Table 1 is all over the place, which is my point.

Running t-tests on the continuous variables gives very highly significant differences for age, cholesterol, TAGs, HDL, LDL, HbA1c, a small significant difference for BMI, and nearly significant for creatine and insulin.

Take age, the very first item in the table, which is reported as p=0.26. But, it's actually p<0.0001!

Yes, I know some could probably usea non-parametric test, but 1) we only have summary data, 2) they don't say what they used for their p value calculations, 3) I get the same as them for two of the variables using t tests.

These extreme p values are not values you expect, at all, in a true randomised trial: either 1) it is not randomised/randomisation failed; 2) it was initially randomised and these massive differences are caused by them excluding n=3 from the placebo group and n=1 from the intervention group for this table, in which case the fact they've done their analysis in the PP analysis in such clearly different populations makes it not worth bothering with nad not randomised; 3) something more nefarious.

By my reckoning, the only p values for continuous variables that are correct in table 1 are HOMA2-IR and fasting glucose!

Given their first table reveals fatal mistakes in their basic statistics and randomisation process, I’m with ya ;)

I agree to some extent in practice, but recent changes to diagnostic criteria (ie Rome IV, and shortly Rome V) and a shift towards positive diagnosis are reducing the number of patients defined as having IBS.

There is certainly an overlap with conditions like SIBO, but as I said to another commenter, these are mechanisms within the DGBI umbrella given the need for visceral overreactivity to convey symptoms.

DGBI terminology came with Rome IV, which also saw the prevalence of IBS halve due to exclusion of edge cases. These issues you list are not outside of DGBI mechanisms - they are inside it.

It’s also a very high dose injected directly into the spine.

Good RCTs into this question could have been done years ago, but most are terrible. I’ll believe a substantial pain modulating effect when it’s proven (and if there is one, it will be easy to do so).

You’re right that “know” is being overworked in my initial statement. But, insofar as DGBI is a broad church of mechanisms from stimuli to neuronal function to the brain, and DGBI is the terminology employed by the body defining IBS, there really aren’t any major competing theories any more.

That’s not what this study is suggesting. And we already know IBS is a syndrome related to disordered gut-brain interaction - that’s why the umbrella terminology for these conditions is literally “disorders of gut-brain interaction”.

This study shines a closer light on the specific cellular mechanisms that might be involved in IBS discomfort/pain, which provides targets for specific therapies at the level of the gut - they are specifically looking at hyperactivation of enterochromaffin cells, which are neuronal cells embedded in the gut wall, that then signal to gut neurons and ultimately produce visceral pain reactions.

Edit: the serotonin signalling modulator they use as proof-of-principle in the model is alosetron, which is only used in very severe IBS in women (it was withdrawn from all other indications due to side effects). Trust me - they are not suggesting that everyone take systemic alosetron, they are not stupid:

>Data presented here also support a role for local, rather than systemic serotonergic signalling at the interface between EC cells and mucosal afferents, consistent with recent suggestions that primary afferent nerve fibres form synapse-like associations with enteroendocrine cells. Alosetron, which is approved for the treatment of severe IBS with diarrhoea in women39, has been proposed to exert its analgesic effect centrally or through inhibition of high-threshold nociceptors in the gut wall. We propose an alternative or further mechanism to account for this sex-biased analgesic effect, namely one involving modulation of serotonergic signalling at EC cell–mucosal afferent circuits.

Not an oncologist, but it’s far too early to tell. They saw no tumour responses, and the length of response in a couple of patients could easily just be chance.

They are now running a (single arm) trial in patients with platinum sensitive ovarian cancer in conjunction with platinum chemotherapy, so they clearly see this as an adjunctive therapy. But as said elsewhere, there is a huge way to go from phase 1 to rolling out an actually effective drug that provides net benefits to patients.

It’s a single arm phase 1 study with a handful of patients. It is fundamentally impossible (and irresponsible) to claim that this drug “extends… patients’ lives” on the basis of an uncontrolled study. There is a huge way to go before efficacy can be claimed.

This is a long way from any clinical use.

This is the paper: https://www.sciencedirect.com/science/article/pii/S0956566322010065?via%3Dihub

They aren't actually assessing how good it is as a tool to detect cancer, or monitor response to treatment: most of the paper is demonstrating that the device can analyse metabolic features of a lot of individual cells extracted from (mouse) blood at once - and it seems it can spot cancer cells that are in the blood. But being able to spot mouse cancer cells when you know there is a cancer is a long way from giving someone a blood test to spot cancer. This will be very dependent both on how sensitive and specific any test is, and how frequent the cancers are in the target populations (eg, is it the general population, or those with symptoms, or those at high-risk, or recurrent settings, etc).

Right, title is crap. The paper itself cites lots of earlier studies and papers reporting on the links.

No, it is not true. There are some elements of truth (eg, the patentability of vitamin C [and any unpatentable agent] does hinder research, but that ignores that a lot of research is done/funded by not-for-profit/governmental agencies around the world), but it is not a 'global conspiracy' to suppress a miracle cure.

