researchers to keep to familiar and classic statistics. Using frequency of observation in the control group and the condition group is one of these much less travelled paths. It usually require big sample sizes and many studies have a sample size of 30 (sufficient for the mean and standard deviation approach).

I just completed code to compute Chi2 using Biomesight data for users reporting ME/CFS.

• Total Population: 3525
• ME/CFS: 280

Chi2 can be converted to probability (p) of happening at random with the following table

Seen too Rarely(Want to increase)

We see one bacteria available as a probiotic Bifidobacterium adolescentis. The rest would need to be altered by diet.

tax_name	TAX RANK	Chi2	Observed	Expected	Shift
Segatella oulorum	species	13.9	16	38	Under-Represented
Bifidobacterium cuniculi	species	10.8	33	57	Under-Represented
Brenneria	genus	10.5	1	12	Under-Represented
Bifidobacterium adolescentis	strain	10.3	50	77	Under-Represented
Prevotella veroralis	species	10.3	6	20	Under-Represented
Aggregatibacter	genus	10.1	46	72	Under-Represented
Hoylesella shahii	species	10.1	12	28	Under-Represented
Segatella paludivivens	species	9.6	40	64	Under-Represented
Actinobacillus pleuropneumoniae	species	8.6	33	54	Under-Represented
Slackia heliotrinireducens	species	8.2	8	20	Under-Represented
Prevotella micans	species	8.1	3	13	Under-Represented
Actinobacillus	genus	8	123	157	Under-Represented
[Actinobacillus] rossii	species	7.1	79	105	Under-Represented
Pasteurellaceae incertae sedis	no rank	7.1	79	105	Under-Represented
[Pasteurella] aerogenes-[Pasteurella] mairii-[Actinobacillus] rossii complex	species group	7.1	79	105	Under-Represented
Aggregatibacter aphrophilus	species	7	4	13	Under-Represented
Mitsuokella multacida	species	6.8	3	12	Under-Represented

Seen too Often (Want to decrease)

We see 118 bacteria over a Chi2 of 6.635 ( P < 0.01 or 1 change in 100 of being a false detection). The list below are for genus and higher taxonomy orders.

Tax Name	Rank	Chi2	Observed	Expected	Shift
Dethiobacteraceae	family	20.9	75	45	Over-Represented
Hungateiclostridiaceae	family	16.1	71	45	Over-Represented
Tepidimicrobiaceae	family	14.6	16	7	Over-Represented
unclassified Burkholderiales	family	12.6	29	16	Over-Represented
Corynebacteriaceae	family	11.2	159	123	Over-Represented
Halanaerobiaceae	family	10.1	76	54	Over-Represented
Alcanivoracaceae	family	9.6	53	35	Over-Represented
Halanaerobiales	order	9.6	81	58	Over-Represented
Desulfonatronovibrionaceae	family	9.1	58	40	Over-Represented
Acetobacteraceae	family	8.7	124	96	Over-Represented
Hyphomonadaceae	family	8	29	18	Over-Represented
Tissierellaceae	family	7.5	20	11	Over-Represented
Rhizobiaceae	family	7.1	98	76	Over-Represented
Nannocystineae	suborder	6.9	36	24	Over-Represented
Rubritaleaceae	family	6.9	124	99	Over-Represented

Bottom Line

The next step is to compute similar tables for all symptoms and incorporate these findings into a new algorithm. I say new, because I do not know if it is better than the existing ones. Conceptually, it would be added as a 5th set of suggestions to the existing consensus view on Microbiome Prescription.

This is a “scribe notes” post. I am working on implementing odds ratio as a forecaster for ME/CFS and encountered some issues. In my work experience, this means taking a significant step backwards to look at the data better. I will look at the bacteria with a high frequency of being reported in tests first. The genus of greatest interest from preliminary work are:

• Blautia
• Faecalibacterium
• Clostridium
• Lachnospira
• Streptococcus
• Collinsella
• Anaerostipes
• Parabacteroides
• Anaerotruncus
• Bifidobacterium
• Pseudobutyrivibrio

The data is from BiomeSight samples. The red line is what would be expected with no influence. The population percentile included those with ME/CFS. Removing them would increase the differences more (I went lazy).

Charts

Blautia

Lower levels are clearly significant.

Faecalibacterium

Lower levels are some significance.

Clostridium

Lower levels are clearly significant.

Lachnospira

Higher levels are some significance for low values.

Streptococcus

Does not appear to have much significance

Collinsella

Higher levels are some significance for below average values

Anaerostipes

Lower levels are significance for all values.

Parabacteroides

Does not appear to have much significance

Anaerotruncus
Lower levels are significance for all values.

Bifidobacterium

Higher levels are significance for all values.

Pseudobutyrivibrio

Higher levels are significance for all values.

Next Steps

We can visually see how bacteria is shifted for these genus. The challenge is converting it to a formula to forecast that cross validates. Stay tune.