Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank, 2024, Huang et al

[Hutan]

The production of the model was using 7 disease populations.

That means that the model is definitely not useful for diagnosing ME/CFS then. All of those highly selected homogeneous disease populations exclude many people with diseases associated with dislipidemia; for example people who are obese. The ME/CFS group, as a heterogenous cohort, does not.

Of course, if you have data where the ME/CFS group is the only group allowed to have obese people, you will find that levels of blood lipids are different in the ME/CFS group compared to the healthy group.

From Table 2, the number of people on cholesterol-lowering medication in the ME/CFS group is 15.7%. This is probably normal for people of this age in the UK who are educated and concerned about health enough to be part of the UK Biobank. Indeed a study on the UK Biobank data reported statin use at baseline of 15.4% for its whole population.
(from Association of statin use with risk of depression and anxiety: A prospective large cohort study)

Here are the percentages of people on cholesterol-lowering medication in the other groups of people used as comparator groups in this paper (also Table 2)

Hypertension 9.7%
Depression 0.7%
Asthma 0.9%
IBS 0.5%
Hypothyroidism 0.6%
Migraine 1.7%
No health conditions (C2) 0.8%

Can you see how highly selected the comparison groups are? By not allowing people in the comparator groups to have any health condition other than the one the group is labelled with, you are badly skewing the comparison.

Taking hypothyroidism for example, there's a paper 'Prevalence of Hyperlipidaemia in Adult Patients with Hypothyroidism: A Systematic Review'. It lists out the findings of a whole lot of papers in hypothyroidism.
One study reports the prevalence of Hypercholesterolemia: 48.4%. Hypertriglyceridemia: 32.3%
Another notes that low HDL-C is present in 69.2% of people with hypothyroidism.
I haven't checked the details of those studies, but there is overwhelming evidence that people who are hypothyroid are very likely to have issues with weight control and issues with blood lipids. The selected people with Hypothyroidism control group are not at all normal for people with hypothyroidism.

It's like, I don't know.... having two jars of M&Ms filled straight from the packet, calling one ME/CFS and the other Hypothyroidism. And then deliberately taking nearly all of the yellow M&Ms out of the Hypothyroidism jar. And then saying that 'we can diagnose if a jar of M&Ms has ME/CFS with a reasonable degree of accuracy by looking at how many yellow M&Ms are in it'. This paper is like that.

 

[Jonathan Edwards]

Even 5 years ago our discussion with the business groups at the University had pointed to problems they'd had in translating AI tools to GPs, that largely fallen away on the past 5 years.

I am sceptical of that. GPs were taught to diagnose primary hyperparathyroidism by asking for a 'bone profile' of calcium, phosphate and alkaline phosphatase fifty years ago without most of them remembering why those tests were useful.

Maybe wariness of black box sets of results where none of the individual results is outside the normal range reflected a healthy caution about diagnostic tests that might not be all they seem and maybe everyone has got so obsessed with tech now they have lost that caution?

Comorbids we used were the common ones that mecfs patients in ukbiobank actually had and were enriched against a general population background in ME. We added hypertension to account for the lipid and lipoprotein elevations. Interesting thing is that mecfs group (25% hypertension) had lipoprotein profiles that were close to equal to a group where 100% of people had hypertension.

OK, so you are using comorbidity to mean something found in statistical association with ME/CFS that may confound attempts to identify markers that sort with ME/CFS because of ME/CFS biology specifically. If you can do that you have some biological explanatory data. That is what Beentjes and co hope they had done but I fear may have been due to confounders.

But I thin this is actually a different problem from discriminating diagnoses in an individual. It may translate to that if the biological link is robust but again, the question is whether anyone has ever found a set of markers with a pattern of values within normal ranges that is robust enough that does not make sense biologically. I don't know of a case. It seems you don't either, since none has been mentioned so far?

If we are allowing AI why not tell patients just to log on to Google and ask it 'Have I got ME/CFS?' No doubt Goggle has the sense to go through a history including questions that test for fitting CCC. You cannot do better than that if that is how ME/CFS is defined.

 

[Trish]

I am trying to catch up with this discussion. I have not been able to read the whole paper yet, but from what I have read, it seems to me that Hutan is raising relevant points. This post is a work in progress.