I think it's also worth pointing out that enthusiasm in medicine in general is pretty tempered because with vitamin C we've been here quite a lot before. Eg, IV doses were heavily promoted by some fringe medics as being a revolutionary cure (ie, reducing death by nearly 90%) in sepsis ICU patients, but we now know that risk of death is at the very least not decreased and in is often increased by high doses. I think IV vitamin C is a difficult sell to funders, when there are many such potential avenues to investigate. There is some promising preclinical evidence, eg in this study, and it may be that there will be a new wave of trials as that evidence accumulates to convince people.

Aye, Figure 3 showing the efficacy vs remdesivir is basically all we need to see. Huge difference in observed 'potency', even accounting for their presentation of different units... and this against a drug that is far from optimal anyway.

>Because if you assumed that they were pure compounds 5 ug/mL is probably somewhere in the 10 uM range, which is not what you’d hope for from a potential hit.

Indeed. 99% of SciRep papers reporting successful anti-SARS-CoV-2 activity fall over completely at the first glance of the concentrations used/required.

Guarantee no one is gonna want to/be physiologically able to consume enough of these extracts to achieve 10 uM lung tissue concentrations, even if these extracts are absorbable, survive the GI tract, accumulate in lung, and don't kill or maim you first.

Indeed. It’s nice that they are frank about this in the discussion but worrying that they say nothing about it in the abstract. A good journal wouldn’t publish this without an explicit warning that it was a post hoc analysis. Violation of basic CONSORT guidelines.

This is a post hoc analysis. This was NOT a named secondary! It explicitly says so in the paper, and it’s why I explicitly said it was a post hoc analysis…

For your info, from the reg record:

>Primary outcome measure

>Neonatal whole body bone area, bone mineral content and bone mineral density assessed by dual energy x-ray absorptiometry (DXA) within 10 days of birth.

>Secondary outcome measures

>1. Neonatal and childhood anthropometry and body composition (weight, length and skinfold thickness measurements), assessed within 48 hours of birth

>2. Women's attitude to pregnancy vitamin D supplementation (qualitative study; assessed in main study only). Methodology and timepoints of assessment not yet defined as of 03/03/2008

>3. Childhood bone mass at 4 years

If you don’t have any hypothesis before you do the trial to statistically “test” (ie through null hypothesis testing) any significant result (regardless of multiple comparisons control) you generate is - by definition - hypothesis generating.

I don’t mean that the trial itself isn’t robust. It is. But the finding is not enough on its own to say “this is real”.

It’s significant (and the 95% CI indicates it is reasonably so), but this is a post hoc analysis of an outcome they already ‘knew’, in a trial that already had many outcomes, making this result at pretty high risk of being a chance false positive. This is probably why they don’t give actual p values - the findings are hypothesis generating, not confirmatory.

The major ‘caveat’ here is that this is a post hoc analysis of a trial that was done to look at something else, and you have to consider that this was one aspect of many they looked at - hence, they findings are only hypothesis generating, and require confirmation.

The actual study acknowledges they only have correlative data and then loses restraint and talks about causation in the Conclusions...:

>Members of genera Eubacterium coprostanoligenes, Lachnospiraceae NK4A136, Akkermansia, and Parabacteroides might be potential taxa for intervening in obesity regarding different degrees of obesity. Furthermore, three species, Prevotella copri, Bacteroides ovatus, and Dorea formicigenerans also showed great potential to regulate obesity. In contrast, a strategy that targets the inhibition of certain taxa in different obesity groups might be another way to improve obesity.

I think the take home here from the above trials is that the hype far, far exceeded the evidence - if microbiota modulation for obesity was a drug requiring substantial financial investment, it very likely would have been dropped in preclinical development, and it certainly would have been dropped after these negative pilot trials. By contrast, the effect of, say FMT on single and multiple recurring C diff is very obvious, and the RCT evidence for an effect in ulcerative colitis is at least mixed, if not somewhat promising.

The reason for the focus on obesity, rather than say obesity-associated CVD or CKD or NAFLD or extra-GI cancers, is that these conditions take years to develop, in a relatively small number of people - it should be much easier to see an effect on weight in people already obese, if one exists. There is some very limited evidence that microbiota intervention can improve glucose homeostasis in people, but I wouldn’t hang my hat on those studies.

There are number of trials looking at microbiota interventions and obesity, and none have found any effect.

It may be that we aren’t using the right protocol, but it really does seem that if there is an effect it is far smaller than previously believed.

Latest trial to my knowledge is this one: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2799634

Others include but are not limited to:

https://www.ncbi.nlm.nih.gov/pubmed/33346848

https://pubmed.ncbi.nlm.nih.gov/31301451/

https://pubmed.ncbi.nlm.nih.gov/32150549/

Even the animal models that launched this research are overhyped, finding marginal effects in tiny experiments often with improper controls or analysis approaches.

Agree.

No human interventional studies have found any effect of changing the microbiota on obesity, and there have been a good number, and any non randomised cross sectional studies are far too at risk of reverse causation and confounding to say anything on causation.

Rates of suicidal ideation were low and similar between all groups. The only events of “severe” suicidal ideation were balanced across groups and happened after day 1. Data is in table 3.