Amatica Patient-centred chronic disease research

"If you’re a Clinician or patient who knows a proactive Long Covid & ME/CFS clinician - we’d love to talk.

Please reach out via dm or email (team@amaticahealth.com)

Clinicians are integral to acquiring high quality data, solving the disease won’t be possible without them".

 
For what it's worth, I reached out a few weeks ago about some of the methods used in their labs. Specifically, I'm interested in high AngII/long Ang1-7 in Long Covid, and their package includes those tests. From what I am able to dig up online, these tests are not readily available commercially and require flash freezing of blood samples, etc. So, I asked if he could tell me about the methods used for testing AngII and Ang1-7. I never got a response, which is probably telling.
 
"Coming soon we have a series on our first PEM findings.

Below: one patient's ACE levels before and after PEM

Despite being for many patients the single symptom that prevents them from returning to life, PEM is not well understood. Our goal is to add much needed resolution".
 
Answers by them from the replies
Will make a list when I have some time of anecdotes I’ve heard for different treatments!
Gives me hope that is largely just dysfunction vs damage. I think many treatments or cures could maybe take months, but symptom modulation and severity changes can definitely be almost instant
[question about very severe]
For sure. I think it will be more difficult and they may be more sensitive. But I know of reports of very severe people having huge gains over night with different treatments.
 
Sorry to revive this, I used to be a big fan at the start but I'm not anymore.

Unfortunately, they demonstrate very poor data hygiene and overall understanding of how to interpret data.

For example, looking at the before-after scatterplots above, there is no difference at all. Cherry picking one data point to prove a point is a huge red flag. They cannot interpret their data well at all.

Another problem is they simply don't have enough HC and they also forget that HC may have different biomarkers.

Also, their thesis of subgroups will likely fall victim to Simpson's paradox - "a statistical phenomenon where a trend seen in different data subgroups reverses or disappears when the subgroups are combined, "
 
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"We also include overlapping conditions when relevant (eg fibromyalgia, POTS, MCAS, EDS, chronic Lyme).

Many patients sit at the intersection of these, and comparing patterns may be informative".

 
"PEM focus:

We’re planning a before-and-after exertion study (pre vs post exercise sampling) and we’re analysing long-form PEM descriptions from many patients to look for recurring patterns and rarer “5%” cases".
 
A lot of their subgroup philosophy comes from reading anecdotes on X and Reddit about 1000 different drugs that worked for recovery.

They fail to realise majority of the stories are fake.
 
1 AVP Z ≈ +4.74 Role: vasopressin stress axis / hypothalamus autonomic nervous system ➡️ strongest abnormality in the report.
I'm not understanding this neophyte. Your results on the chart are only significantly outside the reference range for 'mast cell and allergy related', 'neutrophil related' and maybe 'complement system related'. Only one of the specific genes mentioned seems to be described as being relevant to any of those three groups.

Do you know how they determined the reference ranges? I suspect they have been produced from an inadequately sized sample.
 
I'm not understanding this neophyte. Your results on the chart are only significantly outside the reference range for 'mast cell and allergy related', 'neutrophil related' and maybe 'complement system related'. Only one of the specific genes mentioned seems to be described as being relevant to any of those three groups.

Do you know how they determined the reference ranges? I suspect they have been produced from an inadequately sized sample.

Excuse me, my friend, I made a mistake in the analysis. We are 60, but more than 250 will be added next month. 75 controls. More than 1220 genes analyzed. I'm sharing the data with you not for medical advice, as I know Amatica isn't very popular here, but that's okay. It matches my symptoms very well... The only problem: the blood test was in July, and I was still quite severe then, not as severe as I am now.

I'm sharing my detailed results with @amaticahealth regarding the genes (|Z| ≥ 2).

Is it really MECFS, or a combination of syndromes that, together, are causing this mess, with PEM aggravating the body, the brain, the genes... each time?

In any case, the results perfectly match my physical experience. This is what 4 to 5 years of untreated illness without pacing looks like (I didn't think I had MECFS):

1. Brain / neuronal activity
Abnormal genes:
ENO2 ⬆️ +2.60 → neuronal activation / neuronal stress
DRD2 ⬆️ +2.08 → altered dopamine signaling
SNAP25 ⬆️ +2.06 → increased synaptic activity
PER1 ⬆️ +2.05 → circadian rhythm dysregulation
SEMA3A ⬆️ +2.04 → neuronal remodeling
suggests increased neuronal activity.

