Why Reference Ranges Matter

If you’ve ever received lab results marked “High” or “Low,” you’ve already encountered the power of reference ranges. In medicines development, these cutoffs aren’t just helpful—they’re critical. They determine how we interpret biomarker data in diagnostics, clinical trials, and regulatory submissions.

But here’s the twist: “normal” is not one-size-fits-all. Reference ranges can vary based on population characteristics, lab methods, and even geography. And as trials become more global, calls for harmonisation—standardising how biomarkers are defined and interpreted—are growing louder.

So how are these ranges set? When should we standardise them? And when should we not?

Defining “Normal”: The Basics

Most biomarker reference ranges are established using data from a healthy population. The typical method is statistical: capture the middle 95% of values, flag anything below the 2.5th percentile or above the 97.5th as abnormal. For example, alanine aminotransferase (ALT), a liver enzyme, is considered elevated above ~50 IU/L in men or ~35 IU/L in women (Call, 2024).

Diagnostic companies like Roche or Siemens define these ranges when developing lab assays. The ranges are submitted with regulatory filings and included in Instructions for Use. However, clinical labs may revise these cutoffs based on their own population data or local studies. When this happens, labs update their internal systems (LIMS) and inform clinicians (Call, 2024).

Why Reference Ranges Aren’t Universal

There are good reasons why reference ranges may vary:

  • Sex: Hormones, enzymes, and metabolites often differ between males and females.
  • Age: Paediatric biomarkers change rapidly with development. Older adults may show different baselines too.
  • Genetics and ancestry: While some biological variation exists, applying ethnicity-specific ranges is controversial and may risk bias (Call, 2024).
  • Clinical context: Exercise, recent illness, or medications can all affect lab values. A single test result doesn’t tell the whole story.

In short: “normal” must be interpreted in context. That’s where harmonisation enters the conversation.

Harmonisation: What It Is and Why It’s Needed

As global trials become standard, inconsistent biomarker thresholds create real problems. A patient might qualify for a study in one country, but be excluded in another—just because their lab uses a different reference range.

This is where harmonisation helps. It aims to standardise biomarker definitions and interpretations, especially in multicentre trials and regulatory reviews.

According to Lee et al. (2018),

“Harmonization efforts should be undertaken in multicenter trials for accurate data analysis.”

Xiong et al. (2024) add that merging data from different studies requires aligned measurement standards. Without harmonisation, even the best-designed studies risk comparing apples to oranges.

Industry experts echo this concern. As one article put it:

“Unless that data can be quickly and accurately harmonized, it does nothing to advance pharmaceutical development or approval” (GEN, 2023).

Harmonisation Has Upsides… and Risks

The benefits are clear:

  • More reliable data across trial sites
  • Fewer delays in regulatory review
  • Greater safety through consistent clinical thresholds
  • Easier integration of companion diagnostics and real-world data

But the risks are real too:

  • Oversimplified cutoffs may ignore key biological differences
  • Reference values based on narrow populations could introduce bias
  • Rigid frameworks may delay adoption of new or more precise biomarkers

Most experts agree: harmonisation should enhance consistency—but not at the expense of clinical nuance or innovation.

What This Means for You

As a GMDP Academy learner, you’re preparing to work in a world where biomarkers guide major decisions—from who joins a trial to how a treatment is approved.

Here’s what to keep in mind:

  • Reference ranges aren’t fixed. They change with context, population, and purpose.
  • Harmonisation helps—but must be thoughtful. One global standard doesn’t fit everyone.
  • Context is key. Age, sex, and background matter when interpreting any result.
  • You have influence. Whether in trials, strategy, or policy, you’ll shape how biomarkers are used.

Stay curious, ask better questions, and lead with clarity.

Final Word

Biomarkers are essential to personalized medicine, but standardising how we define “normal” is both a scientific and ethical challenge. Harmonisation can streamline research and improve safety—but only if it respects diversity, context, and the pace of innovation.

Global Medicines Development Professionals (GMDP) won’t just follow these developments. They’ll help lead them—with clarity, evidence, and integrity.

Take the Next Step with GMDP Academy

🔍 Want to explore the regulatory side of biomarker interpretation?
 GMDP Academy’s Module 5 – Regulatory Affairs, Drug Safety and Pharmacovigilance explores how global frameworks, harmonisation efforts, and safety data shape the use of biomarkers in clinical trials and regulatory decision-making. It’s a vital step for anyone looking to lead in medicines development with clarity and confidence.

References

Call, T. (2024). How are healthy biomarker ranges defined? PharmaPhorum. https://pharmaphorum.com/rd/how-are-healthy-biomarker-ranges-defined

Lee, W. et al. (2018). Harmonization of laboratory results by data adjustment in multicenter clinical trials. Korean Journal of Internal Medicine, 33(6), 1157–1166. https://pubmed.ncbi.nlm.nih.gov/29065441

Xiong, M. et al. (2024). Correlational analyses of biomarkers that are harmonized. Scientific Reports, 14, 10293. https://pubmed.ncbi.nlm.nih.gov/37994004GEN. (2023). The Challenges of Harmonizing Biomarker Data. Genetic Engineering & Biotechnology News. https://www.genengnews.com/insights/the-challenges-of-harmonizing-biomarker-data

Disclaimers

  • The material in these reviews is from various public open-access sources, meant for educational and informational purposes only
  • Any personal opinions expressed are those of only the author(s) and are not intended to represent the position of any organization(s)
  • No official support by any organization(s) has been provided or should be inferred