Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection, Captur et al, 2022

Background
The majority of those infected by ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) during the UK first wave (starting March 2020) did not require hospitalisation. Most had a short-lived mild or asymptomatic infection, while others had symptoms that persisted for weeks or months. We hypothesized that the plasma proteome at the time of first infection would reflect differences in the inflammatory response that linked to symptom severity and duration.
Methods
We performed a nested longitudinal case-control study and targeted analysis of the plasma proteome of 156 healthcare workers (HCW) with and without lab confirmed SARS-CoV-2 infection. Targeted proteomic multiple-reaction monitoring analysis of 91 pre-selected proteins was undertaken in uninfected healthcare workers at baseline, and in infected healthcare workers serially, from 1 week prior to 6 weeks after their first confirmed SARS-CoV-2 infection. Symptom severity and antibody responses were also tracked. Questionnaires at 6 and 12 months collected data on persistent symptoms.
Findings
Within this cohort (median age 39 years, interquartile range 30–47 years), 54 healthcare workers (44% male) had PCR or antibody confirmed infection, with the remaining 102 (38% male) serving as uninfected controls. Following the first confirmed SARS-CoV-2 infection, perturbation of the plasma proteome persisted for up to 6 weeks, tracking symptom severity and antibody responses. Differentially abundant proteins were mostly coordinated around lipid, atherosclerosis and cholesterol metabolism pathways, complement and coagulation cascades, autophagy, and lysosomal function. The proteomic profile at the time of seroconversion associated with persistent symptoms out to 12 months. Data are available via ProteomeXchange with identifier PXD036590.
Interpretation
Our findings show that non-severe SARS-CoV-2 infection perturbs the plasma proteome for at least 6 weeks. The plasma proteomic signature at the time of seroconversion has the potential to identify which individuals are more likely to suffer from persistent symptoms related to SARS-CoV-2 infection.
Funding information
The COVIDsortium is supported by funding donated by individuals, charitable Trusts, and corporations including Goldman Sachs, Citadel and Citadel Securities, The Guy Foundation, GW Pharmaceuticals, Kusuma Trust, and Jagclif Charitable Trust, and enabled by Barts Charity with support from University College London Hospitals (UCLH) Charity. This work was additionally supported by the Translational Mass Spectrometry Research Group and the Biomedical Research Center (BRC) at Great Ormond Street Hospital.

https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(22)00475-3/fulltext

 

"Big if true" in my opinion. It's early stage research. I hope it gets validated.

 

Journalist and economist Jason Murphy replying to Prof Danny Altmann on Twitter (1/2):

"[...] i'm about to click but is this one of those things where if you add in a thousand different cytokines you can get a linear regression model with area under the curve of like .70? because I won't be impressed!"

Jason Murphy on Twitter (2/2):

"Fair play, Prof. AUC of 1 is a result and your proteomic profile is not *too* stuffed with crappy peptides whose sole known function is to prop up biostatisticians regressions. Would love to see if the result holds up if done again. "

(Not able to read more and no idea what "AUC of 1" means, just thought that second tweet could be encouraging.)

 

To greatly simplify, AUC is area under curve. It's a measure of how accurate a test is as the cut-offs for positive vs. negative are changed.

 

I believe Jason has ME/CFS. He had this story about Alem Matthees and PACE published in 2017:

How Alem Matthees’ letter helped solve Chronic Fatigue Syndrome mystery

 

“The researchers couldn’t really split their data into a training data set (for developing the tool by machine learning) and a test data set for internal validation, because they didn’t have enough data. The tool was developed using data on the level of 91 different proteins, but for just 52 patients, those who developed antibodies (“seroconverted”). That’s a pretty small number for developing this kind of predictive tool. Of those 52, just 11 had long Covid in the way defined in this study (persistent symptoms continuing for a year or more). Given that the random forest method can be pretty flexible in the way it learns from the data, it’s not very surprising that the results from applying the tool to just these 52 patients seems to correspond exactly to whether they, in fact, had long Covid."

Well yes, it is like I imagine when you are using it for other contexts - the simplest form many already understand (as it is used in websites like Amazon or social media ads) being 'prospecting'. They analyse who responded to what and ended in the most 'desired result' (those who bought the product rather than just clicked through to look at it) and see what they can find out on them to work out profiles normally based on behavioural/demographic etc data. That is then used to target, and your 'test data' might be based on the next however many page viewers that come through to see if that pattern holds up/how predictive it is.

Of course at this stage it now isn't 'tested' to see if the same thing works in larger 'test' data yet, because that's the next part. But the fact they found 20/90 differences is something given the spiel about 'nothing can be found' for ME/CFS. Not finding those would have made recruiting that larger number less justified I guess. It's a 'this is worth looking into' result. Early days don't get too excited we get, this is really just sign off that it's worth trying the real thing?

At least, despite his keen to mention the title 'random forest' a lot, it hasn't been contrived by human hand in the way a number of BPS papers in the past have taken many measures then switched them round and done strange calcs to 'find something' then stretch logic to make that mean something they wanted to claim. These could be something of nothing, but @rvallee makes a good point of this potential upside in another thread.

Maybe the gent could use his words to talk about 'power' with regards statistics/research - there are hints of him knowing this in his early paras. And how design is all-important for this vs just sample size. There may be many who read the SMC site who could use that expertise being address using examples and detail to get it across well? Have I seen him asked in to do such a useful technical analysis on the methodology and stats/probability/robustness of the findings for the BPS ones in this way?


What I found more interesting was that it revealed there are things that are publicly acknowledged could be used for those who are known likelihood for long covid in order to prevent it if caught: antivirals. And for long covid we do have large numbers already who sit in the group as they have LC, ME, Post-viral in their history.

Which makes this great research re: finding out what is going on by doing research longitudinally surely more the important part re: potential for this. Not the mere prevent illness, if they aren't already enacting the policy of providing those based on increased susceptibility already - or is that due to 'already damaged goods' theory?

 

good article

 

I have little idea whether the study really means anything, and the limit of my maths capability was reached at long division, but I did like this.

 

Plot true positive vs false positive on an x/y graph (or sensitivity vs specificity) and optimise for the greatest Area Under the Curve.
https://en.wikipedia.org/wiki/Receiver_operating_characteristic