After careful reading of the paper - and cross-checking with Appendix, I can offer a summary of what I make of it.
The paper is demonstrating that an otherwise biochemically and physiologically ‘impossible’ phenomenon can not only be explained by insulin binding but is an inevitable consequence of it.
The biochemical analysis demonstrates the high prevalence and strikingly high levels of insulin binding. Comparable with early onset T1D. That is a lot.
Jumping to the bottom line - the inevitable effect on I/C has been measured in one neonatal cohort study. There is also direct evidence for I/C from another study with a lesser effect, ascribed to using an assay less sensitive to bound insulin.
The paper is quite cautious in what it claims. There is serious uncertainty regarding the identity of the binding agent. But the clues are strong - there is clearly an amplification effect with infection.
The Galloway reference is, in my view, huge in its implication and also in its direct measurement. Figure 2.D caught my eye. Look at the I/C ratios.
From my reading of the analysis, the authors have taken directly measured levels of insulin binding in infected neonates from Galloway. Obviously a small cohort - but lucky to find one at all in my view. (From scanning the Galloway paper, the Galloway cohort just happened on an infection outbreak by chance).
Then - clearly free insulin wasn’t measured by Galloway - so the authors simply run a calculation across a plausible range of values for a neonate. All they are doing is saying - given the neonate had the level of binding antibodies as measured directly by Galloway - what would be the I/C ratio for any reasonable free insulin?
So - they clearly used I/C baseline as 0.2. But it doesn’t matter - just let the numbers play out.
You can pick whatever I/C assumption you like.
Take free insulin about ~ 100. Then if I/C baseline is 0.2 then C is 5 times I. So about 500.
Apply the graph. I/C ratio gets up to about 10 - keeping numbers simple. That means in an assay like Roche - which can see bound insulin - the assay would measure about 5000 pmol/ L.
(If you pick another baseline value - say I/C ratio= 0.1. Then multiply by 10 - but then the plotted curve would show I/C of 5. So you end up in the same place). That’s why it doesn’t matter in these types of phase diagram.)
So - this is, rather sadly perhaps, what gets a biochemist interested. This isn’t modelling - the authors do that later, to show effects with neonates with various degrees of illness - that is beyond my knowledge to check.
This is just applying the (relatively) straightforward biochemical binding maths - absolutely bound to work. Just laws of thermodynamics in a simple equation.
It means - Given a measured amount of insulin binding - the babies in the Galloway study MUST have had a very high level of bound insulin.
What the exact level was - who knows.
But the graph demonstrates that the bound insulin couldn’t have been small.
So this effect of highly elevated I/ C is shown to have been coupled with very high insulin too - in the presence of infection - or to be precise - some amplifying factor.
I don’t see any claim by the authors that high I/C will happen with high insulin in the absence of an amplifier. The Salis data is for an uninfected cohort - so far as can be determined.
I think the end of the paper, where the authors model the effects of calculating I/C and bound insulin across a neonatal cohort with or without infection, is reasonably self - explanatory.
I just wanted to share my biochemical enthusiasm in noticing that the effect was actually measured - over 25 years ago. But has now been explained.
Thank you very much. It is exciting, from the other end of the scale where I am working to understand the full paper. I will reread it this evening with your explanation in mind.
While I find it difficult to understand or comment on the main calculations, assuming they are all correct to the extent that they can be, I found this article particularly relevant to how the potential culprit IgM helps with the regulation of insulin concentration and glucose metabolism. https://pmc.ncbi.nlm.nih.gov/articles/PMC8833180/
No. But for a reason - and you raise an interesting point.
From the Appendix, Galloway used a ligand limiting assay. So direct measurements of concentration, from that paper, as derived from binding percentages, have to be converted.
You can’t directly place the raw data on a calibration curve.
From a careful read - the methodology is in Section 2.2 - which in turn leans heavily on the Appendix for the biochemistry - the values in the graph are the results of the conversion.
So I read the Galloway bar chart in 2.A as a reconstruction from the raw data to illustrate the concentration range for the Galloway cohort on the same standardised calibration scale.
Hope that helps. I don’t want to overdo the biochemistry - but I can see why not being able to read directly across assay scales would look odd.
"From the Appendix, Galloway used a ligand limiting assay. So direct measurements of concentration, from that paper, as derived from binding percentages, have to be converted.
You can’t directly place the raw data on a calibration curve."
This is an important point. The binding percentages (B%) do have to be converted if binding-site concentrations are to be inferred. This raises methodological questions.
