For any one that is interested, bacteria with P < 0.005 significance to 324 symptoms and diagnosis is now available (with source data) at https://microbiomeprescription.com/sample/Frequency
Some items of interest to the ME/CFS Community are below

In my last post on MRI Scans, I felt the best model is based on Evidence of widespread metabolite abnormalities in Myalgic encephalomyelitis/chronic fatigue syndrome: assessment with whole-brain magnetic resonance spectroscopy [2020]. Metabolite abnormalities can be a direct result of microbiome dysfunctions. Those abnormalities are very treatable using microbiome tests and expert systems such as generated by Microbiome Prescription.

What are Metabolites?

Metabolites are substances made or used in the body during metabolism, which is the process of breaking down food or chemicals into energy and other useful materials. They help the body grow, repair itself, and function properly. Examples include amino acids, vitamins, and sugars.

Example for ME/CFS

In Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), metabolites have been found to play a critical role in understanding the disease’s mechanisms and symptoms:

1. Gut Microbiome and Butyrate: ME/CFS is associated with changes in gut bacteria, leading to reduced levels of butyrate, a metabolite produced by certain gut microbes. Butyrate supports gut health, immune regulation, and energy production. Reduced butyrate levels in ME/CFS patients are linked to fatigue severity and inflammation.
2. Energy Metabolism: Studies reveal abnormalities in pathways like fatty acid metabolism, glucose metabolism, and the citric acid (TCA) cycle in ME/CFS patients. These changes suggest impaired cellular energy production, contributing to chronic fatigue.
3. Amino Acid Metabolism: Altered tryptophan metabolism and disruptions in the kynurenine pathway have been observed, which may affect immune function and contribute to neurocognitive symptoms through the gut-brain axis.
4. Plasma Metabolites: ME/CFS patients exhibit differences in plasma metabolites compared to healthy controls, particularly after physical exertion. These include disruptions in glutamate metabolism, which may impact recovery and exacerbate symptoms.
5. Disease Subtypes: Metabolomic studies have identified distinct metabolic profiles among ME/CFS patients, suggesting subtypes with different clinical presentations and underlying mechanisms.

These findings highlight the importance of metabolites in ME/CFS research, offering potential biomarkers for diagnosis and targets for therapeutic interventions.

Example for IBS

In the context of Irritable Bowel Syndrome (IBS), metabolites play a significant role:

1. Gut microbiota-derived metabolites: These are substances produced by the bacteria in our intestines and are thought to be involved in IBS symptoms. Some important examples include:
   • Bile acids
   • Short-chain fatty acids
   • Vitamins
   • Amino acids
   • Serotonin
   • Hypoxanthine
2. Blood metabolites: Certain metabolites in the blood have been found to have a causal relationship with IBS. For example:
   • Stearate: Associated with decreased susceptibility to IBS
   • Arginine: Associated with increased risk of IBS
   • 1-palmitoylglycerol: Associated with increased risk of IBS
3. Fecal metabolites: Studies have identified specific fecal metabolite profiles in IBS patients that differ from healthy individuals. These metabolites are often amino acids or fatty acids.
4. Brain-gut interaction: Some metabolites, particularly amino acids like tryptophan, glutamate, and histidine, may influence brain function in IBS patients5. They could affect brain connectivity either directly by crossing the blood-brain barrier or indirectly through peripheral mechanisms.

Understanding these metabolites and their interactions with the gut microbiome may provide valuable insights into the underlying mechanisms of IBS and potentially lead to new diagnostic tools or treatments.

Enzymes Role to Metabolites

Enzymes play a crucial role in managing metabolites within our bodies. Here’s a simple description of their relationship:

1. Enzymes are proteins that act as biological catalysts7. They speed up chemical reactions in our cells without being used up themselves.
2. Metabolites are substances produced or used during metabolism1. They can be small molecules like sugars, amino acids, or fatty acids.
3. Enzymes help break down large molecules (like proteins, fats, and carbohydrates) into smaller metabolites. This process is essential for digestion and energy production.
4. Enzymes also help build larger molecules from smaller metabolites. This is important for creating cellular structures and storing energy.
5. Each enzyme typically works on specific metabolites, called substrates1. The enzyme and substrate fit together like a lock and key.
6. By controlling which reactions happen and how quickly, enzymes regulate the levels of various metabolites in our bodies. This helps maintain balance and allows cells to respond to changing needs.

