Modern environmental factors

If we are looking at a systems condition with likely multiple, perhaps interrelated factors to initiate or predispose we need a lot more nuance

How does a note detailed map look when adjusted for
population density
Rural v urban
Age in each state
Occupation ( linked to potential exposure)
Time of year of onset ( if we are looking at environmental factors, rural plus rotas for pesticide, herbicide, fertiliser for crops and dipping / spraying for animals may factor)
Air circulation routes for environmental factors and airborne pathogens . Some of these darker areas are edges of Hadley/ Ferrel / polar cells where air currents drop or pick up and change direction

I think it's a combination. Network analysis of different variables would be a good idea

 

Diagnosis does not equal onset .
Many have taken years to be diagnosed and some never are

 

Hear hear.

Me: Onset 1976. Diagnosis 1999.

Aunt: Onset 1935. Diagnosis 2006.

 

The previous map was from 2022. I found CDC Long COVID Household Pulse Survey, which is done around monthly, up to last month.

These aren't exactly beautifully uniform between months. Flipping through months, I can't visually see any regions or states that are consistent.

March 5, 2024 - April 1, 2024


February 6, 2024 - March 4, 2024


Searching for maps of worldwide prevalence of Long COVID or ME/CFS reminds me that getting detailed, consistent data for ME will be near impossible in the near or maybe even long term. With so many people undiagnosed, so many regions that don't even know it's a thing, and different attitudes towards seeking a diagnosis, I don't know how it could be done.

I don't know much about network analysis, but I was hoping to play around with various variables, like the ones @Amw66 mentioned, along with prevalence, looking for patterns with machine learning (though which I also am a beginner with, so it would unlikely be very fruitful). Maybe I'll try to think of another chronic condition that could potentially have unknown environmental risk factors but where diagnosis is straightforward and data exists worldwide.

 

@forestglip I suggested about that you check out Dr Li's (Jackson Laboratory) metabolomics talk (part of the NIH metabolism webinar). There are references to xenobiotics being relevant - I'd guess products of (gut) microbial metabolism - probably not what you had in mind re "environmental factors" though!

 

Thanks, will check it out when I have more energy!

Link to the referenced talk (starts at 17:25)

 

They won't be consistent ; it's correlations with variables that would be interesting, not consistency over time.
Our atmosphere is in constant flux as there is life , no flux is a dead planet.
Crops get planted and sprayed, polar cell circulation strength varies ( vortex strength has been an issue over past few years , dragging air circulation into different areas) . New variants have different transmissibility.

Again I would say - get a climate change physicist on board . This disease is a similarly huge enigma.

 

Yes, most of it. It was quoting weak papers at the fringe of research that suggest something is significant when the weight of evidence goes the other way. The scientific literature is now awash with disinformation from people writing stuff that hypes fringe ideas (and also mainstream dogma ideas that don't actually have any foundation). Charbots will pick up on that and spout it out.

 

I think those are prevalence data rather than incidence?
The peak times of incidence of ME/CFS are in adolescence and mid thirties for women (probably not men for that).

Most typical autoimmune disorders have an incidence that rises with age, like breast cancer, with a slight downturn in very old age probably due to an artefact relating to increasing proportion of genetically non-susceptible individuals.

On the other hand T cell mediated diseases tend to occur in early life - unsurprisingly perhaps since the thymus involutes.

MS is interesting in that it has a peak around 30. I don't think we understand that at all unless it has something to do with being related to late acquisition of EBV (i.e. teens to twenties).

 

Especially as, if we accept that ME/CFS has

• a clear association with Epstein-Barr virus
• a clear association with COVID-19 virus

we have enough causation theories. What we need is an understanding of the puzzling chain of events that can result.

 

The charts say diagnosis. I'd assume typical diagnoses not that far off from onset. I'm sure there are exceptions though.

That would be a good data to know. Any link to a stat? I don't think we can simply assume ME/CFS to be immune/autoimmune disease and extrapolate.

 

It occurred to me Tortoise or Koala with ME/CFS could survive longer than a lion with ME/CFS. They don't have to chase food or run from predators. But then, we won't be able to tell tortoise with ME/CFS from healthy tortoises, so we still won't be able to observe

ME/CFS as hibernation perhaps? I'm not exactly a fan of Robert Naviaux and his theory, but hey, everything is just about equally possible at this point.

 

I looked at it again just to make sure. It said "diagnosed rate per 100,000". I take it to mean the diagnosis rate, not number of people with the diagnosis. Are you sure it's the cumulative prevalence? I mean, it doesn't make sense for the cumulative total to go down between 40s and 50s, unless people with ME/CFS dies en masse in their 50s. (Edited: added one more sentence at the end).

 

Yep, survival matters only because you have no chance at reproducing if you are dead. And you can't reproduce either if the peahens don't let you. Grandmother theory says non-reproducing members can still contribute to the evolution, but I don't think it is proven.

 

They say with a diagnosis i.e. prevalence of having a diagnosis, not time of being diagnosed.

I don't remember the details but the most useful incidence study is from Norway and it shows peaks in late adolescence and thirties roughly. We had a discussion about it maybe around 2018. The second peak in males may be an artefact but it looked real in females.

 

There are all sorts of reasons why these data show apparent anomalies. People whose illness started thirty years earlier may never have got a relevant diagnosis. People get better. People may stop wanting to say they are ill. Response rates at different ages may bias sampling in different ways.

The method of ascertainment in that study looks pretty crude and open to all sorts of bias, just being through insurance data.

 

BMC Med. 2014 Oct 1:12:167. doi: 10.1186/s12916-014-0167-5.
Two age peaks in the incidence of chronic fatigue syndrome/myalgic encephalomyelitis: a population-based registry study from Norway 2008-2012
Inger Johanne Bakken 1, Kari Tveito 2, Nina Gunnes 3, Sara Ghaderi 4, Camilla Stoltenberg 5 6, Lill Trogstad 7, Siri Eldevik Håberg 8, Per Magnus 9

 

@forestglip I bring good news! I recall that glyphosate (very commonly used weed killer) turned up a few years ago. From memory a small metabolomics study identified a pathway which relates to glyphosate metabolism. The good news is therefore that metabolomics can identify environmental triggers. If you consider Jonathan's comments above then you'll see the issue --- you'd expect the epidemiology to show higher prevalence in gardeners etc. i.e. those with a higher exposure to glyphosate.
I suggested you check out Dr Li's (Jackson Laboratory) metabolomics talk [part of the NIH metabolism webinar]. One of the questions Dr Li responded to was --- doesn't one of these metabolomics studies identify where the individual lives down to post code? This illustrates an issue --- you always find some false positives [EDIT - you might just be identifying those with a unusual breakfast cereal]!

Of course common variant genetic studies - GWAS [DecodeME] - is also a way to identify potential enviromental triggers --- genes which decrease/increase risk.

I'm hoping that DecodeME will provide clues and indeed metabolomics studies - Dr Li states that metabolite coverage is very low in ME/CFS - we haven't looked at most metabolites.

I'm hoping that this is an iterative process e.g. DecodeME finds genetic clues to focus research and metabolomics can therefore be more focused/targeted --- I'd settle for progress via other routes though!