⚡ 2. Glutamate / neuronal excitation
Abnormal genes:
GRIK4 ⬆️ +5.91 (most abnormal gene in the dataset)
CACNG2 ⬆️ +3.31
GRM6 ⬆️ +2.31
ARHGEF9 ⬆️ +2.04
signature of increased glutamatergic excitation.

3. Hypothalamic stress axis (HPA axis)
Abnormal genes:
AVP ⬆️ +4.74 (very high)
CRHR2 ⬆️ +2.64
CYP17A1 ⬆️ +2.26
PER1 ⬆️ +2.05
indicates overactivation of the hypothalamic stress axis.

4. Inflammation / immune system
Abnormal genes:
NOS2 ⬆️ +3.53
LRBA ⬆️ +2.35
SERINC5 ⬆️ +2.12
moderate immune activation.

️ 5. Complement system
Abnormal genes:
C8B ⬆️ +2.67
C4BPB ⬆️ +2.32
complement pathway activation.

⚡ 6. Mast cell / immune signaling
Abnormal gene:
PLCG1 ⬆️ +2.03
immune signaling / mast cell activation.

7. Mitochondria / energy metabolism
Several abnormalities.
Overexpressed genes
TRAP1 ⬆️ +3.39
POLG2 ⬆️ +2.84
ACAD9 ⬆️ +2.64
DELE1 ⬆️ +2.60
SUPV3L1 ⬆️ +2.45
TRMU ⬆️ +2.42
COQ6 ⬆️ +2.41
PRKN ⬆️ +2.33
MRRF ⬆️ +2.32
MTERF3 ⬆️ +2.30
GUF1 ⬆️ +2.29
NFS1 ⬆️ +2.27
ELAC2 ⬆️ +2.17
RARS2 ⬆️ +2.15
COQ9 ⬆️ +2.07
TIMM44 ⬆️ +2.04
GFM1 ⬆️ +2.00
Underexpressed genes
COX5B ⬇️ −2.20
MPC1 ⬇️ −2.12
TXN2 ⬇️ −2.12
MPC2 ⬇️ −2.05
indicates mitochondrial stress / energy metabolism disruption.

8. Neuromuscular junction / autonomic signaling
Abnormal genes:
CHRNA3 ⬆️ +3.28
COLQ ⬆️ +2.79
RIC3 ⬆️ +2.41
SNAP25 ⬆️ +2.06
neuromuscular / autonomic dysfunction.

9. Autoimmunity / immune regulation
Abnormal genes:
LRBA ⬆️ +2.35
TREX1 ⬇️ −2.83
immune regulation abnormalities.

10. NK cells
Abnormal gene:
KLRG1 ⬆️ +2.13
altered NK cell activity.

Most abnormal genes overall
Top genes in your dataset:
GRIK4 +5.91
AVP +4.74
NOS2 +3.53
TRAP1 +3.39
CACNG2 +3.31
CHRNA3 +3.28

Main biological patterns in your RNA profile
The dominant patterns are:
1️⃣ Increased glutamatergic neuronal excitation
2️⃣ Hypothalamic stress activation (AVP)
3️⃣ Mitochondrial stress
4️⃣ Autonomic nervous system dysfunction
These patterns are commonly reported in:
Myalgic Encephalomyelitis / Chronic Fatigue Syndrome
Postural Orthostatic Tachycardia Syndrome
 
It looks as though you have 47 genes with a z score over 2. You say they looked at 'more than 1220 genes'. So, with normal distributions, we could expect at least 54 gene expressions to fall outside the 95.45% range. You had less hits than what we might expect from chance.

I think it would be interesting to take a sample of the controls, and see how many of their results were outside the Z<2 range.
 
It looks as though you have 47 genes with a z score over 2. You say they looked at 'more than 1220 genes'. So, with normal distributions, we could expect at least 54 gene expressions to fall outside the 95.45% range. You had less hits than what we might expect from chance.

I think it would be interesting to take a sample of the controls, and see how many of their results were outside the Z<2 range.

I don't know, my friend... I have over 200 genes that are either too high or too low compared to the average of 75 control groups. 47 are above Z2. There are 14 categories in total. Within each category, there are subcategories with a description and my value for each gene, as well as the control value.
 

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