“Obesity is genetic”

If you were just triggered, then this thread is for you.

If “but the human genome can’t evolve in a generation!” has passed your lips in the past few months, then this thread is for you.

🧬This is why obesity is genetic. And the environment is to blame. Let’s set the scene. It’s 1980s in America. Average BMI of adults is 25. There is a distribution around that value, whereby at the far right of that curve, 15% of the population fall past the threshold of obesity.

We looked something like this cartoon graph.

The @usda MyPlate serves as the primary educational tool for the Dietary Guidelines for Americans.

📊New CDC report: how many Americans have even heard of MyPlate? And how many tried to follow its guidance?

💣Are you ready for this?
🍽️1 The USDA MyPlate infographic was unveiled in *2011* replacing its predecessors (pyramid, pyramid with steps) that intend to convey to Americans what defines a healthy diet. They accompany the Dietary Guidelines for Americans updated every 5 yrs.

nytimes.com/2011/06/03/bus…

🍽️2

🥚Eggs and CVD🥚

Could there BE a more classic single food nutritional epi example? I submit that there is not!

When another egg paper recently popped up in @CircAHA I jumped at the chance to walk through some of my favorite fundamental issues with single food analyses. Let me first remind you of "Is everything we eat associated with cancer? A systematic cookbook review" and its scathing illustration published a decade ago now by Prof Ioannidis.

Take-away: Everything is good. Everything is bad. Nutritional epi get your act together.

Noted.

Some 📝nuggets from the editor's desk:

Introduction: NEEDS to hard sell the research's importance. Why is exposure and outcome of public health interest? What gap in knowledge do you seek to address?

But authors' common mistakes -- DO NOT pitch your hypothesis based on your... ✏️Results.

Your hypothesis & study should be interesting and needed regardless of its outcome. Keep results OUT OF THE INTRO. "Statistically significant findings" should *not* be the motivation to read your paper.

📜This comprehensive thread by lead author @KevinH_PhD on our new paper of what the modern Energy Balance Model (EBM) of obesity *is* and *is not* is a *must read*.

However - I want to emphasize a few points from the epi/population health P.O.V. that underscore the biology:

/1

https://twitter.com/KevinH_PhD/status/1489622951712354308

Firstly, we're talking about common obesity.

And the fact that it's increased Big League, both in the US and globally.

WHY?

/2

⏰WEEKLY NUT-EPI QUIZ!

Remember: we are ultimately interested in estimating long-term habitual intake for most nutritional exposures.

Back to this figure for now:
Green dots = true intake of nutrient X for a given day
Blue line = average of nutrient X across days measured If we have a 2 days (green dots) of accurate measurements for nutrient X we can calculate a mean and standard deviation to estimate the blue line.

Q1: If we *increase* number of days of accurately measured diet (more green dots), how does this impact our calculated mean intake?

💣It's LDL results time!

1/

https://twitter.com/deirdre_tobias/status/1466485389657526277

2/ Visit the above linked thread for some set-up. And this thread here by @kevinnbass gives further background into the motivation behind the hypothesis explored in this analysis:

https://twitter.com/kevinnbass/status/1466855371960553472?s=20

Does higher LDL cause increase risk of all-cause mortality?

📉Cholesterol-lowering therapies like statins are a cornerstone of preventing cardiovascular disease since the 1990s.

So with that, please join me in an epidemiologic adventure as we address an emerging alternative viewpoint that refutes the role of LDL (bad cholesterol) in CVD. A few weeks ago social media was riled up over a controversial claim to forgo lipid-lowering therapies for elevated serum LDL in patients where metabolic parameters were otherwise normal.

One of their tweets:

🧵 When it comes to diet/disease studies how well do the bodies of evidence: -RCTs- and -observational epi- agree?

@lschwinshakl et al tackled this oft-raised question with an impressively expansive systematic review @BMJ.

Here's a thread about *all of that*: Wouldn't it be nice to have RCTs to quantify the causal effect for every combination of nutritional factor, dose, population, long-term outcome?

Country-level correlation studies like the "lettuce will kill you" preprint provide epidemiologists with a good laugh-cry, & I want to be sure everyone understands why.

So why are diet/disease correlations (AKA ecologic studies) less than useful for deriving causality? 1/ The Y-axis represents country-level disease prevalence or rates. Actually can be useful in characterizing between-country differences. Wow Belgium is concerning!
2/