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Pauli Ohukainen

25 Oct, 9 tweets, 4 min read

I find this quite interesting; the estimates for the association between wearing a #mask and some coronavirus infection in non-hc setting (0.56, 95% CI: 0.40-0.79) is roughly the same magnitude as the association for #bicyclehelmet and head injury (0.49, 95% CI: 0.42-0.57) 1/9

Are they both confounded? You bet. But they're also both mechanistically plausible. And there's some major issues with conducting RCTs for both. Ethical reasons for helmets, and for masks, statistical power issues (adherence, sample size, disease prevalence) seem pretty bad 2/9

With helmets, the people wearing them probably have some different characteristics than non-users, that causes confounding. Probably they're more cautious and take less risks in traffic. It's also possible that for some individuals, helmet causes risk compensation... 3/9

...but not to the extent that it would offset the population-level benefit. And in fact, adjusting for risk compensation might not change the results 4/9

academic.oup.com/ije/article/46…

It should also be noted that indeed, wearing a helmet doesn't prevent head injuries altogether; they just reduce the risk. Mandatory helmet legislation can't be expected to eliminate head injuries and helmet's don't protect from other injuries sustained from falling 5/9

This is analogous to #facemask discussion; no, masks don't stop the entire spread of #COVID19, just reduce the risk. Masks don't stop all routes of transmission. Failure of containment in some countries with mandatory masking is not evidence that they're useless. 6/9

I think it's probable that masks are in fact a multifactorial population-targeted intervention where looking at the observational estimate is very useful. It captures also the psychology of masks; they're a constant reminder not to touch your face, safe distances etc. 7/9

Observational estimate for mask is confounded by factors not directly linked to blocking droplets, but the recommendations are aiming to combine those confounders into practice. The estimate is also a likely combination of protecting the wearer from others but also vice versa 8/9

Back to helmet analogy, a pandemic is like we're all cyclists and the streets are frozen - in public places, we're constantly exposed to falling. I think it's useful to think about wearing a mask in a similar way; you have a tool for likely cutting your risk roughly in half 9/9

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More from @POhukainen

Pauli Ohukainen

@POhukainen

16 Feb

This is a pretty cool and elegant study: Monogenic vs Polygenic Hypercholesterolemia and Atherosclerotic #Cardiovascular Disease Risk ja.ma/2UYCZ4G

A brief 🧵 (1/11)

Key point was to compare the risk between three groups:

1️⃣ LDL high because of a pathogenic single-gene variant
2️⃣ LDL high because of a particularly strong effect from a combination of variants in 223 genes
3️⃣ LDL high but neither of the above genetic effects

(2/n)

In other words the comparison is between:

1️⃣ One physiological LDL pathway malfunctioning
2️⃣ Some combination of multiple pathways producing high LDL
3️⃣ LDL high mainly due to non-genetic effects (most likely diet, exercise, the usual)

(3/n)

Read 11 tweets

Pauli Ohukainen

@POhukainen

14 Dec 19

@ATHjournal

Our latest paper fresh out in @ATHjournal! We asked: "If we used an #AI algorithm to group individuals based on similar blood profile of 44 #lipid measurements, could we improve coronary #heartdisease risk prediction?" Turns out: no.

Thread! 🚨 (1/13)

atherosclerosis-journal.com/article/S0021-…

We looked to see if we could detect subgroups in the population that would have a) similar blood profiles across 14 lipoprotein subclasses and b) possible differences in CHD risk. The algorithm we use is called a self-organising map (SOM)

See: academic.oup.com/ije/article-ab… (2/13)

SOM is a tool for multivariate subgrouping. Essentially the user puts in variables and the algorithm groups individuals based on similarity of these inputs. Here's a rough emoji-schematic of how this applies to population cohorts (3/13)

Read 13 tweets

Pauli Ohukainen

@POhukainen

12 Nov 19

@doctortro

I do my best not to block ppl on Twitter but I'm now blocking @doctortro. I know it'll cause some to call me cowardly or whatever but it's none of that. It's also not some emotional tantrum but rather the result of cool-headed contemplation. Here's a #WhyIBlock thread (1/13)

In almost 6y in Twitter, I've only ever had to block two accounts and one was temporary. I get the appeal and of course everyone does what's best for them. I just like to hold myself a high bar. I find blocking "too easy" when even strong disagreements could be explored (2/13)

I especially don't think knee-jerk blocks are for me. I've often had the temptation but after some cool-down I've decided not to. I do mute some accounts occasionally if they constantly cause my notifications to go crazy.

That's why I now want to explain this decision (3/13)

Read 13 tweets

Pauli Ohukainen

@POhukainen

2 Aug 19

🚨 Watch out, time for another #cholesterol #tweetorial! 🚨This time we’re going to take a look at the very fundamental nature of cholesterol molecule. It has biochemical properties that make it 1) essential to life but 2) problematic in excess. So let’s get to it! (1/33)

First, as everyone can read in wikipedia or a high-school level biology book, cholesterol is used to make many things. It’s a good building block for certain hormones, cell membranes and bile acids. But why? (2/33)

The reason is this sturdy multi-ring structure that has many “corners” for attaching stuff. Depending on specific needs, different types of molecular side chains can be attached to this basic frame (3/33)

Read 33 tweets

Pauli Ohukainen

@POhukainen

25 Jun 19

#Tweetorial time! 🚨 Looks like there’s another misconception about #cholesterol and #atherosclerosis making rounds again. This one has to do with the process of #LDL particles entering the arterial wall. So gather round friends and let me science the s*it out of this 🤓 (1/24)

For background, here’s a widely cited series of figures by Nakashima et al. (2007). They performed autopsies on 38 people aged 7-49, who died of non-#cardiovascular causes (2/24)
ncbi.nlm.nih.gov/pubmed/17303781

Nakashima et al. looked for atherosclerotic lesions at different stages, according to a previously published morphological classification scheme. They found them, took slices, stained them to find #lipids and #macrophages and organized them nicely (3/24)

Read 25 tweets

Pauli Ohukainen

@POhukainen

25 Apr 19

@nature

🚨 Thread time! 🚨 Fascinating research *letter* (lol, contains more data than many "regular" papers combined!) published in @nature. Looks at the molecular mechanism of how #LDL #cholesterol particles get into arterial walls and cause #atherosclerosis | nature.com/articles/s4158…

The authors build upon previous knowledge that a particular scavenger receptor (SR-B1) has a key role. As the name implies, scavenger receptors pick up all sorts of stuff from their surroundings (2/14).

First, a transgenic mouse is made that has SR-B1 knocked out *specifically* from endothelial cells. This was done in combination with different athero-prone- and control mice and the data consistently shows that if there's no SR-B1, there's much less atherosclerosis (3/14)

Read 14 tweets

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