I'm going to try and just give you an idea of the story behind this, because it's been a long running story,
certainly sort of in epidemiology, whether actually moderate alcohol consumption is actually beneficial.
Okay. So this started off with something known as the kind of French paradox.
So this is kind of information from an ecological study.
And they were looking at the association between heart disease, coronary heart disease and cholesterol, and they noticed that, you know,
for most countries, you know, there was a kind of linear relationship Finland have had sort of at that time when this was done.
So this was done in about sort of 1980 or something like that.
Finland had a really sort of bad sort of cardiovascular disease sort of profile.
And they've they've since sort of modified that.
But essentially, France, even though they had a high cholesterol intake, they actually had lower levels of coronary heart disease.
And then a kind of follow up to this was then. So people are saying, well, what what was the explanation for this?
And they did a plot of the per capita alcohol consumption against sort of death rates.
And you could see that Finland were sort of down at the other end.
And here he was, France, with low death rates, but still, you know, higher levels of alcohol consumption.
So this could got people thinking, well, what what are the reasons behind this?
Because it's kind of ecological data. So this kind of spawned lots of sort of hypotheses.
And so, you know, basically based on looking at this data saying, well,
this positive relationship with saturated fat situation in France is paradoxical simply because, you know,
they had this kind of low mortality rate and they thought it might be due to high wine consumption because they had some,
you know, various sort of things in wine that people believe are kind of protective.
And I'll say more about that a bit bit later. But also there are other things, you know, in terms of hypotheses.
Some people thought that it might be a time lag because they actually had to look at this.
So that information and they were thinking that actually in terms of cholesterol levels,
maybe it was taking time to kick in before you started seeing the benefits.
So people started actually having saturated fat in France later than some other countries.
And you see a similar thing with smoking.
You don't see the death rate until to the 20 years later because people have to be smoking for a while before you start seeing the harm that it does.
And then they thought, could it be diet as well?
So the differences in diet in terms of sort of things like folate.
Yeah, nuts and then sort of things like polyunsaturated fats as well.
And then they also thought, well, the French, you know, they drink with males.
So it's the patterns of drinking.
Is that the kind of thing that might be going on as opposed to the Brits who go to the bingeing at weekends or something on an empty stomach?
So it's that kind of thing. So that was one of the things that they thought might be of interest.
So this led to several sort of cohort studies being done.
And one of the things that they found, this is the data from sort of five cohort studies.
They were the largest done at that time.
And actually, you can see this kind of j-shaped association in all of them where actually the lower levels of alcohol consumption,
there seemed to be some benefit. And then that starts going the way of people to the drink more and more.
And these it is mostly done in sort of male cohorts.
But there's a similar it's the same sort of thing with women.
So essentially this kind of j-shaped association was always sort of related to sort of,
you know, at the lower levels of alcohol consumption, you saw some kind of benefit.
And then as people sort of think more and more, then you started seeing harms.
So that was kind of, you know, evidence and kind of the analytical epidemiologic.
But there's quite a few sort of biases in there. I'm not expecting you to read all of this, essentially.
The kind of bias is things like confounding by the type of drink or the pattern of drinking that we mentioned,
but things like socioeconomic status and lifestyle, those kind of things can sort of create issues.
But also the choice of reference group makes a difference as well.
So if you choose non-drinkers, you might see some sort of differences.
Also things like reverse causality in terms of once people get they'll then actually they change their behaviours,
they stop drinking or smoking, those kind of things.
So those are the kind of things that could be going on. Some of the studies with case control.
So there could be sort of recall or misclassification.
You ask anyone to, you know, what they drink or what they eat.
And just by sort of recalling, they tend to underestimate or under-report.
There's also within person variation. So over time people will change.
As you get older, you start sort of drinking patterns change.
You know, maybe when you're younger, you drink more.
And then, of course, the study design, also the publication bias that could maybe affect what's going on.
So this was sort of a review paper that a colleague and I did several years ago now.
But this is just to give you an idea of what the impact of the choice of the reference category is.
So essentially this dark line is choosing individuals that are non-drinkers.
So you see a kind of more accentuated sort of association.
And then this dotted line is actually when you change the reference category.
So what we did in this paper, we got hold of the individual data.
We had a look at what they had originally when they used a reference category of non drinker and then we
changed it to sort of like drinking and see what you would get if you sort of plotted that out again.
So the difference that you see here is actually on how beneficial you think it might be for moderate drinking because,
you know, you've got an idea that's in a different level for, you know, once you use a different reference category.
And then here is showing, well, where does it become harmful?
Well, it's actually at a different level.