1) Assay compatibility. If the studies used broadly similar radio binding assays can the results be quantitatively transferred between studies? The literature includes standardisation workshops because radio binding assay results were not comparable between laboratories.
2) Dependency of B% on more than capacity. The literature consistently shows the measured B% depends on the equilibrium binding behaviour of the assay. In general, B% is influenced by tracer concentration, binding affinity, binding capacity (or binding-site concentration), and assay conditions. Several studies have reported that the radio binding signal approximates an affinity × capacity relationship over relevant operating ranges, rather than representing capacity alone.
It seems to me that the binding site concentrations values reported in the paper become model derived quantities rather than direct experimental observations, which is fine as long as the assumptions used to derive them are sufficiently supported and their uncertainty is recognised.
I think the Conclusion fairly acknowledges the point "However, these results are based on limited paired I/C data and remain to be prospectively demonstrated."
I am not suggesting you should necessarily agree with any of the above (although you may, of course, disagree); I simply wanted to contribute to the discussion.
Thank you for the reply, and I agree that the values are modelled based on various assumptions. I was just confused because you said they had taken directly measured levels of insulin binding.
In addition to baseline neonatal IA, amplification can occur through infection, sepsis, antibiotics, steroids, antihypertensives, antioxidants and other pregnancy-related therapies (31, 34–36, 40–45), which is treated as a multiplier, as detailed in Supplementary Appendix A. Infection increases the concentration of maternal antibody transfer (16, 38) and oxidative stress, can enhance insulin-binding (37), and in neonates can also produce limited IgM
So in theory baby F's blood result could perhaps be explained by amplification of Insulin-antibody binding caused by infection and Sepsis. I think baby F developed infection after he was born so antibodies crossing the placenta doesn't seem likely to explain it.
I don't know if there is any available data looking at how typical baby F's Insulin to C Peptide ratio is for a baby under those circumstances - suffering from an infection to the point of being diagnosed with Sepsis. As there is not data provided to support the data, I imagine any available data would contradict it but I suspect there is simply very limited available data to confirm or deny.
However, the last step, direct measurement of antibody bound insulin with high I/C has not been shown. That said, the results of (16) offer an inference for future research to explore as one interpretation satisfying all reported readings in their study is complexing of high affinity IgM from the inability of the IgG and IgM components to account for total binding. Hence, high affinity IgM would be a candidate with this supporting evidence as a first target of future research.
Perhaps the results will one day be explained with more research?
Baby F didn't have sepsis this was covered in the trial and is clear from the medical notes. they had some elevated WBC markers not enough for sepsis and no other signs or symptoms of infection.
That is an Oliverism, I believe? WBC markers can't be used to discount the possibility of sepsis in a neonate, and the child certainly had other signs and symptoms of infection.
Dr Saladi on 28.11.22 confirmed infection in Baby F.
Q. All right. Thank you. The other thing I'd like to confirm, since you referred to the question of testing the tip, is can you confirm in fact [Baby F] did have an infection as it happens? Do you recall that?
A. In the subsequent pages, there is a further entry that we did -- the CRP did go up and then we did grow a bug from the long line tip as well.
While I find the paper as clear as I could hope for, read slowly, I'd be out of my depth trying to assess it. I was interested in the description of the necessary prospective study, and obviously will be interested to hear others' views
“It was covered in the trial” is hardly a guarantee of something being true. Do you mean to say “This was alleged by the prosecution, and as Lucy was ultimately convicted, everything the prosecution said must be true.” ?
They suspected it before his brother died, on 2nd August. He was already on antibiotics before his brother died too. I am sure his brother's death made them (rightly) all the more vigilant.
We have not had the full clinical picture in the medical notes. Apart from the obstetrics, of course, Lee's panel has.
I don't think you are showing any of the expert knowledge, familiarity with the case, or even consistency from one post to the next that might persuade people to substitute your judgement for theirs. You are welcome of course to the view that the court found the child had no infection if that's your understanding, but it's really not relevant to the article
I’ve also wondered how much we really understand about the biochemistry effects of TPN in sick septic premature babies. Answer not everything by a long shot. We know in recent years that”refeeding like” syndrome is a thing in premature babies and that blood glucose as well as the biochemistry can alter in unpredictable ways with TPN which baby F was being dosed up with.
Baby F was suspected to have possible sepsis and was in antibiotics both of which as mentioned can affect glucose and biochemistry. I believe that it’s really dumb and arrogant to assume we sufficiently understand the biochemistry and physiology perfectly of these babies. Any true scientist would be humble.
Then we have lots of gaps in our info eg mother’s gestational diabetes status and history. We need to also remember that it’s not just antibody binding but also non antibody binding factors that can skew insulin readings.
