Why single-biomarker health scoring misses most of the signal
By Dr. Musa Abdulkareem ·
Standard blood panels present each biomarker with a "normal" or "abnormal" flag based on a population reference range. That framing is a product of printing costs from forty years ago, and it systematically hides the risk information that's actually there.
The problem with "normal"
A reference range is a statistical construct. It's typically the 2.5th to 97.5th percentile of some reference population — often healthy adults, sometimes subdivided by age and sex, usually not subdivided by anything else. When your lab report says a biomarker is "normal," it's telling you you fall somewhere in the middle 95% of that population.
That framing has two deep problems, and they compound.
Problem one: normal is not the same as optimal. A marker sitting at the 90th percentile of a reference range is technically "normal" in the sense that it's inside the range. But if the relationship between that marker and health outcomes is monotonic — as it is for markers like fasting glucose, LDL cholesterol, and homocysteine — then being at the 90th percentile is meaningfully worse than being at the 50th percentile, even though both are flagged the same way. The binary normal/abnormal view throws away the distance from the centre of the range, which is where the risk signal usually lives.
Problem two: individual markers don't carry individual meaning. Almost no clinically important question can be answered by looking at one biomarker in isolation. Cardiovascular risk is not just LDL. Metabolic dysfunction is not just fasting glucose. Chronic inflammation is not just CRP. Every one of these is a multi-marker pattern. And the patterns matter — a patient with elevated CRP and normal lipids is in a very different situation from a patient with elevated CRP and a poor lipid profile, even though the individual flags on the lab report might look similar.
When you treat biomarkers as a set of independent flags, you miss both of these things. You miss the graduated signal, and you miss the interaction structure.
What a better model looks like
A useful biomarker platform — the kind of thing we build for precision-health clients — does three things differently:
1. Graduated risk scoring, not binary flags
Every marker gets a continuous risk score based on its position within (and potentially beyond) the reference range, weighted by the empirical relationship between that marker and downstream outcomes. Fasting glucose at 88 mg/dL is scored differently from fasting glucose at 98 mg/dL, even though both are "normal." This is not exotic — it's just not doing the binary thresholding that printed lab reports do.
The grounding for this scoring comes from three sources: published clinical reference ranges, peer-reviewed literature on marker-outcome relationships, and functional medicine thresholds (which are often tighter than the reference range and reflect what's optimal rather than what's common). For markers where the literature is inconsistent, you build in explicit uncertainty and flag it as such.
2. Multi-marker interaction modelling
This is where the real value is. A cardiovascular risk score shouldn't just average LDL, HDL, triglycerides, and ApoB — it should capture that the ratio of ApoB to ApoA1 is independently informative, that the combination of high LDL and high CRP is worse than either alone, and that the pattern of HDL + triglycerides tells you something about insulin sensitivity that neither tells you individually.
Modelling these interactions is where much of the work sits. You're not doing deep learning here — the feature space is small (dozens to low hundreds of biomarkers) and interpretability is non-negotiable, so simple models plus clinically-grounded feature engineering beat complex architectures. But you are doing careful statistical modelling, with interaction terms, non-linear response curves where the literature supports them, and validation against established clinical risk calculators (Framingham, QRISK, etc.) for the dimensions where those calculators exist.
3. Multi-dimensional output, not a single number
Collapsing a hundred biomarkers into "your overall health score is 76/100" is actively unhelpful. The 118-biomarker panel we work with maps to 27 distinct health dimensions — cardiovascular, metabolic, hepatic, renal, thyroid, hormonal, inflammation, immune function, nutritional status, and more. Each dimension gets its own score, its own driving markers, and its own interpretation.
This structure matters because health risks aren't fungible. A patient with strong cardiovascular markers and weak hepatic markers has a very different problem from a patient with the reverse pattern. Averaging them into a single score hides the very information that would make the result actionable.
Why this doesn't happen by default
If multi-marker, multi-dimensional scoring is so clearly better, why isn't it everywhere?
Three reasons, in roughly descending order of weight:
Labs optimise for throughput, not interpretation. A commercial lab's job is to produce accurate numbers for individual markers, quickly and cheaply. Interpretation is someone else's problem — historically the ordering physician's. That model worked fine when panels had six markers and physicians could hold the pattern in their head. It's broken for 118-marker panels.
Regulatory caution. Anything that produces health-risk scores risks being classified as a medical device, with all the regulatory work that entails. This is navigable — you build the scoring engine with appropriate disclaimers and positioning, you partner with clinicians, you don't make diagnostic claims — but the regulatory overhead alone keeps most labs from engaging.
It's genuinely harder than it looks. The clinical grounding work is tedious. Every marker has a literature, every literature has conflicting studies, and turning them into defensible scoring functions is slow. The modular architecture to let those functions evolve as the evidence evolves is a second-order problem that most teams don't get to. And validating the resulting scores against real outcomes data takes years.
The payoff
When you do the work, the output of a single blood draw stops being a list of numbers and becomes a multi-dimensional health profile. Patterns that would be invisible in the raw data — a metabolic-inflammatory signature that precedes measurable disease, a hormonal imbalance reflected in three markers simultaneously, an early-warning pattern in the renal dimension before eGFR moves — become visible. And visible means actionable.
This is what precision health should look like. Not more biomarkers; better use of the biomarkers we already measure.
We build biomarker scoring platforms for precision-health clients across a range of panels and dimensions. If you're working on a platform that would benefit from this kind of modelling — or you have a panel that you think is underutilised — get in touch.
Related capability: Clinical & precision-health AI