I have just reached this bit in the paper and it seems relevant

Addressing comorbidities within ME/CFS​

To thoroughly investigate the impact of comorbid conditions in ME/CFS requires stratifying the cohort into groups of isolated condition combinations, which can substantially reduce the sample size and the statistical power. For example, there were 211 ME/CFS individuals with a combination of depression and other comorbid conditions, and 24 individuals with depression only. We recognise that the other 265 comorbid conditions not analysed in this study may influence the biomarker associations. Therefore, we created another cohort with 354 ME/CFS individuals with or without hypertension, depression, asthma, IBS, hay fever, hypothyroidism, or migraine and performed association tests (Supplementary Fig. 7) and sensitivity analysis for this subset (Supplementary Data 9). Thirty-one of the initial 168 ME/CFS biomarker associations remained significant (P < 2.01×10−4). SFA% and omega-3 were the only significant associations that produced greater odds ratio in the subset than the full cohort. The lower odds ratios observed may be attributed to the reduced number of comorbid conditions reported by each individual, rather than the specific condition. The average number of comorbid conditions was 3.0 for the full cohort and 0.6 for the subset. This suggests that the burden of having several comorbid conditions might exacerbate ME/CFS symptoms (inclusive of symptoms from common comorbid conditions), reflecting a higher disease severity, leading to more pronounced biomarker signals in the full cohort.

If direct comparisons are to be made between pwME and healthy people, then the ME/CFS group needs to be a group with no co-morbidities. That is, the only diagnosed difference between the groups is ME/CFS. Once co-morbidities are added to the ME/CFS group, and not to the comparitor group, there is no way of knowing the source of the differences in tests.

The research attempts to get around this by comparing groups with single conditions against the healthy group. If each of these data sets is to be useful for identifying ME/CFS specific biomarkers, the direct comparison needs to be between people with, say, migraine alone, and people with ME/CFS and migraine and no other diagnosed condition.

Or for combined conditions, you need to compare, say, people with migraine and IBS and nothing else with people with ME/CFS, migraine and IBS and nothing else.

It seems from the section I quoted above, once you start subsetting like this, the significant markers fall away rapidly.

I'll try to read more of the paper later, and comment further. This is a work in progresss but I wanted to make my notes here now so I don't lose track.

 

[Jonathan Edwards]

Another thing that worries me is that there seems to be an assumption that there are definable number of 'diseases' each with a causal pathway that may include certain measurable chemical variables. I think it more likely that what we call diseases are the tips of icebergs with vast stretches of differences in variables invisible - sometimes separate, sometimes part of the same iceberg underneath with several bits visible. Maybe even some bergs being weighed down by other overlapping bergs. Which means that there is probably no clearly definable causal pathway for most of the variables that show differences to fit in to.

The assumption of a causal path is what worried me about the Beentjes study. It didn't look realistic to me.

There seem to be too many unknown unknowns here.

 

[Utsikt]

The problems were around AI tools could pick up patterns without explaining the why. Clinicians were weary of using tools if they themselves didn't understand the biological. I believe clinicians are taught this way. But as trust in AI is growing there is beginning to be trust in not needing to know the why, just that AI analysis has seen a complex pattern and the outcome is a result that is beneficial.

The trust in AI is declining, even thought the use of it is increasing. The distrust is usually higher among the people with the most knowledge and expertise.

The explainability issue isn’t just a case of trust either - it’s about ethics and law as well. If we can’t know why a decision was made, we can’t ensure it’s medically sound. That makes it impossible to ensure the patient’s rights are met. We need transparency and traceability.

 

[Hutan]

I am trying to catch up with this discussion. I have not been able to read the whole paper yet, but from what I have read, it seems to me that Hutan is raising relevant points.

Oh, thank goodness, Trish. It's been feeling a bit as if I have been hitting my head against a brick wall, coming across as someone being difficult for the supposed fun of it. I started raising this point in relation to another paper by this group in December. I've tried several ways of expressing it, privately, and in the threads and still didn't seem to be getting any cut through. The problem seems immediately obvious to me. I think it completely invalidates the reported findings. I can't see any finding in this paper that we can rely on.

I really want to know if people with ME/CFS do have issues with blood lipids. But, unfortunately this study does not answer that question. I think it is misleading and likely to negatively affect future research. I don't say that lightly, I really want this team to succeed. It's possible that I've misunderstood something, but I've asked questions and looked again and, from the answers and the looking, I don't think I am.