In essence, enzymes are the workers that manage metabolites, ensuring our bodies can efficiently use the food we eat and carry out the chemical processes necessary for life.

Data From Samples Uploaded with ME/CFS

It happens that from uploaded samples and KEGG: Kyoto Encyclopedia of Genes and Genomes; we can determine that the following enzymes are (VERY VERY) statistically significant. The most significant ones are all too high. The top ones comes from the three genus only: Chlorobaculum , Pelodictyon and Prosthecochloris

• Chlorobaculum limnaeum
• Chlorobaculum parvum
• Chlorobaculum tepidum
• Chlorobium chlorochromatii
• Chlorobium limicola
• Chlorobium phaeobacteroides
• Chlorobium phaeovibrioides
• Chloroherpeton thalassium
• Pelodictyon luteolum
• Pelodictyon phaeoclathratiforme
• Prosthecochloris aestuarii
• Prosthecochloris sp. CIB 2401
• Prosthecochloris sp. GSB1
• Prosthecochloris sp. HL-130-GSB

Some (but not all) enzymes can be provided by some probiotics. Below is recent feedback from a person dealing with a child’s autism.

EC Key	Enzyme Name	Probability	Shift
1.1.1.325	sepiapterin reductase (L–threo-7,8-dihydrobiopterin forming)	1.61685e-015	high
2.1.1.331	bacteriochlorophyllide d C-121-methyltransferase	1.61685e-015	high
2.1.1.332	bacteriochlorophyllide d C-82-methyltransferase	1.61685e-015	high
2.1.1.333	bacteriochlorophyllide d C-20 methyltransferase	1.61685e-015	high
3.1.1.100	chlorophyllide a hydrolase	1.61685e-015	high
4.2.1.169	3-vinyl bacteriochlorophyllide d 31-hydratase	1.61685e-015	high
2.3.3.8	ATP citrate synthase	1.18208e-013	high
1.3.1.75	3,8-divinyl protochlorophyllide a 8-vinyl-reductase (NADPH)	5.353e-013	high
2.5.1.42	geranylgeranylglycerol-phosphate geranylgeranyltransferase	7.71322e-013	high
1.17.98.2	bacteriochlorophyllide c C-71-hydroxylase	2.69579e-012	high
2.7.8.36	undecaprenyl phosphate N,N′-diacetylbacillosamine 1-phosphate transferase	3.60014e-009	low
1.11.1.6	catalase	1.37633e-008	low
6.5.1.8	3′-phosphate/5′-hydroxy nucleic acid ligase	1.02548e-007	low
2.5.1.105	7,8-dihydropterin-6-yl-methyl-4-(β-D-ribofuranosyl)aminobenzene 5′-phosphate synthase	1.27364e-007	low
1.1.1.65	pyridoxine 4-dehydrogenase	1.89183e-007	high
2.6.1.59	dTDP-4-amino-4,6-dideoxygalactose transaminase	3.34948e-007	low
4.1.1.31	phosphoenolpyruvate carboxylase	4.73602e-007	high
5.4.99.26	tRNA pseudouridine65 synthase	5.94186e-007	high
1.1.1.127	2-dehydro-3-deoxy-D-gluconate 5-dehydrogenase	8.09119e-007	low
3.4.21.83	oligopeptidase B	8.612e-007	high
1.1.1.9	D-xylulose reductase	1.07212e-006	high
1.12.1.4	hydrogenase (NAD+, ferredoxin)	1.22627e-006	high
3.5.2.9	5-oxoprolinase (ATP-hydrolysing)	1.30156e-006	high
2.7.1.12	gluconokinase	1.49961e-006	high
1.6.1.2	NAD(P)+ transhydrogenase (Re/Si-specific)	3.05641e-006	high
7.1.1.1	proton-translocating NAD(P)+ transhydrogenase	3.05641e-006	high
3.1.1.114	methyl acetate hydrolase	3.98055e-006	low
3.2.1.165	exo-1,4-β-D-glucosaminidase	4.31375e-006	low
2.3.1.117	2,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succinyltransferase	4.31566e-006	high
2.7.8.12	teichoic acid poly(glycerol phosphate) polymerase	4.59165e-006	low
6.1.1.13	D-alanine—poly(phosphoribitol) ligase	4.88544e-006	low
1.12.98.4	sulfhydrogenase	6.48299e-006	low
4.2.1.22	cystathionine β-synthase	7.49383e-006	high
2.3.1.78	heparan-α-glucosaminide N-acetyltransferase	8.1071e-006	low

This is an update of my post from 10 years ago, CFS: Appropriate Brain Scans. I will focus on studies in those 10 years. Short version of these studies below.