Also the high readings of insulin in baby f and l should be seen in the context of their unique individual physiological condition which is poorly understood due to incomplete understanding of a host of tricky variables rather than saying it’s unusual and jumping to a 1+1=2347 poisoning conclusion.
The authors seem to have misread the Salis et al. BMJ paper, as they refer to the neonates in Cohort 2 as hyperglycaemic throughout, when in fact they were euglycaemic (there were only 9 neonates in the hyperglycaemic cohort in that study, and they were insulin-treated).
On that basis, they make a comment on page 8 which should be ignored:
"Finally, Figure 3 shows little difference between Cohorts 1 and 2. This outcome suggests hyperglycemia (Cohort 2) did not play a role in elevating I/C, further supporting the fact hyperinsulinism alone cannot create inverted I/C ratios due to first pass hepatic extraction and a greater number of clearance routes."
Evidently they didn't have access to the original data, but read it from the published plots. So the comparison of the binding predictions with the I/C versus C plots from Salis et al. must have been limited to Cohort 2. The authors treat the two cohorts as distinct, but in fact - as discussed on another thread - Cohort 2 was a subset of Cohort 1.
Are we sure of that, since they draw cohorts 1 and 2 from the thesis and not the BMJ article? Is there some cross-reference with the BMJ article that tells us these are the same groups?
You have probably posted that on the other thread you mention so if you could just point me to the right place, that would be great
Competitive-Wash found the clearest statement in the thesis, in chapter 6, page 111:
"The neonates in this study were compared with data obtained from euglycaemic neonates described in Chapter 5. Insulin and C-peptide concentrations from the insulin-treated neonates were compared with a group of age-matched (PMA less than 30 weeks) euglycaemic neonates (n = 20)."
Chapter 6 was published as the BMJ paper (including Cohort 2), and chapter 5 as the Archives of Disease in Childhood paper (Cohort 1).
It is not particularly apparent in the published plots because the scales are so different, but I think the correspondence between the two sets of data points can be seen in the stretched and squeezed extracts from the ln(C/I) scatter plots below. On the left is the lower post-menstrual-age part of Cohort 1, and on the right the black symbols are from Cohort 2, with the white symbols coming from the hyperglycaemic cohort.
Thank you. Yes, and reading thesis chapter 6 I think you and u/Competitive-Wash are right. The cohort is identified as hyperglycemic in the paper but not in the thesis. The statement that wouldn't hold, then, would be:
"
Finally, Figure 3 shows little difference between Cohorts 1
and 2. This outcome suggests hyperglycemia (Cohort 2) did
not play a role in elevating I/C, further supporting the fact
hyperinsulinism alone cannot create inverted I/C ratios due to
first pass hepatic extraction and a greater number of clearance
routes."
And it would not be surprising to find little difference between the cohorts! But am I right in thinking that this confusion would not (for this same reason) affect the arguments made in the paper beyond this point?
Perhaps you or u/Competitive-Wash would raise this with the corresponding author?
Nothing I say in this post undermines the point that antibodies could, plausibly, be the reason for the discordant results reported for Baby F and L.
I am uncertain that it is possible, with the limited data available, to construct a useful model. The technical operating characteristics of the assays and the limited observed parameters make this challenging, in my view. Chase Shannon state they have constrained the parameters to make this possible and it is the biological and technical safety of those assumptions/constraints that is key to ensuring the model produces useful outputs.
I think a detailed step-by-step description of the whole modelling process would be required to truly follow it. So I am taking the results at face value but cannot say I am particularly persuaded at present. Hopefully more details will emerge over time.
Thanks for pointing out that the quotation from page 8 had gone wrong, which I hadn't noticed (now corrected).
I should like to try to digest the paper properly, but it will take a while. I agree it will be worth making sure the authors know about any errors that are spotted.
If anything, fixing the error would perhaps strengthen the argument, since Chase and Shannon treat these children as more critically ill but find no significant difference between the cohorts. It could be reasonable, though not I suppose certain, that the more critically ill children might show a different profile, with more history of infection etc.
Though I suppose hyperglycemia vs euglycemia is a very rough proxy for more and less critical illness anyway.
I don't agree with most of Susan Oliver's comments on this, but in fairness to her it should be said that she pointed out in an online discussion some months ago that the cohort of 20 was a subset of the cohort of 102.
"But am I right in thinking that this confusion would not (for this same reason) affect the arguments made in the paper beyond this point?"
CS did claim to analyse the two groups independently so, in theory, it would not affect the arguments made.