We should not take these findings as telling us anything about ME/CFS when deciding what further research is needed or hypothesising about underlying pathology. It would be great if @MelbME and his team could acknowledge the problem. I want this team to understand, because we are relying on them to do useful research, and, at the moment, this paper will be leading them and others to conclusions that aren't well supported by evidence. It would be a sign of the team's integrity and desire to solve the question of ME/CFS, rather than just win funding and produce papers, if they would acknowledge the problem.

The example of the recent Shan group paper is one to emulate. They made an error, one that could have resulted in future research and hypothesising heading in the wrong direction. They recognised the problem and intend to fix it. That won our respect. I deeply appreciate @MelbME's presence on the forum; I hope he can be open to the arguments made here.

From the paragraph that Trish quoted, it appears that the authors, or some of them, did begin to appreciate the problems with their study concept. From @MelbME 's replies, I think he genuinely doesn't see the problem in the study's concept yet.

To reiterate:

There is an enormous degree of selectivity in the creation of the disease subsets. Something of the order of one in ten of the people in the Biobank who have the relevant diagnosis label have made it into each subset. They are the people with no comorbidities, including the comorbidity of obesity. There is similar selectivity in the creation of the C2 group, consisting of people with no disease diagnoses at all. The act of selecting makes these 'homogeneous' groups far less likely to have blood lipid issues than the entire population in the Biobank with the disease diagnosis. I've presented evidence that shows that the people in the comparator groups are indeed unusual with respect to the incidence of blood lipid issues.

Then these subsets are compared with the people who have an ME/CFS label, a group that is acknowledged as being heterogeneous. The ME/CFS group includes people who are obese, and have other conditions known to have high levels of dyslipidemia. The result is that the comparison tells us nothing certain about blood lipids in people with ME/CFS. The findings could be wholly attributable to differences between the groups in the prevalence of comorbidities, including obesity.

 

[Trish]

I'll try to work on it today.

 

[bobbler]

Even 5 years ago our discussion with the business groups at the University had pointed to problems they'd had in translating AI tools to GPs, that largely fallen away on the past 5 years.

Application of the tech is still new, the potential was recognized decades ago. Sure.

Comorbids we used were the common ones that mecfs patients in ukbiobank actually had and were enriched against a general population background in ME. We added hypertension to account for the lipid and lipoprotein elevations. Interesting thing is that mecfs group (25% hypertension) had lipoprotein profiles that were close to equal to a group where 100% of people had hypertension.

Re that last line, that’s interesting. Do you know whether data was collected to confirm whether those with hypertension had been put on meds eg statins in large %?

It seems a silly q as the point is that would reduce hypertension if that’s defined by taking a pressure reading vs a questionnaire answer where I don’t know if someone who is on statins because it controls their hypertension successfully would answer to such a questionnaire question. Im just trying to picture in my mind what that pot includes characteristic wise that lipoprotein is the same as to see what it might tell us.

I also don’t know how/ reason why statins would relate to lipoprotein but there’s so much upstream downstream possibility I’m just being curious for the sake of it in what to rule out. Doing the logic puzzles in my head given how many in uk over a certain age got bunged on statins. And whether statins are given for cholesterol or blood pressure etc. but the action isn’t ‘direct’

 

[bobbler]

Oh, thank goodness, Trish. It's been feeling a bit as if I have been hitting my head against a brick wall, coming across as someone being difficult for the supposed fun of it. I started raising this point in relation to another paper by this group in December. I've tried several ways of expressing it, privately, and in the threads and still didn't seem to be getting any cut through. The problem seems immediately obvious to me. I think it completely invalidates the reported findings. I can't see any finding in this paper that we can rely on.

I really want to know if people with ME/CFS do have issues with blood lipids. But, unfortunately this study does not answer that question. I think it is so misleading and so likely to affect future research that I honestly think this paper should be retracted. I don't say that lightly, I really want this team to succeed. It's possible that I've misunderstood something, but I've asked questions and looked again and, from the answers and the looking, I don't think I am.