Data showed that MRI studies frequently reported structural changes in the white and gray matter. Abnormalities of the functional connectivity within the brainstem and with other brain regions have also been found. The studies have suggested possible mechanisms including astrocyte dysfunction, cerebral perfusion impairment, impaired nerve conduction, and neuroinflammation involving the brainstem, which may at least partially explain a substantial portion of the ME/CFS symptoms and their heterogeneous presentations in individual patient
Brainstem Abnormalities in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Scoping Review and Evaluation of Magnetic Resonance Imaging Findings [2021]

In our family dealing with ME/CFS, we were fortunate is having brain scans done by and interpreted by Doctor Daniel Amen. Our effective treatment was focused on shifting bacteria, addressing coagulation, and reducing inflammation.

For more information on metabolites, see this post.

Magnetic Resonance Imaging

• Chronic fatigue and headache in post-COVID-19 syndrome: a radiological and clinical evaluation [2025]
  “T2-hyperintense lesions are common in patients with post-COVID-19 syndrome,”
• Hippocampal subfield volume alterations and associations with severity measures in long COVID and ME/CFS: A 7T MRI study [2025]
  “we found significant associations between hippocampal subfield volumes and severity measures of ‘Pain’, ‘Duration of illness’, ‘Severity of fatigue’, ‘Impaired concentration’, ‘Unrefreshing sleep’, and ‘Physical function’ in both conditions. These findings suggest that hippocampal alterations may contribute to the neurocognitive impairment experienced by long COVID and ME/CFS patients.”
• Hypothalamus Connectivity in Adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome [2024]
  “We observed weak-to-moderate evidence of increased degree, but not strength, of connections from the bilateral anterior-inferior (left: pd [%] = 99.18, median [95% CI] = -22.68[-40.96 to 4.45]; right: pd [%] = 99.86, median [95% CI] = -23.35[-38.47 to 8.20]), left anterior-superior (pd [%] = 99.33, median [95% CI] = -18.83[-33.45 to 4.07]) and total left hypothalamus (pd [%] = 99.44, median [95% CI] = -47.18[-83.74 to 11.03]) in the ME/CFS group compared with controls. Conversely, bilateral posterior hypothalamus degree decreased with increasing ME/CFS illness duration (left: pd [%] = 98.13, median [95% CI]: -0.47[-0.89 to 0.03]; right: pd [%] = 98.50, median [95% CI]:-0.43[-0.82 to 0.05]). Finally, a weak relationship between right intermediate hypothalamus connectivity strength and fatigue severity was identified in the ME/CFS group (pd [%] = 99.35, median [95% CI] = -0.28[-0.51 to 0.06]), which was absent in controls. These findings suggest changes in hypothalamus connectivity may occur in adolescents with ME/CFS, warranting further investigation.”
• Six-Week Supplementation with Creatine in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): A Magnetic Resonance Spectroscopy Feasibility Study at 3 Tesla [2024]
  “Creatine supplementation over six weeks in ME/CFS patients increased brain creatine and improved fatigue and some aspects of cognition. “
• A Multimodal Magnetic Resonance Imaging Study on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Feasibility and Clinical Correlation [2024]
  “The study demonstrates the feasibility of combining MRS and fMRI to capture neurochemical and neurophysiological features of ME/CFS in female participants.”
• Imbalanced Brain Neurochemicals in Long COVID and ME/CFS: A Preliminary Study Using MRI [2025]
  “Our study identified significantly elevated glutamate and N-acetyl-aspartate levels in long COVID and ME/CFS patients compared with healthy controls. “
• Blood-brain barrier disruption and sustained systemic inflammation in individuals with long COVID-associated cognitive impairment [2024]
• Cluster analysis to identify long COVID phenotypes using ¹²⁹Xe magnetic resonance imaging: a multicentre evaluation [2024]
  “We identified four ¹²⁹Xe MRI long COVID phenotypes with distinct characteristics. ¹²⁹Xe MRI can dissect pathophysiological heterogeneity of long COVID to enable personalised patient care.”
• Brain-regional characteristics and neuroinflammation in ME/CFS patients from neuroimaging: A systematic review and meta-analysis [2023]