If you use one reference category as opposed to another.
So trying to change is your kind of overall interpretation, you see.
I mean, so this led to sort of people thinking, well, we need to have some kind of comprehensive meta analysis,
trying to have a look at what's been done and try and really understand what's happening.
So this paper was published in The Lancet,
and what they did was they got 83 large perspective studies and actually got them all together to the
information and then tried to see if they could come up with a clearer picture of what was going on.
So the methodology of that and because this is a kind of medical sort of stats, cause I thought I'd sort of mention give you a bit of detail on this.
So they focussed on current drinkers that three main reasons what they wanted to do is think about alcohol guidelines.
So what can you actually say for people who are current drinkers based on that information,
but also to try and limit some of those biases that I sort of mentioned on the previous slide.
So things like reverse causality, residual confounding and so on.
And also there seems to be some evidence that never drinkers kind of systematically differ from drinkers somehow.
So that's why it's not usually a good sort of reference category to use.
And then they tried to harmonise all the alcohol consumption across the contributing
studies using a conversion rate of one unit being about eight grams of pure alcohol.
And then they converted that to a kind of standard scale of grams per week.
Then they tried to make sure they looked at sort of various confounders.
So, you know, things like smoking,
history of diabetes and obviously age and they all they wanted to correct for measurement error and within person variation,
which I sort of alluded to. So within these kind of cohorts, they had repeat measures on, you know, subset of individuals.
He showed how peoples of alcohol behaviour or drinking habits had changed over time.
So they tried to sort of account for that change over time by using the information on these kind of cereal assessments.
And they used a kind of method known as regression calibration.
For those of you that are not familiar, this is kind of like a standard way of actually adjusting for measurement error,
assuming that it's sort of a sort of random regression.
Calibration is a kind of standard approach for dealing with that.
And then they also looked at patterns of drinking as well.
So they had to wine, beer and spirits, you know, frequency consumption and then episodic heavy drinking.
So here on the left is a first.
So this is looking at the association between all cause mortality from all the studies.
So the usual alcohol consumption means that it's been corrected for within person variation rather than to the baseline alcohol.
And you can see that for all cause mortality.
I mean, it's sort of fairly flat. So the low levels and then starts picking up afterwards after about 150 or something.
Whereas for cardiovascular disease, you still got that at the lower levels.
You've got that inverse association that then sort of goes up afterwards.
So in that kind of shape that they try to look at different subtypes.
So they looked at sort of stroke and my coronary disease, excluding my heart failure and deaths and other types of sort of cardiovascular disease.
So you can see for stroke, it looks like there is a kind of strong positive association.
So, you know, drinking more seems to be harmful for stroke.
Whereas for me, well, it looks like it's an inverse association there, whereas for coronary disease excluding them, I sort of fairly unclear.
Heart failure looks like a kind of positive trend.
And death to the cardiovascular disease is similar and they summarise the results here.
So for all stroke, they told us about a 14% and they split into nonfatal and fatal and haemorrhagic and ischaemic.
And sort of with that sort of subtypes. You can see those are the ones that are all sort of positively associated with alcohol consumption.
Whereas for me, it looks like there's an inverse association and then sort of mixed results for some of these other things down there.
And generally not too much heterogeneity, probably a little bit high for this one, but generally,
you know, reasonably consistent results between the studies from what they reported.
They also tried to sort of get a good public health message across that it's time to have a look at the estimated years of life lost.
You know, if you a regular sort of low alcohol drinker, 100 to 200 grams, you know,
sort of moderate and then sort of high amount to try and have a look at what we could do.
And you could actually they plotted this of the years of life lost males and females and, you know,
seeing very similar sorts of shapes for both of those two and, you know, similar amounts of years lost for drinking.
Yeah, moderately or heavily.
And they did try and actually sort of do a trial.
So there was there was there was a trial that was going to be run in the US.
At one stage they tried to get 7800 participants.
They were going to randomise them to one drink a day to none for six years.
And the primary outcome was going to be as those who got it, cardiovascular disease and secondary was going to be kind of vascular death and diabetes.
But you won't be surprised to know that actually they thought, well, that's not going to work.
And they pulled the plug on that. At the time when we were looking at, we thought, well, this is going to be sort of quite ambitious.
And yeah, so they ended up not doing that because of, you know, the flaws in observational evidence.
You know, there's not really much chance of getting a trial. Could genetics give us a kind of clue on what to do?
There are a couple of genetic variants that actually sort of effect alcohol metabolism.
So they sort of got very similar name. So it can be quite confusing.
So 88 is alcohol, dehydrogenase and what that does, it converts to as to how old I am actually, you know, it kind of increases that.