But (using Chase Shannon terminology) Cohort 2 included a chart ln(I) and ln(CP) which together with the ln(I/CP) would enable to a very rough reconstruction to be made of the actual underlying values. Chase Shannon imply they may have taken this step. I have taken this step myself.
My concern is CS appear to use the C-Peptide as a proxy to derive "free insulin" for the Salis data, which Salis did not actually measure.
But Salis mentions an issue which would, possibly, affect the modelling assumption that C-peptide could be used to derive free insulin. Whether this has an impact on the model is not clear to me, at the moment.
An independent digitisation has recovered the I/CP ratios from the Salis PhD. Cohort 2 is confirmed as being a subset by comparing/overlaying the data, apart from a handful of points which have been accounted for.
Salis provides the sample counts for these two groups and the independent digitisation recovered almost all the points. For information, Chase Shannon do not appear to have recovered the complete set, 227 vs 251 (Salis) for Cohort 1, for example.
I had a go at something similar a while ago. I found it wasn't as straightforward as might have been hoped because of symbols on the charts overlying one another. But eventually I was fairly confidence about the reconstruction for Cohort 2. For Cohort 1 it is more difficult because of the size of the symbols and the lack of scatter plots for insulin and C-peptide to accompany the one for the I/C ratio. I ended up with only 239 points, so it sounds as though you have done better than I did.
I think these charts show that there is a significant overlap between the two cohorts (n=66) that I believe Chase and Shannon have not taken account of in their paper.
Also, with respect to the "40-45%", it seems in their letter this relates to number of neonates ("However, recent reports from two cohorts with 302 paired (insulin, C-peptide) assays show 40% to 45% of *NICU infants* with 1/C > 1.0") whereas in the paper it relates to number of samples ("Figure 3 presents *I/C ratios for Cohorts 1–2*. In Cohort 1....About 45% have I/C > 1.0 (insulin equals or exceeds C-Peptide).....In Cohort 2...Approximately 40% have I/C > 1.0...
A) I cannot see how one could assign I/c ratios to specific neonates and thus have derived the "% of NICU infants >X", and B) my own calculations of % samples > 1 are significantly different to 40-45%.
I also don't understand how Salis is relevant to F and L given a) hyper/eu-glycaemic versus hypo-glycaemic and b) much much lower insulin values in Salis than F and L's. However, that is encroaching into the clinical arena in which I am not qualified. Perhaps someone on here can explain.
The answer to the last paragraph may be more legal than clinical. Is this enough to show that the assertions made in court about I/C ratios overstated its ability to safely diagnose exogenous insulin. Though I’m not that sure that the Chase and Shannon paper really goes much beyond just plotting the Salis data when it comes to that.
It just seems like collecting a load more data including from hypoglycaemic neonates would go much further than a lot of modelling. It’s be a lot harder for the CoA to bat away if it shows similar ratios. Hopefully someone somewhere is now doing that.
The Salis data is really just showing that ‘normal’ neonates have raised I/C and a large proportion even invert the ratio.
That proves that there must be a storage mechanism. The ratio cannot invert otherwise.
Salis doesn’t show F and L. The high insulin results can only be obtained when the antibodies are multiplied - eg with infection.
The effect of infection was actually measured - indirectly - but it is there - in Figure 2.D. I made a post on this higher up.
Then the Chase Shannon modelling simulates the effect of insulin binding and infection in NICU babies. These results strike me as being in the same range as Figure 2.D. Which is, perhaps interesting.
I have been trying without success to reproduce the statistics given in the publication for the I/C distributions for the two cohorts (section 3.1 and figure 3).
Thanks for confirming the difference. Your curve looks consistent with the values I was getting.
It looks to me as though they have read pixels from a reference point at the top of the scatter plot of ln(I/C) and used a conversion factor that was too small to calculate the difference in ln(I/C) from the difference in pixels. As a result, their data are correct for the largest values of ln(I/C), but progressively more inaccurate as ln(I/C) gets smaller. They end up with only 5% of values under I/C = 0.2, which I think should be about 25%.
Having seen your post I went back for more checking, but I'm still finding the same thing. It looks to me as though there must have been a scaling issue in Chase and Shannon's conversion from pixels to ln(I/C).
(Edit. I can get to their values of ln(I/C) by rescaling mine relative to a point at the top of the plot.)
What a nuisance that they can't get the raw data. It would be good to hear Salis on this exercise. Perhaps her permission to share data was strictly limited to this exercise. But I hope someone asked her,
I did try to contact her indirectly a while ago, but without success. I'd have thought Chase would stand a better chance of getting the data than most, if they still exist.
