What the Reference Range on Your Lab Results Actually Means

Your blood test results arrive with a range beside each number, and one of them is marked H or L. Before you worry, it helps to know what that range is. It isn’t a line between healthy and sick. For most tests, it’s a description of where most healthy people’s results fall, and by design, some healthy people fall outside it.

This article is general information, not medical advice. Your clinician is the right person to explain what your results mean for you.

Where the Range Comes From

For most tests, the range printed on your report is a reference interval. The Clinical and Laboratory Standards Institute’s guideline EP28, followed by labs around the world, describes how one is made:

1. Choose a group of healthy reference individuals, usually at least 120 for each group the lab reports separately, such as by sex or age.
2. Measure the test in all of them, with the lab’s own method.
3. Take the central 95% of their results: from the 2.5th percentile to the 97.5th.

So by construction, 5% of results from healthy people fall outside the range, half above and half below. Ozarda’s review of reference intervals points out that these results are flagged as abnormal even though they come from healthy people.

Why One Flag Is Common

A routine panel can contain 20 or 30 tests. If each test flagged 5% of healthy results, and the tests were independent of one another, the chance that a healthy person had at least one flag among 20 tests would be about 1 − 0.95²⁰, or 64%. Real tests aren’t fully independent, so the true figure differs, but the point stands: one result just outside its range is often not a sign of anything.

What matters more is how far outside it is, whether it’s one of several related results moving together, and whether it’s new for you.

Why Ranges Differ Between Labs

You may notice that two labs print different ranges for the same test. That’s expected:

• Different methods. Labs use different instruments and reagents, and a range belongs to the method it was measured with.
• Different populations. Reference groups differ by age, sex, ancestry, and body size. Ozarda recommends dividing ranges by age and sex at a minimum, and notes that some tests differ noticeably between populations.
• Different units. The same glucose result can be reported in mg/dL in the United States and in mmol/L in Canada and many other countries.

So always read a result against the range printed beside it, on the same report, rather than one from another lab or the internet.

Ranges Aren’t Targets

Some tests don’t use a population range at all. Cholesterol, blood glucose, and hemoglobin A1c are usually judged against clinical decision limits: thresholds chosen from studies of what happens to people above and below them, not from the spread of healthy results. Ozarda’s review stresses the difference and warns that printing both in one column causes confusion. If your LDL cholesterol is “within range” but your clinician sets a lower target, the target is what applies to you.

Reading Your Own Results Over Time

Your results compared with your own past results can tell you more than any one report:

• Keep every report, with the date the sample was taken and the lab.
• Compare like with like: the same test, ideally from the same lab, under the same conditions (fasting or not, time of day).
• Look at direction. A value drifting upward over three years says something a single result can’t.
• Keep calculated and measured results apart. LDL cholesterol, for example, can be measured directly or estimated by one of several equations (Friedewald, Martin-Hopkins, the NIH equation), and each can give a different number.

Keeping Your Results in Kaizora

Kaizora’s Labs page keeps each report as you received it.

1. Log a report. On Labs, choose Log. Set when the sample was taken and, if you like, the lab as its device. Add each result with its value, a comparator for results printed like “< 0.5”, and the range printed beside it. Reports are typed in today; importing lab files isn’t available.
2. See what’s flagged. The Labs page lists each test’s latest result, with those outside the lab’s printed range first, marked Low or High.
3. Follow each test. Every test is its own metric, with its own page and chart over the years: Fasting Plasma Glucose, Hemoglobin A1c (NGSP), Serum Ferritin, Thyroid-Stimulating Hormone, and more. LDL Cholesterol (Direct), (Friedewald), (Martin-Hopkins), and (NIH Equation) are separate metrics, so they never share a line.
4. Add your targets. To have values flagged on a test’s chart and on Home, add a reference range on its page, such as a target your clinician gave you, with its source. See Reference Ranges.

A note on units: for tests reported both in mass units and in molar units, such as glucose and cholesterol, Kaizora records the mass concentration (mg/dL) today. Converting from mmol/L, which needs each substance’s molar mass, is coming. Tests always reported in mmol/L, like sodium and potassium, are kept in mmol/L.

Lab results are one of the few things you’ll want to see years from now. You can keep them beside your blood pressure, weight, and sleep, and export everything whenever you like.

The Short Version

• A reference interval is the central 95% of results from healthy people, so 5% of healthy results fall outside it.
• With many tests, one flag is common and often means little on its own.
• Read each result against the range on its own report; ranges differ by lab, method, population, and unit.
• Cholesterol, glucose, and A1c are judged against clinical targets, not population ranges.
• Your own trend over the years is often the most useful thing to watch.

If you’d like one place for every lab report you’ve had, Kaizora is free to start.

Sources

• Clinical and Laboratory Standards Institute, EP28-A3c: Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline, 3rd ed. (2010). Preview
• Ozarda Y, “Reference intervals: current status, recent developments and future considerations,” Biochemia Medica 26, no. 1 (2016): 5–16. doi:10.11613/BM.2016.001