  “These abnormalities, occurring in pivotal network hubs bridging reason and emotion, disrupt connections with the limbic system, contributing to the hallmark symptoms of ME/CFS. Furthermore, we discuss the regions where neuroinflammatory features are frequently observed and address critical neuroimaging limitations, including issues related to inter-rater reliability. This systematic review serves as a valuable guide for defining regions of interest (ROI) in future neuroimaging investigations of ME/CFS.”
• A prospective randomized, double-blind placebo-controlled study to evaluate the effectiveness of neuroprotective therapy using functional brain MRI in patients with post-covid chronic fatigue syndrome [2023]
  “ the study demonstrates the potential effectiveness of CCSA therapy in relation to a wide range of symptoms (chronic fatigue syndrome/ asthenic syndrome and cognitive impairment) in patients with post-COVID syndrome. The first time demonstrated the effectiveness of neuroprotective therapy after post-COVID asthenic syndrome with the use of high-tech neuroimaging techniques.”
• Hypothalamus volumes in adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): impact of self-reported fatigue and illness duration [2023]
  “preliminary evidence was identified to suggest greater fatigue severity and longer illness duration were associated with greater right anterior-superior and superior-tubular volumes, respectively. “
• What lies beneath: White matter microstructure in pediatric myalgic encephalomyelitis/chronic fatigue syndrome using diffusion MRI [2023]
  “our findings suggest that white matter abnormalities may not be predominant in pediatric ME/CFS in the early stages following diagnosis. The discrepancy between our null findings and white matter abnormalities identified in the adult ME/CFS literature could suggest that older age and/or longer illness duration influence changes in brain structure and brain-behavior relationships that are not yet established in adolescence.”
• Brainstem volume changes in myalgic encephalomyelitis/chronic fatigue syndrome and long COVID patients [2023]
  ” Our study demonstrated an abnormal brainstem volume in both ME/CFS and long COVID consistent with the overlapping symptoms.”
• Connectivity Between Salience and Default Mode Networks and Subcortical Nodes Distinguishes Between Two Classes of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome [2023]
  “This work reports the remarkable finding that functional magnetic resonance imaging connectivity can differentiate between these two classes of ME/CFS.”
• Multimodal MRI of myalgic encephalomyelitis/chronic fatigue syndrome: A cross-sectional neuroimaging study toward its neuropathophysiology and diagnosis [2022] Study Proposal
• Volumetric differences in hippocampal subfields and associations with clinical measures in myalgic encephalomyelitis/chronic fatigue syndrome [2022]
  “we detected positive correlations between fatigue and hippocampus subfield volumes and a negative correlation between sleep disturbance score and the right CA1 body volume. In ME/CFS_ICC patients, we detected a strong negative relationship between fatigue and left hippocampus tail volume. Strong negative relationships were also detected between pain and SF36 physical scores and two hippocampal subfield volumes (left: GC-ML-DG head and CA4 head). Our study demonstrated that volumetric differences in hippocampal subfields have strong statistical inference for patients meeting the ME/CFS_ICC case definition and confirms hippocampal involvement in the cognitive and memory problems of ME/CFS_ICC patients.”
• Limbic Perfusion Is Reduced in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) [2021]
  ” the patient group showed significant hypoperfusion (uncorrected voxel wise p ≤ 0.001, FWE p ≤ 0.01) in several brain regions of the limbic system, including the anterior cingulate cortex, putamen, pallidum, and anterior ventral insular area. For the ME/CFS patients, the overall symptom severity score at rest was significantly associated with a reduced rCBF in the anterior cingulate cortex. The results of this study show that brain blood flow abnormalities in the limbic system may contribute to ME/CFS pathogenesis.”
• Neurochemical abnormalities in chronic fatigue syndrome: a pilot magnetic resonance spectroscopy study at 7 Tesla [2021]
  “The changes in glutathione and creatine are consistent with the presence of oxidative and energetic stress in CFS patients and are potentially remediable by nutritional intervention. “
• Subcortical brain segment volumes in Gulf War Illness and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome [2021]
