What If 'Normal' Is No Longer Good Enough?

By Dr. Pascale Ricci, Head of Precision Nutrition at REVIV

"Normal" blood test ranges are defined by population-based reference intervals (typically the central 95% of values from healthy individuals), but this approach has significant limitations for individual health optimization. 

Population ranges fail to capture individual biological variation, set points, and early pathophysiological changes that can occur years before clinical thresholds are crossed. 

Population-based reference intervals are established by testing at least 120 healthy individuals in a given demographic group and adopting the central 95% of values as "normal".  This methodology inherently labels 5% of healthy individuals as abnormal and fails to account for individual biological variation, physiological rhythms, age, sex, ethnicity, and other factors that influence test results. Reference ranges represent statistical constructs rather than physiological optima for any given individual.

Individual set points may differ substantially from population averages. Research demonstrates that each person has unique, stable biomarker set points around which their values fluctuate. An individual whose results fall within the population "normal" range may still be significantly outside their own optimal range, representing early pathophysiological change for that person. 

The "index of individuality" measures how useful a population's normal range is for interpreting an individual's test result. When it's below 0.6, it means people tend to have their own stable "normal" level, so comparing a person's current result with their past results is often more informative than comparing it with the laboratory's reference interval.

Population-based reference intervals treat patients as members of a group rather than as individuals. 

Key limitations include:

• Failure to detect meaningful individual changes: A person's biomarker can shift substantially within the "normal" range yet represent significant physiological deterioration for that individual 
• High false-negative rates: Early disease states often manifest with biomarker values still within population ranges 
• Inability to take into account for how something changes over time: Static ranges cannot identify trends that predict future disease 
• Demographic diversity: Age, sex, and ethnicity significantly affect biomarker distributions, yet many laboratories use overly broad reference ranges 

Biomarker changes can manifest years or even decades before clinical disease diagnosis. 

For example:

• Diabetes: Metabolic biomarkers show distinct changes up to 20 years before diagnosis.
• Cardiovascular disease: High-sensitivity troponin (a protein found in cardiac muscle) levels below conventional detection thresholds can predict cardiovascular events such as heart attack and death. 

Longitudinal monitoring tracks changes in health over time, enabling earlier and more accurate disease detection than single measurements. Dynamic risk prediction models that incorporate biomarker trends provide a better understanding of disease progression. Because healthy individuals exhibit stable biomarker levels over time despite substantial differences between individuals, personalized baseline values are more informative than population averages. 

Adaptive reference ranges, which update with each new measurement, can identify abnormal physiological changes early and have demonstrated value in routine annual health assessments.

Behavioural interventions targeting diet and physical activity produce measurable improvements in biomarkers such as weight and waist circumference, systolic and diastolic blood pressure, even in individuals without established cardiovascular risk factors. 

Some further examples:

• For triglyceride reduction, lifestyle interventions show dose-response effects: per every 1 kg of weight lost, triglycerides decrease by approximately 4.0 mg/dL. 
• Mediterranean dietary patterns demonstrate the strongest evidence for LDL-C (low-density lipoprotein cholesterol) reduction and cardiovascular outcomes improvement. 
• The Diabetes Prevention Program demonstrated that intensive lifestyle intervention such as, ≥150 minutes/week moderate physical activity, reduced diabetes incidence by 60% compared to placebo in individuals with prediabetes. 

These data support early intervention when biomarkers show unfavourable trends within the "normal" range, rather than waiting for clinical thresholds to be crossed.

The precision health model rejects the "wait until abnormal" approach in favour of identifying individual trajectories, understanding personal set points, and intervening with lifestyle modifications when trends indicate movement away from optimal individual baselines; often years before population-based reference ranges would flag abnormality.