Retracting a paper is difficult, but, at the very least, we should not take these findings as telling us anything about ME/CFS when deciding what further research is needed or hypothesising about underlying pathology. It would be great if @MelbME and his team could acknowledge the problem. I want this team to understand, because we are relying on them to do useful research, and, at the moment, this paper will be leading them and others to conclusions that aren't well supported by evidence. It would be a sign of the team's integrity and desire to solve the question of ME/CFS, rather than just win funding and produce papers, if they would acknowledge the problem and retract (or redo) the paper.

The example of the recent Shan group paper is one to emulate. They made an error, one that could have resulted in future research and hypothesising heading in the wrong direction. They recognised the problem and intend to fix it. That won our respect. I deeply appreciate @MelbME's presence on the forum; I hope he can be open to the arguments made here.

From the paragraph that Trish quoted, it appears that the authors, or some of them, did begin to appreciate the problems with their study concept. From @MelbME 's replies, I think he genuinely doesn't see the problem in the study's concept yet.

To reiterate:

There is an enormous degree of selectivity in the creation of the disease subsets. Something of the order of one in ten of the people in the Biobank who have the relevant diagnosis label have made it into each subset. They are the people with no comorbidities, including the comorbidity of obesity. There is similar selectivity in the creation of the C2 group, consisting of people with no disease diagnoses at all. The act of selecting makes these 'homogeneous' groups far less likely to have blood lipid issues than the entire population in the Biobank with the disease diagnosis. I've presented evidence that shows that the people in the comparator groups are indeed unusual with respect to the incidence of blood lipid issues.

Then these subsets are compared with the people who have an ME/CFS label, a group that is acknowledged as being heterogeneous. The ME/CFS group includes people who are obese, and have other conditions known to have high levels of dyslipidemia. The result is that the comparison tells us nothing certain about blood lipids in people with ME/CFS. The findings could be wholly attributable to differences between the groups in the prevalence of comorbidities, including obesity.

Is it an ‘averaged thing’ across the heterogeneous group being compared to that of homogeneous group

Or is it the average of the homogeneous group being used to see / vs the distribution for the heterogeneous group (and saying how many matched that) ? I’m not on top form and getting thrown by the %s of group vs numbers as it goes down the chain of what’s compared to what and stated as the finding

 

[MelbME]

I am sceptical of that. GPs were taught to diagnose primary hyperparathyroidism by asking for a 'bone profile' of calcium, phosphate and alkaline phosphatase fifty years ago without most of them remembering why those tests were useful.

Maybe wariness of black box sets of results where none of the individual results is outside the normal range reflected a healthy caution about diagnostic tests that might not be all they seem and maybe everyone has got so obsessed with tech now they have lost that caution?


OK, so you are using comorbidity to mean something found in statistical association with ME/CFS that may confound attempts to identify markers that sort with ME/CFS because of ME/CFS biology specifically. If you can do that you have some biological explanatory data. That is what Beentjes and co hope they had done but I fear may have been due to confounders.

But I thin this is actually a different problem from discriminating diagnoses in an individual. It may translate to that if the biological link is robust but again, the question is whether anyone has ever found a set of markers with a pattern of values within normal ranges that is robust enough that does not make sense biologically. I don't know of a case. It seems you don't either, since none has been mentioned so far?

If we are allowing AI why not tell patients just to log on to Google and ask it 'Have I got ME/CFS?' No doubt Goggle has the sense to go through a history including questions that test for fitting CCC. You cannot do better than that if that is how ME/CFS is defined.

This is something we are assessing now. Some clinical AI products can link patient data to CCC and IOM. None are currently moving through an exclusion diagnosis yet.

 

[MelbME]

I am trying to catch up with this discussion. I have not been able to read the whole paper yet, but from what I have read, it seems to me that Hutan is raising relevant points. This post is a work in progress.

I have just reached this bit in the paper and it seems relevant

If direct comparisons are to be made between pwME and healthy people, then the ME/CFS group needs to be a group with no co-morbidities. That is, the only diagnosed difference between the groups is ME/CFS. Once co-morbidities are added to the ME/CFS group, and not to the comparitor group, there is no way of knowing the source of the differences in tests.

The research attempts to get around this by comparing groups with single conditions against the healthy group. If each of these data sets is to be useful for identifying ME/CFS specific biomarkers, the direct comparison needs to be between people with, say, migraine alone, and people with ME/CFS and migraine and no other diagnosed condition.