  “CFS and GWI were appropriate “illness controls” for each other. The different patterns of adjusted segment volumes suggested that sexual dimorphisms contributed to pathological changes. Previous volumetric studies may need to be reevaluated to account for gender differences.”
• High Prevalence of Perineural Cysts in Patients with Fibromyalgia and Chronic Fatigue Syndrome [2021]
  “ In patients diagnosed with FM or CFS, the prevalence of TCs was three times higher than that in the general population”
• Neuroimaging characteristics of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): a systematic review [2020]
  “ Neuroimaging studies of ME/CFS have frequently observed additional brain area recruitment during cognitive tasks and abnormalities in the brain stem. The frequent observation of additional brain area recruitment and consistent observation of sluggish fMRI signal response suggest abnormal neurovascular coupling in ME/CFS.”
• Using structural and functional MRI as a neuroimaging technique to investigate chronic fatigue syndrome/myalgic encephalopathy: a systematic review [2020]
  “The most consistent finding was CFS/ME exhibited increased activations and recruited additional brain regions. Tasks with increasing load or complexity produced decreased activation in task-specific brain regions.”
• Mapping of pathological change in chronic fatigue syndrome using the ratio of T1- and T2-weighted MRI scans [2020]
  “he voxel-based group comparison with sub-millimetre resolution voxels detected very significant clusters with increased T1w/T2w in ME/CFS, mostly in subcortical grey matter, but also in brainstem and projection WM tracts. “
• Neuroimaging markers of chronic fatigue in older people: a narrative review [2021]
  “Fatigue was associated with reduced hippocampus volumes and with hippocampal amyloid deposition. Regarding the association between fatigue and the circuit of basal ganglia, putamen and thalamus were associated with physical fatigability, whereas amygdala and thalamus with mental fatigability”
• Machine Learning Detects Pattern of Differences in Functional Magnetic Resonance Imaging (fMRI) Data between Chronic Fatigue Syndrome (CFS) and Gulf War Illness (GWI) [2020]
  “We concluded that CFS and GWI are significantly differentiable using a pattern of fMRI activity based on an ensemble machine learning model.”
• Altered Structural Brain Networks Related to Adrenergic/Muscarinic Receptor Autoantibodies in Chronic Fatigue Syndrome [2020]
  “Our findings suggest that β1 AdR-Ab and β2 AdR-Ab are potential markers of ME/CFS.”
• A systematic review of neurological impairments in myalgic encephalomyelitis/ chronic fatigue syndrome using neuroimaging techniques [2020]
  “Changes in gray and white matter volumes, cerebral blood flow, brain structure, sleep, EEG activity, functional connectivity and cognitive function.”
• A Machine Learning Approach to the Differentiation of Functional Magnetic Resonance Imaging Data of Chronic Fatigue Syndrome (CFS) From a Sedentary Control [2020]
  “This study was able to uncover a pattern of activated neurological regions that differentiated CFS from Control. “
• Intra brainstem connectivity is impaired in chronic fatigue syndrome [2019]
  “In ME/CFS, connections were absent between medulla and midbrain nuclei, although hippocampal connections with these nuclei were enhanced. When corresponding correlations from HC and ME/CFS were compared, ME/CFS connectivity deficits were detected within the brainstem between the medulla and cuneiform nucleus and between the brainstem and hippocampus and intralaminar thalamus, but only during task. “
• Evidence of widespread metabolite abnormalities in Myalgic encephalomyelitis/chronic fatigue syndrome: assessment with whole-brain magnetic resonance spectroscopy [2020]
• Severe posterior hypometabolism but normal perfusion in a patient with chronic fatigue syndrome/myalgic encephalomyelitis revealed by PET/MRI [2019]
• Hyperintense sensorimotor T1 spin echo MRI is associated with brainstem abnormality in chronic fatigue syndrome [2018]
  “the T1wSE group comparison detected decreased signal-levels in CFS in a brainstem region (cluster-based inference controlled for family wise error rate, P_FWE= 0.002), and increased signal-levels in large bilateral clusters in sensorimotor cortex white matter (cluster P_FWE < 0.0001). Moreover, the brainstem T1wSE values were negatively correlated with the sensorimotor values for both CFS (R² = 0.31, P = 0.00007) and healthy controls (R² = 0.34, P = 0.0009), “
• Brain abnormalities in myalgic encephalomyelitis/chronic fatigue syndrome: Evaluation by diffusional kurtosis imaging and neurite orientation dispersion and density imaging [2019]
