Normal vs Optimal Values

Section 1

The fundamental distinction

A reference range tells you where 95% of a population falls. An optimal range tells you where outcomes — lower disease risk, better long-term health — are best.

If the population being studied has a high burden of metabolic disease, the "normal" range for fasting glucose, triglycerides, or waist circumference will shift upward to reflect that population — not reflect ideal health. You can be "normal" and still be at elevated risk.

Optimal ranges come from a different type of study: longitudinal outcomes research. Researchers follow thousands of patients for years and examine which biomarker values at baseline predict the best long-term health outcomes. This gives a different — often narrower — target range.

Example: LDL cholesterol

The standard reference range upper limit for LDL is around 3.0–3.5 mmol/L in most labs. But cardiovascular guidelines recommend <2.0 mmol/L for high-risk patients and <1.4 mmol/L after a heart attack. "Normal" and "optimal" diverge by more than 100%.

Section 2

Tests where optimal ranges are evidence-based

For several key biomarkers, medical guidelines provide outcome-based targets that differ meaningfully from population reference ranges.

These are the tests where decades of research and clinical trial data justify a specific target range — and where hitting that target measurably improves outcomes.

HbA1c

Normal <5.7% · Optimal for most diabetics <7.0%

Below 5.7% is the population reference. The ADA target for managed diabetes is <7.0%, though individualized targets may be lower for some patients.

LDL-C

Normal <3.0 mmol/L · Optimal varies by risk

High-risk patients benefit from <1.8 mmol/L. Very high-risk (post-MI) benefit from <1.4 mmol/L. Population normal range is not clinically useful for cardiac risk management.

Vitamin D

Normal >50 nmol/L · Optimal 75–150 nmol/L

Deficiency is clear below 25 nmol/L. Many practitioners and guidelines suggest optimal bone and immune function sits between 75–150 nmol/L, above the basic sufficiency threshold.

Blood Pressure

Not a blood test but the same principle

Population "normal" used to include up to 140/90. Current evidence supports treating at ≥130/80 for most adults, as outcomes improve at lower targets.

Section 3

Where "optimal" ranges lack robust evidence

A growing number of functional medicine practitioners quote optimal ranges for tests where the clinical evidence for those specific targets is limited or absent.

Testosterone, ferritin, free T3, cortisol, and many micronutrients are commonly cited with "optimal" ranges that differ from standard reference ranges. In some cases, there is genuine emerging evidence. In others, the optimal range is based on expert opinion, small studies, or theoretical mechanisms rather than large-scale outcomes data.

This does not mean these practitioners are wrong — it means the evidence base is less certain. When a practitioner suggests your result, though in the reference range, should be "optimised," it is reasonable to ask: what clinical outcomes data supports this specific target, and what are the risks of the proposed intervention?

The key question

Ask: "If we treat to this target, what outcome data suggests my long-term health improves?" Evidence-based optimal ranges have an answer. Speculative ones often point to theoretical mechanisms instead.

Section 4

Using this in practice

You are not trying to have perfect numbers — you are trying to reduce your risk of long-term harm.

For biomarkers with strong outcomes data (LDL, HbA1c, blood pressure, eGFR in kidney disease), working toward an evidence-based target is worthwhile. For biomarkers where optimal targets are speculative, focus first on whether you have symptoms and whether your result is trending in the wrong direction.

Bring your previous results to appointments. A trend is almost always more informative than a single snapshot. Your doctor can use longitudinal data to identify early drift toward risk before it crosses into out-of-range territory.

Section 5

Functional medicine ranges — where is the evidence?

Functional and integrative medicine practitioners often use narrower optimal ranges derived from clinical experience rather than large outcomes trials.

Functional ranges for ferritin (often cited as 70–150 ng/mL for optimal energy, vs the standard lower limit of 15 ng/mL) are based on observational associations between higher ferritin and symptom improvement — not on randomised controlled trials showing that supplementing to this target improves measurable outcomes.

This does not make the concept wrong. There is a difference between "no evidence of harm from a low normal value" and "evidence that this value is genuinely optimal". Functional ranges often sit in the former category — plausible, sometimes clinically useful, but lacking the outcome study architecture that drives formal guideline targets.

Ask for the study type, not just the claim

When a practitioner cites an optimal range, ask: is this based on a randomised trial with hard clinical endpoints, or on cross-sectional symptom associations? Both can be valid, but they carry different levels of certainty.

Section 6

When optimal targets should be individualised

Even evidence-based optimal ranges are not universal — your personal target should account for your specific risk profile, age, and comorbidities.

The HbA1c target of less than 7.0% for type 2 diabetes applies to most adults but not to elderly patients with multiple comorbidities, where a target of up to 8.5% may be more appropriate to avoid hypoglycaemia. The optimal LDL target for a 35-year-old with no risk factors is very different from that for a 60-year-old with established coronary artery disease.

Individualised targets require a clinician who knows your full history. A number on a lab report, however optimally interpreted, is only useful in the context of who you are and what you are trying to achieve.

Section 7

How to track progress toward optimal values

If you and your doctor have agreed on specific target values, tracking them systematically makes your progress visible and consultations more productive.

Keep a personal spreadsheet or health tracking app that stores both your result and the reference range from each report. Over time you will see whether you are trending toward your target and at what rate — which helps your doctor assess whether current management is adequate.

For tests where you are trying to optimise, testing intervals matter. LDL reflects weeks of lipid metabolism. HbA1c reflects 3 months. Testing too frequently gives noisy data. Testing too rarely misses drift. Follow the monitoring intervals your clinician recommends.

Section 8

The risk of over-optimising

Pursuing optimal values can occasionally cause harm — particularly when the intervention required to reach the target has its own risks.

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Iron: supplementing to optimal ferritin can cause overload

High-dose iron supplementation to reach ferritin above 100 ng/mL in someone without iron deficiency anaemia risks iron overload — associated with joint damage, liver disease, and cardiovascular harm.

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Vitamin D: doses needed for very high levels carry risk

Supplementing to achieve vitamin D levels above 150 nmol/L requires high doses that can cause hypercalcaemia. The benefit of very high vitamin D levels is not well-established, but the harm of toxicity is.

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Testosterone: supraphysiological levels are not optimal

Targeting the high end of the testosterone reference range, or above it, requires pharmacological intervention and carries cardiovascular and haematological risks.

✓

The principle: optimise where evidence supports, proceed cautiously where it does not

For LDL, HbA1c, and blood pressure, aggressive optimisation has clear evidence. For micronutrients and hormones, discuss the specific intervention required before committing.

Normal and optimal are not the same thing — here's the difference.