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So today I want to do something a little bit different and do a podcast on
correlation and a practical guide to not getting fooled by microbiome data.
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So let’s start with a very normal internet moment.
You’re scrolling, you see a gut bacterium that predicts anxiety.
Then you may see low diversity means inflammation,
or this microbiome score says you’re 83% optimized.
And you’re like, OK, should I eat kimchi or call a priest?
Seriously, though, the truth is that most microbiome claims are correlations.
Some are useful, but many are not.
So today I’m giving you a checklist with examples so you can spot the difference
fast without becoming the person at dinner who ruins everyone’s mood.
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Microbiome data sets are ridiculously high dimensional.
That means there are a lot of variables,
many ways to slice them,
and about a million chances to find a pattern that looks meaningful.
If you measure 500 bacteria and 50 lifestyle variables, you can discover relationships all day.
That’s not fraud.
That’s the math of many comparisons.
The problem is when people treat associated with,
like it means cause,
or like it means treat this and you’ll feel better.
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All right, checklist time.
Eight checkpoints, each with an example.
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The first one is what outcome are they claiming?
So the first question you should ask yourself is what outcome are they actually talking about?
An example here could be a company that says our probiotic improves gut health.
You read the fine print and it says gut health measures or means their internal
balance score moved from 62 to 68.
That’s not a clinical outcome.
That’s a scorecard that they built.
A real outcome would be fewer IBS symptoms per day,
lower fecal calprotectin,
improved HbA1c,
fewer antibiotic-associated diarrhea episodes.
So your rule should be,
if the outcome is a score they invented,
treat it like a hypothesis,
not a health claim.
Quick translation for listeners.
Basically, symptoms and validated clinical endpoints beat vibes.
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Number two, what did they measure?
Bugs, function, or just you?
This question asks, what kind of data is it?
Someone posts, this bacteria means you’re inflamed.
But what did they measure?
Did they do 16S sequencing?
That tells you relative abundance of bacteria, but not whether your body is inflamed.
So imagine two people have the exact same stool microbial profile.
One sleeps eight hours a night, eats fiber, and has no chronic stress.
The other is sleeping four hours, running on caffeine, is on a drug like metformin.
Their host inflammation markers could be totally different,
but their microbial pattern could be the same.
So if the claim is about physiology,
inflammation,
insulin resistance,
stress,
you need to ask yourself,
did they measure host markers?
These could be something like glucose, calprotectin, CRP, cytokines.
Or are they just guessing health from taxa?
Guessing is allowed, but it’s not allowed to pretend that it’s certain.
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Number three, does the microbe cause the outcome or reflect it?
The third question that you should ask yourself is, which direction does causality go?
So an example could be,
in inflammatory bowel disease,
you often see reduced diversity and shifts in specific taxa.
Does that mean low diversity causes IBD?
Or does inflammation,
diet restriction,
diarrhea,
steroids,
immune suppressants,
and antibiotics cause low diversity?
Often the microbiome is a readout of the disease environment.
An analogy that makes this more coherent could be when you see smoke,
it correlates with fire,
but smoke doesn’t cause the fire,
it’s the signal.
So when you hear microbe X predicts disease,
you should think to yourself,
did they prove direction or did they just observe a snapshot?
Snapshots are fine, but they are not causality.
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Number four, confounders, the sneaky villains.
The fourth question on this checklist is, did they control for obvious confounders?
Here’s an example.
A study finds that microbe Y is associated with type 2 diabetes,
but half of the cohort is on metformin,
and metformin is known to shift the microbiome.
So the disease signature might actually be a metformin signature.
Another example could be that a depression microbiome study does not track SSRIs,
sleep,
diet,
alcohol,
or BMI.
This is like studying lung health without asking who smokes.
So you should ask yourself,
if the study didn’t measure medications and diets,
you shouldn’t treat the conclusions as actionable.
And if you’re a consumer, that’s the same rule.
If your stool test doesn’t ask about meds and diet, the insights are basically running blind.
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Number five, compositionality, the relative abundance lie.
This one’s nerdy, but it’s important.
Microbiome data is usually relative.
So think about a pie chart, and bacterium A is 20% of that pie.
The next week, you do the same measurements, and bacterium A is 30%.
Did bacterium A increase?
Or did bacterium B and C decrease and A stayed the same?
So A’s slice just looks bigger.
The real consequence here is that people see proteobacteria increased and they panic.
But without absolute abundance or total bacterial load,
you don’t know if it truly expanded or if something else shrank.
So you should think or ask yourself,
did they measure absolute abundance or only relative percentages?
Relative only equals uncertainty.
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Number six, pipeline fragility, same data, different answers.
The sixth question is, would the results survive a different analysis?
So your example here is that you have two labs analyze the same data set.
Lab one filters rare taxa aggressively and uses method A.
They get bacterium M and N are significant.
Lab two keeps rare taxa, but they use method B. They get bacteria P and Q are significant.
Both publish.
Both claim discovery.
Welcome to chaos.
Strong studies do robustness checks.
They say we tested multiple approaches and the signal held.
Weak ones give you a pipeline, one p-value, and a confident headline.
So when you see something like this,
you should think,
did they show robustness or just one brittle result?
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Number seven, external validation, the adult test.
This basically asks, does this work in a new group of people?
So a company that says they can predict IBS from stool.
They trained their model in one city, one diet culture, and one sequencing pipeline.
Then someone tested on a different cohort, and performance drops from wow to meh.
That’s common.
Microbiomes vary across geography, diet patterns, lab methods, and time.
So internal cross-validation is not enough.
It’s like practicing a speech in your bedroom.
It’s helpful, but it doesn’t mean it will land at the conference.
You need to think, show me external validation.
Different cohorts, different sites, same result.
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Number eight, effect size and usefulness.
Final question here should be, even if it’s real, does it matter?
A study that finds a statistically significant association between microbe Z and
improved sleep,
but the difference is tiny,
like two minutes of sleep per night.
P-value is impressive, but your life remains unchanged.
Another version could be that a score shifts but symptoms don’t.
That’s very common in wellness products.
So your line,
or your thinking should be,
is the effect large enough to change decisions or outcomes?
Statistics can be significant while your body remains unimpressed.
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So what to do with all of this?
When you see a microbiome claim, you should think about these questions.
And here’s the non-cynical approach.
Treat correlations as a map of possibilities, not a prescription.
If your diet is low in fiber and you see low representation of common fiber
fermenters,
that’s not a diagnosis.
It’s a useful nudge.
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Increase fiber slowly, track your symptoms, and don’t assume any single microbe is your destiny.
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But if someone says your microbiome proves you’ll get Alzheimer’s or proves you’re
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inflamed or proves you need supplement X that only they can provide,
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that’s where you pull out this checklist and go full skeptical adults.
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So recapping this whole eight checkpoints,
Number one, asking what outcomes are they claiming?
Number two, what did they measure?
Number three, what’s the direction of causality?
Number four, did they handle their confounders well?
Number five, relative versus absolute abundance?
Number six, robust to analysis?
Number seven, was it externally validated?
And number eight, is it big enough to matter?
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If you use these thinking points in this checklist,
you won’t fall for every single shiny microbiome story.
And you also won’t miss the real ones.
Because the microbiome is real science.
It’s not just magic.