Or for combined conditions, you need to compare, say, people with migraine and IBS and nothing else with people with ME/CFS, migraine and IBS and nothing else.

It seems from the section I quoted above, once you start subsetting like this, the significant markers fall away rapidly.

I'll try to read more of the paper later, and comment further. This is a work in progresss but I wanted to make my notes here now so I don't lose track.

So like the NIH study?
The issue with that is that comorbidity seem to be part of the illness. In fact number of comorbidities was a useful marker of me/cfs against other diseases. We didn't include it in our scores though.

The point of this study was to see if we could take the most common comorbidities in the ME/CFS cohort and still find a marker signature that could separate ME/CFS from those comorbidities.

 

[MelbME]

Another thing that worries me is that there seems to be an assumption that there are definable number of 'diseases' each with a causal pathway that may include certain measurable chemical variables. I think it more likely that what we call diseases are the tips of icebergs with vast stretches of differences in variables invisible - sometimes separate, sometimes part of the same iceberg underneath with several bits visible. Maybe even some bergs being weighed down by other overlapping bergs. Which means that there is probably no clearly definable causal pathway for most of the variables that show differences to fit in to.

The assumption of a causal path is what worried me about the Beentjes study. It didn't look realistic to me.

There seem to be too many unknown unknowns here.

Yeah we aren't assuming that. These diseases are messy. The outcome couldn't be a definitive all in one test based on the data in UK Biobank. We still wanted to show a proof of concept because even with this limited data we got to 83%. I think like 70% of mecfs patients in our cohort had at least one of the 6 comorbidities we were trying to separate them from.

 

[MelbME]

The trust in AI is declining, even thought the use of it is increasing. The distrust is usually higher among the people with the most knowledge and expertise.

The explainability issue isn’t just a case of trust either - it’s about ethics and law as well. If we can’t know why a decision was made, we can’t ensure it’s medically sound. That makes it impossible to ensure the patient’s rights are met. We need transparency and traceability.

I would say that many people (not all) often don't know why a decision is made but still trust it is medically sound if there is at least one human somewhere that understands it. In this case, instead of it being that one human that understands it, it's AI.

I doubt there are many other any people that understand all the technology they rely upon for their safety. They trust that someone somewhere understands it even if they never heard of them.

 

[Trish]

In this case, instead of it being that one human that understands it, it's AI.

I have no idea what this means. If you are talking about LLMs, they don't 'understand' anything. And they often make stuff up.

 

[MelbME]

Re that last line, that’s interesting. Do you know whether data was collected to confirm whether those with hypertension had been put on meds eg statins in large %?

It seems a silly q as the point is that would reduce hypertension if that’s defined by taking a pressure reading vs a questionnaire answer where I don’t know if someone who is on statins because it controls their hypertension successfully would answer to such a questionnaire question. Im just trying to picture in my mind what that pot includes characteristic wise that lipoprotein is the same as to see what it might tell us.

I also don’t know how/ reason why statins would relate to lipoprotein but there’s so much upstream downstream possibility I’m just being curious for the sake of it in what to rule out. Doing the logic puzzles in my head given how many in uk over a certain age got bunged on statins. And whether statins are given for cholesterol or blood pressure etc. but the action isn’t ‘direct’

Not sure but could look into it.

 

[forestglip]

The point of this study was to see if we could take the most common comorbidities in the ME/CFS cohort and still find a marker signature that could separate ME/CFS from those comorbidities.

I'm wondering about the specific points Hutan raised that don't seem to have been addressed yet. Is this study concluding that lipids are a problem in ME/CFS based on comparing an ME/CFS group that could potentially include individuals with multiple comorbidities to groups where individuals either had no conditions at all or at most one? If so, couldn't one of the other conditions found in the ME/CFS group be responsible for the differences, without it having anything to do with ME/CFS?

It seems to me that if this is the case, then if the study were to be redone with the same methodology, but switching out ME/CFS for virtually any other condition, it would tend to show similar results. If the study were redone with hypertension as the main heterogenous group of interest, and ME/CFS as a homogenous group where individuals could only have ME/CFS, would we not see high lipids in the hypertension group compared to the rest of the groups?

 

[Utsikt]

I would say that many people (not all) often don't know why a decision is made but still trust it is medically sound if there is at least one human somewhere that understands it. In this case, instead of it being that one human that understands it, it's AI.