  “In the ME/CFS patients, we observed significant FA decreases in the genu of the corpus callosum and the anterior limb of the right internal capsule (P < 0.05),”
• Brain function characteristics of chronic fatigue syndrome: A task fMRI study [2018]
  “The results suggest the brain responds differently to a cognitive challenge in patients with CFS, with recruitment of wider regions to compensate for lower information capacity.”
• Intracranial compliance is associated with symptoms of orthostatic intolerance in chronic fatigue syndrome [2018]
  “Within the patient group, higher severity of OI symptoms were associated with lower intracranial compliance (r = -.346, p = .033) and higher resting perfusion (r = .337, p = .038). In both groups intracranial compliance was negatively correlated with cerebral perfusion.”
• Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients [2018]
  “The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. “
• Cortical hypoactivation during resting EEG suggests central nervous system pathology in patients with chronic fatigue syndrome [2018]
  “The implications of abnormal cortical sources in patients with CFS are discussed.”
• Elevations of Ventricular Lactate Levels Occur in Both Chronic Fatigue Syndrome and Fibromyalgia [2017]
  “Mean CSF lactate levels in CFS, FM and CFS+FM did not differ among the three groups, but were all significantly higher than the mean values for control subjects.”
• Decreased Connectivity and Increased Blood Oxygenation Level Dependent Complexity in the Default Mode Network in Individuals with Chronic Fatigue Syndrome [2018]
  “This study showed that DMN activity is more complex and less coordinated in CFS, suggesting brain network analysis could be potentially used as a diagnostic biomarker for CFS.”
• Grey and white matter differences in Chronic Fatigue Syndrome – A voxel-based morphometry study [2017]
  “patients had larger GM volume and lower WM volume. The voxel-wise analysis showed increased GM volume in several structures including the amygdala and insula in the patient group. Reductions in WM volume in the patient group were seen primarily in the midbrain, pons and right temporal lobe.”
• Altered right anterior insular connectivity and loss of associated functions in adolescent chronic fatigue syndrome [2017]
  “These results suggest a distinct biological signature of adolescent CFS and might represent a fundamental role of the dAI in motivated behavior.”
• Multimodal and simultaneous assessments of brain and spinal fluid abnormalities in chronic fatigue syndrome and the effects of psychiatric comorbidity [2017]
  “Thirteen of 26 patients had abnormal values on two or more of these 4 brain-related variables.”
• Prefrontal Structure Varies as a Function of Pain Symptoms in Chronic Fatigue Syndrome [2017]
  “Individual variation in the presence of pain, rather than fatigue, is associated with neuronal alterations in the dorsolateral prefrontal cortex of patients with CFS.”
• Chronic fatigue syndrome in women assessed with combined cardiac magnetic resonance imaging [2016]
  “In patients with CFS, CMR demonstrated lower LV dimensions and a mildly reduced LV function. The presence of myocardial fibrosis in some CFS patients suggests that CMR assessment of cardiac involvement is warranted as part of the scientific exploration, which may imply serial non-invasive examinations.”
• Autonomic correlations with MRI are abnormal in the brainstem vasomotor centre in Chronic Fatigue Syndrome [2016]
  “Abnormal regressions were detected in nuclei of the brainstem vasomotor centre, midbrain reticular formation and hypothalamus, but also in limbic nuclei involved in stress responses and in prefrontal white matter.”
• Evidence in chronic fatigue syndrome for severity-dependent upregulation of prefrontal myelination that is independent of anxiety and depression [2015]
  “MRI regressions with depression itself only detected associations with WM volume, also located in prefrontal WM”
  
• 
  

Bottom Line Model

I believe the best model is based on Evidence of widespread metabolite abnormalities in Myalgic encephalomyelitis/chronic fatigue syndrome: assessment with whole-brain magnetic resonance spectroscopy [2020]. Metabolite abnormalities can be a direct result of microbiome dysfunctions. Those abnormalities are very treatable using microbiome tests and expert systems such as generated by Microbiome Prescription.

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.