Your blood test says “normal”. But normal compared with whom?

Open a typical blood test and you’ll see your result next to a reference range. If your number falls between the two limits, it may be marked as normal. If it falls outside, it may be flagged.

It’s a useful system. But “normal” can sound more definitive than it really is.

For many biomarkers, a reference interval is based on the values seen across a reference population. A common approach is to take the central 95% of results from that group.

In other words, the range is primarily telling you where your result sits in relation to a defined population. It isn’t necessarily telling you what is normal for you.

And people can differ considerably.

One striking example comes from a study of 1,002 adults published in Nature Medicine. Researchers gave participants standardised meals and measured their responses afterwards. Even when people ate the same food, their blood responses varied substantially. The population coefficient of variation was 103% for triglycerides, 68% for glucose and 59% for insulin.

In plain English, people’s bodies responded very differently to exactly the same input. Some people had much larger changes in their blood markers than others. The 103% figure describes how widely triglyceride responses varied across the group. It doesn’t mean everyone’s triglycerides increased by 103%.

PREDICT wasn’t a study of laboratory reference ranges, so we shouldn’t pretend it was. But it demonstrates a broader point that matters when interpreting health data: population-level numbers can hide considerable differences between individuals.

And that’s where your own history becomes interesting.

Where do reference ranges come from?

There isn’t one universal reference range for every blood marker.

Ranges can vary between laboratories because of the population used to establish them, the testing method and equipment, and other analytical factors. Some are also separated by characteristics such as age or sex where those differences materially affect the marker.

This is why comparing your result with a range you found online can be misleading. The relevant interval is generally the one supplied with the test, using the appropriate method and population.

There’s also an interesting consequence of the way many reference intervals are constructed.

If an interval contains the central 95% of results from a reference population, around 5% of results from that population will, by definition, sit outside it.

So “within range” does not literally mean healthy, just as “outside range” does not automatically mean unhealthy.

There is also an important distinction between a reference interval and a clinical decision limit.

They are sometimes treated as though they mean the same thing, but they answer different questions. A reference interval generally describes the distribution of results in a reference population. A clinical decision limit is a threshold used to inform a particular diagnosis, risk assessment or clinical decision.

For markers such as glucose, HbA1c and some lipid measurements, established clinical thresholds can therefore matter more than simply asking whether a result falls inside a laboratory’s population reference interval.

The International Federation of Clinical Chemistry review on reference intervals and clinical decision limits provides a useful explanation of the distinction.

What if your result is still “normal”, but has changed?

This is where looking at a blood test once can leave useful information on the table.

Imagine your result for a particular marker is comfortably within its reference range today. Six months ago, it was also within range.

On both reports, there may be nothing to flag.

But the two numbers could still be quite different.

That doesn’t mean every movement is significant. Blood markers naturally fluctuate, and results can be affected by everything from time of day and fasting status to exercise, illness, medications and the normal variation that happens within our bodies.

The laboratory measurement itself also introduces some variation.

But for many biomarkers, an individual's results tend to vary within their own pattern. That has led researchers to study within-person biological variation and ways of determining whether a change between two measurements is larger than we might expect from normal biological and analytical variation alone.

One of these tools is known as the reference change value.

You don’t need to know the calculation to understand the idea: rather than only asking whether your latest number falls within a population range, you can also ask whether it has changed meaningfully from your own previous results.

This isn't a fringe concept. Biological variation and serial measurements have been studied in laboratory medicine for decades. This review provides an overview of how biological variation can be used when interpreting laboratory results.

Could we have personal reference ranges?

Researchers have started exploring an interesting extension of this idea: whether enough previous measurements could eventually help establish more personalised reference intervals.

A 2021 study published in Clinical Chemistry used previous laboratory results to generate personalised reference intervals. For many of the analytes studied, these individualised intervals were considerably narrower than conventional population-based intervals.

That doesn't mean everyone should have their own diagnostic thresholds. And personalised reference intervals are not a replacement for established clinical decision limits.

But the research points towards something fairly intuitive.

Your previous results contain information about you that a population range cannot.

If your usual value for a marker has been relatively consistent across multiple measurements, a meaningful change from that pattern may provide additional context even when the new result remains inside the population reference interval.

This is what we mean when we talk about building a personal baseline.

It isn't a new definition of what is healthy. It's a record of what your biology has looked like over time.

Then there’s the rest of the blood test

There’s another limitation to looking only for individual red flags.

Blood markers don't exist independently of one another.

A clinician interpreting thyroid function, for example, may consider several related measurements rather than treating each number as a completely separate piece of information. The same principle applies across many areas of biology.

There is growing research into how laboratory results might be interpreted in this way. A 2025 paper in Clinical Chemistry and Laboratory Medicine explored multivariate reference regions, looking at combinations of related biomarkers rather than applying reference information to each analyte independently.

This doesn't make conventional reference ranges less useful. They remain an important part of laboratory medicine.

They are simply one part of the information available.

Why Aden tests more than once

This thinking is part of the reason Aden is built around repeat measurement rather than a single blood test.

Your first Aden panel covers 125+ biomarkers across key body systems. The results are analysed together to build a clearer picture of your health and inform a personalised protocol focused on what needs attention first.

Six months later, you test again.

The follow-up panel is compared with your previous results, so you can see what has changed rather than starting again with another isolated snapshot.

As more measurements accumulate, you begin to build something useful: a history of your own biological data.

Reference ranges still matter. Clinical thresholds still matter. The wider evidence still matters.

But now there is another piece of information alongside them: you.

Aden opens first in Dubai. Join the waitlist.

Aden provides health analysis and information and does not provide medical diagnosis or treatment.