But the AI doesn’t «understand» anything. You’re really not addressing the issues raised with regards to XAI (explainable AI), you just keep saying you don’t think they are important.

I doubt there are many other any people that understand all the technology they rely upon for their safety. They trust that someone somewhere understands it even if they never heard of them.

Which wouldn’t be the case for AI because as AI doesn’t «understand» anything. I feel like we’re going in circles..

 

[MelbME]

I'm wondering about the specific points Hutan raised that don't seem to have been addressed yet. Is this study concluding that lipids are a problem in ME/CFS based on comparing an ME/CFS group that could potentially include individuals with multiple comorbidities to groups where individuals either had no conditions at all or at most one? If so, couldn't one of the other conditions found in the ME/CFS group be responsible for the differences, without it having anything to do with ME/CFS?

It seems to me that if this is the case, then if the study were to be redone with the same methodology, but switching out ME/CFS for virtually any other condition, it would tend to show similar results. If the study were redone with hypertension as the main heterogenous group of interest, and ME/CFS as a homogenous group where individuals could only have ME/CFS, would we not see high lipids in the hypertension group compared to the rest of the groups?

Yes it could be explained by comorbidities.

That is the strength of our paper and why it is well regarded. I'd say less than 5 papers in all of ME/CFS research have actually tried to tackle the comorbidity you talk of. Probably this paper and a couple of Pontings papers and I guess the NIH paper. I think for this reason you would find all of these papers in a top 10 ranking of publications in ME/CFS if you asked your favourite AI.

It's a complex issue in the field because comorbidity is part of mecfs but the accumulating comorbidities have their own impact on biology. No study ever produces a control group that can counter that properly. Probably NIH got the closest and that got a ton of backlash because people rightly highlight that some of that comorbidity may actually be the disease. I've heard some argue that no comorbidities likely means they weren't ME.



In our paper we did a sensitivity analysis to rule out the impact. We took the low-comorbidity mecfs group and it reduced signals but many of the lipid signals remained significant. This is all in the paper that Hutan has read through yet they keep attacking our work. I've discuss in PMs as well. It doesn't seem like Hutan is actually listening to what I'm saying but I wish they would. The things they are trying to attack this paper about are actually the strengths it has against just about every other paper in the field. I'm only not replying because every time I try explain they come back more aggressively, now calling for retractions. This has been going on for awhile and not specific to this paper.

Anyway, if you went for a general population control then you be far less controlling of comorbidities than we did in our sensitivity analysis. We've done better than their suggestions, been more conservative. ME/CFS accumulate comorbidities unlike the general population, average was 3 comorbidities in mecfs cohort and this is a relatively healthy cohort.

This paper has been published a while now, it would have been nice to go through this discussion when it was fresher. Here are some quotes from the paper:

“We recognise that the other 265 comorbid conditions not analysed in this study may influence the biomarker associations.”

“The lower odds ratios observed may be attributed to the reduced number of comorbid conditions reported by each individual, rather than the specific condition. … This suggests that the burden of having several comorbid conditions might exacerbate ME/CFS symptoms … leading to more pronounced biomarker signals in the full cohort.”

 

[Felis Catus]

I would say that many people (not all) often don't know why a decision is made but still trust it is medically sound if there is at least one human somewhere that understands it. In this case, instead of it being that one human that understands it, it's AI.

I doubt there are many other any people that understand all the technology they rely upon for their safety. They trust that someone somewhere understands it even if they never heard of them.

Many people don't have a job which involves having a licence and legal responsibility for decisions that affect other people's health, disability and death.

Those who do probably wouldn't get away with "AI said so" in court.

 

[MelbME]

But the AI doesn’t «understand» anything. You’re really not addressing the issues raised with regards to XAI (explainable AI), you just keep saying you don’t think they are important.

Which wouldn’t be the case for AI because as AI doesn’t «understand» anything. I feel like we’re going in circles..

Many people don't have a job which involves having a licence and legal responsibility for decisions that affect other people's health, disability and death.

Those who do probably wouldn't get away with "AI said so" in court.

I think very few clinicians actually understand the biology of all that they recommend. It's more a case of, in my experience giving this treatment helps people. There's plenty of things that just work without us knowing all the details.