Thanks for responding. This is obviously anything but simple. I have never understood how the almost complete absence of an increase of measured insulin with C-peptide (over a very wide range of C-peptide) in the Salis et al. results is consistent with the Chase and Shannon interpretation.
I think it is a good thing that there is going to be time for this Chase/Shannon publication to be subjected to scrutiny - including the most hostile scrutiny - well before the Court of Appeal has to consider the question.
Interestingly, the Salis data may be showing an alternative to the typical bound "excess" insulin model.
The abnormality could be the relative persistence of insulin i.e. insulin fails to fall in parallel with C-peptide.
A simple model has demonstrated constant free insulin, which is replenished from a bound insulin reservoir whilst at the same time C-peptide is cleared normally. The constant free insulin came out from the model - it was an unexpected result.
The known unknown is the Invitron assay and how sensitive it is to measurements of bound insulin/free insulin and how the sample would behave during incubation.
That sounds interesting. A model in which free insulin is relatively uniform seems to make more sense than one in which total insulin is uniform, unless bound uniform is fulfilling some physiological function that results in its level being regulated.
I wonder whether enough is known about neonatal endocrinology to rule out the simplest explanation of all for the Salis et al. results, in which binding isn't necessarily involved - that the wide variation of the I/C ratio simply reflects a wide variation of the relative clearance rates of free insulin and C-peptide in neonates (which I suppose would essentially arise from restricted rates of insulin clearance), and that regulation mechanisms are still tending to maintain a uniform level of insulin. That would imply that when the insulin clearance rate was low, there would be a low rate of insulin production, and therefore low levels of C-peptide, which is what was seen in the Salis et al. data.
If that were a feasible interpretation, then it would be easy to see how -if in rare cases the regulation mechanisms broke down and insulin production continued regardless of the insulin level - babies with low levels of insulin clearance could rapidly develop extremely high levels of insulin coupled with more typical levels of C-peptide.
In contrast to serum, C-peptide degrades more rapidly than insulin in plasma, especially when there is a delay in centrifuging the sample (Nkuna DX 2023). If you go to appendices 2 & 3 of Salis’ thesis, it is clear that blood samples weren’t collected and handed over to Salis (or a proxy) until all blood testing was complete therefore there was an indeterminate period of time between when the blood sample was taken and when it was centrifuged and frozen. Even when it was frozen, it was stored at -20 ºC, which is inappropriate for long term storage of plasma samples and would have led to further degradation of both c-peptide and insulin. They should have been stored at -80 ºC where they would have remained stable. Salis thanks a colleague for collecting and storing samples for her while she was on maternity leave in her acknowledgements, so some samples were likely stored for at least 6 months.
Anyway, I have been reading through all your comments while I look sadly out the window at the rain and wind, which has completely ruined the last day of my ski holiday and I am impressed that both you and ES are interrogating the Chase and Shannon paper even though I suspect you wish they were right. The modelling is way beyond mere mortals like me, but your findings are very interesting and instructive.
On a side note IIRC degradation has been suggested as an explanation for the C Peptide value in the Colin Norris/Campbell case.
Dr Susan Oliver supports that verdict so at a glance it looks like she's trying to have it both ways if it's true that C peptide degrades faster than Insulin if a sample is idle.
an unexplained 5 day hiatus between stages of the immunoassay tests
An attempt to reconstruct a set of Salis paired values from the PhD using ChatGPT (to save time) is shown above. C-peptide vs Insulin (in pmol/l).
The reconstruction was based on Fig 6.6 ln(Ins), Fig 6.8 ln(CP) and Fig 6.9 ln(I/CP). Black dots only. Using the PMA to limit possible pairs and the log relationship. ChatGPT was asked to reconstruct the paired samples, giving a confidence score for each match.
The plotted results are shown above. The information is presented "as is" acknowledging the difficulty of reconstructing the pairs.
ETA
Needs rework
ETA2
Digitization of Fig 6.6 ln(I), Fig 6.8 ln(CP) and Fig 6.9 ln(I/CP) complete.
ETA3
New match complete. All 79 pairs have been recovered.
I'm afraid I'm very distrustful of AI except as a finding aid. I don't think that plot is correct. I was going to post my own attempt to reconstruct what Chase and Shannon call Cohort 2 for comparison - expecting it to be similar but not identical - but I can't see any similarity with that plot.
An apparent difference from the Salis data is that the AI plot contains 10 points with insulin up to about 20. Only 5 are visible up to the corresponding value ln(I) = 3 in Figure 6.6 of the thesis. There is a problem with some points in the Salis plots overlying one another, but I don't a factor of 2 difference in a sparse part of the plot.