So basically when you feel hung over and horrible and nauseous, that that's the reason.
And then the other one, a LDH actually sort of tunes asan aldehyde to acetate, but it slows down that process.
It doesn't quite have a stronger effect on you in terms of how you feel,
but essentially both of them lead to sort of can to sort of nausea and various sort of things
and headaches and all those kind of things that you feel when you're kind of hung over.
Essentially, the general feeling is you can use these to conduct what's known as the Mendelian randomisation study and
essentially a study where if you think of using a genetic variant that you kind of assigned to genotype at birth,
that can give you sort of an idea if you've got a particular genotype, whether you are tolerant for alcohol or not.
And you could do a similar study, which is essentially what they were trying to do, randomise it to a drink or not.
And that that would be probably the the best way of trying to sort of do that.
So I've just put the assumptions here in one sort of figure,
but essentially the assumption is all the genetic variant that you choose must be related to that kind of exposure that you're interested in.
So it must be related to alcohol in this case.
So that's known as the relevance assumption, the relationship between alcohol and the outcome of heart disease.
In this case, it must work through.
That pathway not to any of that pathway.
And then genetic variant must not be associated with any sort of confounders.
So, you know, you can kind of check some of these assumptions, but some of them are not.
It can be difficult unless you know a lot about the kind of biology of of the variants.
In this case, there's a lot known about these this particular snake.
It's it's quite a good candidate to do this kind of analysis.
This was a Mendelian randomisation study that looked at sort of individual participant data,
tried to sort of get the information together and see what they could do.
So what they did was they chose this particular snip.
It is non synonymous means it actually effects that kind of protein directly.
So basically, you know, that particular snake controlled what what's going on in this particular gene, the 88 one zombie.
And I showed you that on the previous slide and that one of the genes that
controls alcohol metabolism and they got an international collaboration together.
If you only had measured this and sort of cardiovascular biomarkers and various sort of events in order to try and understand fully what was going on,
they restricted individuals to those of European descent with data on this particular slip and,
you know, age and sex and any of the outcomes of interest and what they did.
I don't know how familiar you are with genetics, but this particular snap ad H-1B has to illegals.
You know, it's the one that is the bad one and the G is the good one.
And you can actually have three gene, two types, a G essentially.
And what they did is they pulled the result.
So if you had any bad illegal which is a.
They combined individuals with the aye aye and the AG and compared them with the drug.
And they quantified the effects of that a little on different alcohol traits and lifestyle things.
And they also had a look to see how related it was to kind of cardiovascular biomarkers.
So trying to sort of put together a picture of how it might sort of work.
And then they evaluated the strokes on I'm sorry, the analysis on coronary heart disease, stroke, and they looked at type two diabetes as well.
They did some long transformations for the continuous variables.
They looked at the kind of mean difference and worked out the kind of percentage difference.
And they looked at the shape of the association for the kind of biomarkers and confounders.
And then they also compared the genetic results with some observational results as well, just to try and see how different they were, essentially.
So here's what they found. That overall, they found that sort of marginal sort of benefit, about 10% for coronary heart disease.
And then they had a look in drinkers and non-drinkers.
You can see it was stronger in drinkers.
And then they had a look on whether it was like moderate or heavy as well.
And that didn't seem to be anything going on there in terms of types of different types of alcohol consumption there.
They also looked at sort of CVD biomarkers, blood pressure, you know, anthropometric indices, markers of inflammation.
So there's quite a lot in the literature about sort of alcohol being, you know, one of the ways that it might work is raising HDL,
good cholesterol, basically, and also with that kind of markers that might be sort of important.
And so that's what they looked at. So they looked at things like sort of interleukin six and C-reactive protein,
which are markers of inflammation and things seem to be going roughly in the right direction for those.
So, you know, lowering blood pressure, I mean, small amounts.
But, you know, essentially things seem to be going in the, you know, expected direction, but were consistent with the results that they were missing.
But there are limitations to this work. I mean, essentially that the snake that I showed you, it's actually what's known as mono morphic,
which means that nobody with the AA in Europeans that saw that walk,
that's why they had to use this that snap, because that is sort of prevalent in Europeans.
But also they lacked power to identify things with markers of coagulation.
So the type two diabetes and some of the kind of combined subtypes of stroke.
And then there also was evidence of Pleiotropic.
So one that I and my previous Mendelian randomisation slide, I said the pathway that works to must be my alcohol, not to match the pathways.
And the fact is there was some association with of this particular genetic variant
with smokers education and a few other things that you wouldn't want to see.