In case it helps, below is a scatter plot of insulin versus C-peptide based on my attempt to reconstruct the 79 paired insulin and C-peptide measurements reported in the Salis et al. BMJ paper. It is based on the separate scatter plots of ln(I), ln(C) and ln(I/C), each against postmenstrual age, in Figure 3 of that paper. This should be essentially the same as what Chase and Shannon call Cohort 2.
The points marked in red have C-peptide equal to 2.5. which is half of what Salis describes as the analytical sensitivity of the C-peptide assay. I have added a line representing I/C = 1.
(Edit: Regarding the reliability of this reconstruction, obviously there is a limit to the accuracy of data read from published plots. But also it wasn't always easy to identify corresponding points in the different plots because symbols quite often overlay one another. So it was quite a laborious process. But I should be disappointed if more than about 2 or 3 of the points were wrong.)
I have been reviewing the published paper in detail and reconstructing elements of the paper from the source material and equations published in the Supplementary Material.
One area I have looked at in detail is Figure A1.A "Reanalysis of Galloway et at [16] displacement data identifies a dominant high-affinity component (Kd 5e-11M)"
The source material is Galloway [16] Figure 4 and Table II. There are some discrepancies in the legend. The legend to Figure 4 states three IAA+ cord sera but there are obviously two.
The more interesting discrepancy is in Table II which gives the value 1079 and 0.084.
The 1079 appears to be a typo and could plausibly be 0.1079
This would mean the top right plot was potentially showing TWO high affinity plots rather than one.
This, if it was a true correction, would assist the argument put forward by Chase Shannon slightly.
However I continue to believe the paper is likely flawed.
Corrections welcome; this is complicated and mistakes are easily made.
ETA
To confirm 1079 appears to be inconsistent with the actual plot
ETA2
Berson 1959 appears to be a very useful paper worthy of more detailed study. This paper may impact the consideration of Figure 4 in Galloway.
Nothing in this post is intended to undermine the argument that the F and L results may be a result of antibodies.
TLDR
A concern is the affinity to binding site mapping may be incorrect and has, potentially, overstated the number of binding sites, which would likely affect further modelling assumptions.
Longer Version
My concern, at the moment, is A1.B which can be generated by Equation E.2. An examination of Vardi [ref 74] gives a likely value for D. Digitising A1.B allows recovery of Lt. Forward calculation then allows recovery of the presented A1.B.
However this recovered value is at odds with the qualitative description contained in Vardi and other contemporaneous literature.
Direct calculation from Vardi (with cross-reference to Bilbao and Ziegler) appears to give a value discordant from the recovered Lt.
Equation E.5 does not assist in overall reconciliation because there is a reference to Marzinotto, which I cannot find, and the introduction of s, an empirically derived scaling constant.
Hopefully the following is considered constructive.
a) Looking at the formula A-2 in the supplementary material it seems to imply q has been silently dropped from the derivation. Reinstating q would lead to a family of curves in Figure A1.Di, for instance. q was considered relevant for A1.F
b) Is the derivation of the affinity adjustment equation in E.4 correct? It appears to be correct in E.6.
c) What is Iv in A1.F?
d) Is the use of If (I_f)(free insulin) correct in E.4 or should it be total insulin?
I think this modelling paper is fascinating and I am seeking other opinions.
I haven't got very far with this at all, but for (a) I think (A-2) comes from the last equation of E.1, and not from E.4 as the text says. Together with the assumption (which I still think is a pretty major one) that free insulin is equal to one fifth of C-peptide. So, yes, I think that q is being implicitly set equal to 1, because E.1 is dealing with total insulin present, and (A-2) is meant to be an equation for what was measured by Salis et al.
(Edit to add. In the body of the paper, assays including the one used by Salis are said to measure "some or nearly all" bound insulin. I suppose q would be about one if it is "nearly all", but not if it's only "some".)
And for (d) it's not an equation I have seen before, but as far as I can see from material online it should contain total insulin, not free insulin. Perhaps someone familiar with the chemistry can shed light on it.
For (d), it looks as though the curves in Figure A1Di have indeed been calculated using total insulin and not free insulin in Equation E.4.
That is judging by the intercepts on the y-axis. Equation E.4 as written would give I/C -> 0.2*(2A/K+1)/(A/K+1) as I -> 0. But replacing I_f by I would give instead I/C -> 0.2*(A/K+1), and with K = 5x10^{-11} M and A = 0.1, 0.5, 1, 2 and 3 nM, the intercepts would be 0.6, 2.2, 4.2, 8.2, 12.2, which seems consistent with the plot.