You know, that sort of raises some sort of doubts about this.
Can you do something in another population?
I mean, one of the problems with a lot of research that's going on at the moment, it's kind of very sort of Eurocentric.
And actually, can you use a different population? So these two genes are actually present in East Asians.
This particular variant, the Iris 671, is actually common in the East Asian population and it really does slow down that acetaldehyde breakdown.
So it's it's often known as the kind of Chinese flushing gene or the Asian flushing gene because
actually people who have these two A-levels feel absolutely dreadful after just a sip of alcohol.
So these people tend not.
So it really does reduce alcohol intake because you feel so bad that you don't drink at all due to the variant slightly less important in East Asians,
but it's still more prevalent in East Asians than it is in Europeans.
And you know, it reduces alcohol intake and both of these variants are more prevalent in East Asians and other populations, not just Europeans.
So I'm just going to introduce a study called the China Category Biobank, the eyes that work on,
because that gives us some context to the next bit that we're going to be doing.
This study is sort of coordinated by our department in collaboration with a group in China at Peking University,
and we collaborate with the Chinese CDC to actually collect data.
You know, it's a long running study. And essentially, you know, it's you know,
it's going to go on indefinitely in terms of as long as we can sort of get money to keep the fieldwork going,
but have collected lots of information, baseline survey and so on.
They do regular surveys where we actually repeat usually what we've done at baseline,
but actually add some further enhancements to kind of get new information.
And you know, we've got sort of blood stored on sort of various sort of things,
sort of metabolomics, proteomics and genetics and then some whole genome.
So the sequencing data as well, because we've been able to link this to the kind of national health records in China,
we've been able to sort of follow up people passively and actually get really good sort of complete information.
So there's something like a 98% coverage for the health insurance within China,
and you can actually then follow up and get lots of information on the various diseases and so on.
But in China, men drink and women don't,
or very rarely the meeting alcohol intake in the ten regions here you can see for the main it's sort of moderate sort of in Gansu,
quite high in Sichuan, which is also known for kind of spicy food there.
Whereas, you know, in the women, I mean in Sichuan it's quite high compared to others.
But yeah, it's pretty low, you know, basically similar sort of thing.
Just to remind you, essentially,
these two snips are prevalent in East Asians and essentially the alcohol intake is reduced
substantially with this particular marker and is reduced to a less moderate amount,
but still reduced if you've got this particular variant.
And just to check how sort of good it is inside data, we actually looked at our alcohol data and did a DUI.
So this is known as a manhattan plot. It's posted sort of all the peaks supposed to look like a sort of Manhattan.
But this is long tailed p values on on the y axis.
This is a p value. So ten to the -40 or something.
And this is almost sort of ten to the -200.
So basically, basically, they're definitely they're in the population. They're very strong in in this in our data.
What we did was to try and sort of work out the different combinations of the two snakes.
Rather confusingly, they both got the same sort of alcohol.
So if you if you think of it's always six, seven one, it's got those three genotypes and the other also has the same three genotypes.
You've got these kind of nine different combinations. So what we did was for these was to try and put together the nine different combinations,
but also taking into account the kind of regional differences.
So you saw that we had massive regional differences.
On slide couple of slides back, we took into account the kind of genotype that was, you know, combinations of these two and within the region.
And then what we did was we use that to categorise people's alcohol intake.
So we actually we're able to sort of work out what their predicted alcohol would be for these
particular combinations due to sort of small numbers rather than having ten categories,
we ended up with sort of six that we're grouped into that was defined by area and genotype in mean intake.
Then using those six categories that I showed you on that you were able to
try and work out what how much sort of predicted alcohol intake essentially,
you know, if you were in Category six that worked out about sort of four drinks a day is about sort of 280.
And you know, if you were in category one, it was less than one drink a week.
And then we did the same sort of thing for for women as well.
Because even though women didn't drink much data, some people did drink.
So you could still time sort of categorise those as well.
And then we checked to see what was the relationship with smoking, for example, and this is what we would like to see.
So actually the, the, the kind of pattern exactly the same across the different categories for smoking, for example.
So that would suggest that, you know, it's working through alcohol and not some other sort of pathway.
And I'm only showing you this because it's kind of striking, but we looked at other things as well,
you know, to make sure that it wasn't sort of related to other potential sort of confounders.
So then we tried to have a look at genes,
these genetic categories that predict alcohol intake to actually how does it relate to sort of various sorts of things.
So you can see here that actually there's quite a strong positive association with blood pressure.
So if you remember from the European one, they saw a slightly sort of lowering of blood pressure.