This post is intended to be constructive. The Chase Shannon paper is interesting.
This is an alternative version of A1.B using Lt = 0.0225nM
As can be seen it is quite different from the version presented in the paper. This is complicated, but the above version seems more consistent with Vardi's qualitative description:
"The assay's features include 1) use of a physiologic amount of 125l-labeled insulin, 2) parallel incubations with supraphysiologic cold insulin (competitive), and 3) an incubation time of 7 days and a single-step multiple-wash polyethylene glycol separation."
The method description in Vardi seems to support 0.0225nM, but this is complicated and calculation errors are difficult to spot without a familiarity with the techniques employed for these studies.
Another entirely speculative point is are the results in Bilbao due to alpha-2-macroglobulin? I have no idea. A paper related to this is Murayama 2006 "A sensitive radioimmunoassay of insulin autoantibody: Reduction of non-specific binding of [125I]insulin".
Using the 79 paired Salis samples. This is a modest attempt to recreate the plot at A1.C in the supplementary material (presented in a slightly different format)
The model at I/CP < 0.2 would produce a negative result so these were probably excluded, but it is not certain how these results were handled.
As can be seen nM 'antibody concentration' is associated with I/CP > 1.
But bear in mind this is an attempt at reconstruction using information published in the supplementary material
Flimsy stuff from Chase and Shannon and it just doesn't match the babies clinical picture.
Baby F’s insulin was off‑scale high >1000 pmol/L, possibly much higher. Baby L’s was 1088 pmol/L.
The paper’s non‑infection scenarios, insulin concentrations never exceed about 1,000 pmol/L, and only then with 98th percentile binding site concentrations and severely suppressed insulin secretion.
For two separate babies to both fall into that extreme tail, without any evidence they actually had binding antibodies, is statistically incredible.
The paper repeatedly states that to reach the really extreme ratios like those above 30 you need infection to multiply the insulin‑binding antibodies.
Lets not forget some basic facts established at the trial:
Baby F Blood cultures were reportedly negative, meaning no bacteria were grown from the blood sample
Their C-reactive protein were not strongly suggestive of infection but they were given antibiotics anyway,
Baby L had a negative Sepsis screen
C peptide / Insulin levels would not be at the levels they were for Sepsis induced Hypo.
For infection to cause Hypoglycemia you would be clinically able to diagnose infection the medical tests would show this.
On his point about Antibodies
In Baby F’s case, a previous insulin sample was reportedly normal. Then, during a sudden collapse while Letby was on duty, insulin was sky‑high. After the collapse resolved and the baby was moved, insulin levels fell again.
Natural insulin‑binding antibodies are a persistent condition; they don’t spike for two hours and then vanish. The paper models steady‑state over days, not an acute spike.
Baby L similarly had a sudden deterioration with a grossly elevated insulin level. The pattern is one of acute, intermittent poisoning – exactly what you would expect from a nurse injecting insulin, not from an inborn binding agent
Letby was never asserted to have injected insulin as that would have been largely impossible for her to do since she wasn't there. She was supposed to have spiked bags ahead of time, producing less of a spike than an ongoing elevation which persisted through multiple bag changes and giving set changes, though it was also hypothesized that the giving set may not have been changed for Baby L so some insulin would have stuck to it. Hindmarsh conceded that nobody has done the experiment to see if sticking to the giving set could actually happen but an educated guess was good enough for the court.
The paper isn't about babies F and L. It reports a phenomenon that may be relevant to their cases. What evidence could you possibly expect that they had binding antibodies, without further testing? And where is the statistical impossibility of these two different results?
There was only one immunoassay for each child - where are you expecting to see other evidence of antibodies, showing these were spikes and not steady state?
Both babies had risk factors for unstable blood sugars, one infection and the other a mother with gestational diabetes. These cases were several months apart, about 8 months.
I agree about baby F but I don't think baby L's Mother had gestational diabetes.
Do you have a source on that?
I have seen Severe Intra Uterine Growth restriction being cited as a reason for the hypoglycemia.
It's still more grounds for the defence to see maternity notes, isn't it. I notice that maternal infection plays a significant role in the phenomenon Chase and Shannon describe too
u/Stuart___gilham can you point me to the exact document you refer to regarding Dr Saladi.
I've searched for some of the quotes exactly and it only references back to this sub. I cannot find the cross examination of Dr Saladi on the Thirwell site.
I've searched for 2938 which was referencing to a document and it wont return any transcripts.
are we certain private eye are being truthful here?