It was very small, whereas this is suggesting that actually, you know, drinking more actually raises your blood pressure by quite a lot.
And, you know, it does raise HDL,
but only by a small amount and gets kind of like a good marker of the liver function that shows whether you've been drinking or not as well.
So again, you can see that it's related to that.
So one of the reasons we were doing this was to try and say, well,
is it relates to things that we know sort of biologically and physiologically that have been shown before.
So here's what we're looking at for alcohol in this scheme stroke.
One of the reasons for showing these side by side was for you saw these kind of j-shaped associations in the kind of prospective cohort.
So you still see that in our data if we look at the observational data.
But when you look at the genetic, it seems to be, you know, clearer in terms of,
you know, that there's that definite sort of harm from, you know, sort of drinking.
Similarly, for haemorrhagic stroke, again, you see the same sort of j-shaped.
At lower levels. But again, for the genetics, you don't see that.
You see it's sort of fairly sort of clear positive association for MRI.
So this was one that was sort of looked like it was beneficial.
It's inconclusive. Some genetic here, I would say.
I mean, you know, the confidence interval sort of overlaps.
One could be something going on. You know, it could be a lack of power, but it doesn't look like there's any benefit.
If you remember, what we saw from that previous meta analysis in Europeans is there's a very strong inverse relationship with MRI.
We didn't see that here. Do you?
The thing was looking it women. Women act almost like a negative control.
So essentially, women rarely drink and or smoke as well.
In fact, you know, looking at the analysis in, women can actually say, well, eat due to alcohol intake.
And there were no positive associations with the biomarkers, you know, blood pressure, HDL, GTI and stroke or am I in in women?
You know, that would suggest it's sort of alcohol that's, you know, sort of driving it.
We then just tried to look at some other things. So things like sort of BMI and glucose.
And again, it's all positive associations of BMI and glucose with, you know, genetically predicted alcohol.
For diabetes, we saw some kind of positive association.
Again, it's sort of modest.
And actually, when we adjusted for BMI, it's it's all gone away.
And actually, that that kind of makes sense, really. So it's mostly driven by the adiposity rather than sort of alcohol.
And then we also had to look at that cancer.
So, I mean, there's all that benefit for, you know, potentially people that thought there were benefits for cardiovascular disease.
But essentially, you can see there's quite strong associations for different types of cancer that related to sort of alcohol consumption.
And you know, for lung cancer again just looking in evening never smokers.
You know that's that's probably the one to sort of look at you can see I mean, the numbers are small, but,
you know, there's a positive association in sort of never smokers with, you know, alcohol and lung cancer.
People are talking about things sort of like so the dietary factors, the various,
the things we looked at, those are things like linoleic acid and omega three fatty acids.
It looks like. Yeah, it raises those things sort of moderately.
You know, so there could be, you know, if there are any benefits, it could be sort of working through these things.
But, you know, the effects are sort of fairly small. The global burden of disease, you know, says, well, you know,
actually there's quite a few deaths related to sort of alcohol and disability life years and all those kind of things.
And obviously, it's not just sort of CVD.
There's things like injuries and digestive disease and various of the things that can be caused by sort of alcohol.
And it's sort of violence and various other things too.
So those apparent protective effects that they are showing and saying that, you know, benefits and that it's all based on conventional epidemiology.
So, I mean, is that the right thing to be working with if if there is clear evidence that this J-shaped association that seen could be due to sort of,
you know, sort of biases some of the genetic evidence.
Essentially, stroke increases by about a third for every four drinks a day.
You know, moderate drinking doesn't appear to protect against stroke.
Didn't appear to be any clear benefits for me.
We showed that, you know, genetically predicted alcohol based blood pressure, glucose adiposity, those kind of things.
So, yeah, there's a lot of other things going on. And of course, it's associated with several cancers.
Just to conclude. It would say that, you know, that essentially genetic epidemiology suggests that, you know,
the observational work is non causal, you know, so essentially the protective effect of moderate alcohol is not causal.
You know, it does raise blood pressure. And that's that's that's not a good thing.
The it looks like it raises blood pressure to a greater extent than it increases
things like HDL and some of the other things that are considered sort of good.
But it could be that for me, you know,
some of the kind of benefits that are going on could have cardioprotective effects
that actually would being balanced out by sort of raising that pressure example,
which is why you didn't see the line definitely going up for MRI.
It was, you know, sort of fairly flat. So there could be a couple of things that are counterbalancing what's going on there.
To finish, taken together, I would suggest that the harms outweigh the benefits.
So that was my final point. So thanks.
And the collaborators from Google Biobank and the various sort of funders and that's it.