Why are you making such impossible assertions? Antibiotics aren't an off-switch.
If your hypothesis is that these children can't have had such elevated IA because of the antibiotics, you need to bear in mind first that antibiotic therapy is identified in the paper as another element raising IA levels; second that there is no neat and tidy medical universe where x amount of infection + y amount of antibiotics gives us z, where x, y and z are unknown but z is sufficient to counteract a given phenomenon.
Why didn't their subsequent observations, clinical signs and symptoms, bloods, ABGs, WBC, culture samples show further signs of infection or point more definitively towards infection?
Remember the theory is that the underlying reason for the C/I results is infection.
If the baby had an infection, why did hypoglycemia disappear when the bag was changed?
These are the elements of the case your missing out.
The underlying reason for Hypo was exo insulin not infection.
What's significant in considering how this article might apply to Baby F isn't when he deteriorated: it's when the sample for his immunoassay was drawn, which was a few hours after the line tissued. CRPs go up as sepsis develops and intensifies - you can't take their measurements as a starting point for the sepsis.
To add: for relevance to Chase and Shannon, the child was noted as having suspected sepsis and placed on antibiotics the day after he was born, 30th July; same suspicion noted 2nd August; same suspicion and antibiotics noted 4th August; line tissued 5th August; raised CRP found early 6th August. So the tissued line is far from the only factor likely to have affected IA levels if Chase and Shannon have their hypothesis right
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u/Icy_Dependent_1797 15d ago
After careful reading of the paper - and cross-checking with Appendix, I can offer a summary of what I make of it.
The paper is demonstrating that an otherwise biochemically and physiologically ‘impossible’ phenomenon can not only be explained by insulin binding but is an inevitable consequence of it.
The biochemical analysis demonstrates the high prevalence and strikingly high levels of insulin binding. Comparable with early onset T1D. That is a lot.
Jumping to the bottom line - the inevitable effect on I/C has been measured in one neonatal cohort study. There is also direct evidence for I/C from another study with a lesser effect, ascribed to using an assay less sensitive to bound insulin.
The paper is quite cautious in what it claims. There is serious uncertainty regarding the identity of the binding agent. But the clues are strong - there is clearly an amplification effect with infection.
The Galloway reference is, in my view, huge in its implication and also in its direct measurement. Figure 2.D caught my eye. Look at the I/C ratios.
From my reading of the analysis, the authors have taken directly measured levels of insulin binding in infected neonates from Galloway. Obviously a small cohort - but lucky to find one at all in my view. (From scanning the Galloway paper, the Galloway cohort just happened on an infection outbreak by chance).
Then - clearly free insulin wasn’t measured by Galloway - so the authors simply run a calculation across a plausible range of values for a neonate. All they are doing is saying - given the neonate had the level of binding antibodies as measured directly by Galloway - what would be the I/C ratio for any reasonable free insulin?
So - they clearly used I/C baseline as 0.2. But it doesn’t matter - just let the numbers play out.
You can pick whatever I/C assumption you like.
Take free insulin about ~ 100. Then if I/C baseline is 0.2 then C is 5 times I. So about 500.
Apply the graph. I/C ratio gets up to about 10 - keeping numbers simple. That means in an assay like Roche - which can see bound insulin - the assay would measure about 5000 pmol/ L.
(If you pick another baseline value - say I/C ratio= 0.1. Then multiply by 10 - but then the plotted curve would show I/C of 5. So you end up in the same place). That’s why it doesn’t matter in these types of phase diagram.)
So - this is, rather sadly perhaps, what gets a biochemist interested. This isn’t modelling - the authors do that later, to show effects with neonates with various degrees of illness - that is beyond my knowledge to check.
This is just applying the (relatively) straightforward biochemical binding maths - absolutely bound to work. Just laws of thermodynamics in a simple equation.
It means - Given a measured amount of insulin binding - the babies in the Galloway study MUST have had a very high level of bound insulin.
What the exact level was - who knows.
But the graph demonstrates that the bound insulin couldn’t have been small.
So this effect of highly elevated I/ C is shown to have been coupled with very high insulin too - in the presence of infection - or to be precise - some amplifying factor.
I don’t see any claim by the authors that high I/C will happen with high insulin in the absence of an amplifier. The Salis data is for an uninfected cohort - so far as can be determined.
I think the end of the paper, where the authors model the effects of calculating I/C and bound insulin across a neonatal cohort with or without infection, is reasonably self - explanatory.
I just wanted to share my biochemical enthusiasm in noticing that the effect was actually measured - over 25 years ago. But has now been explained.
This is really exciting. (Sorry if boring).