There Is No Single Best Biomarker Blood Test

There is no best biomarker blood test, and no panel tests for everything. A blood panel is a bundle of independent assays. Each one has its own analytical precision, its own within-person biological variation, and its own sensitivity to how your blood was drawn and handled. The useful question is which markers you can measure precisely enough, and often enough, that a change in the number means something changed in you. Asking which panel has the most markers is not useful. The useful question has concrete answers. They depend on assay platform, pre-analytical handling, and cadence far more than on the marketing count of “75+ biomarkers.”

What you are buying when you order a panel

A consumer or clinical biomarker panel is a requisition: a list of test codes sent to a CLIA-certified, usually CAP-accredited laboratory, which runs each analyte on whatever platform that lab has validated. The same analyte name can mean very different measurements. Testosterone by competitive immunoassay and testosterone by liquid chromatography–tandem mass spectrometry (LC-MS/MS) disagree substantially at low concentrations, because immunoassay antibodies cross-react with structurally similar steroids. Estimated glomerular filtration rate depends on whether creatinine was measured by the Jaffe reaction or an enzymatic method, and on which equation the lab applies. Before you compare two results, confirm they came from the same method on the same platform.

This matters for portability. If you switch labs between draws, you have introduced a method change into your time series, and you will not be able to tell a real shift from a calibration difference. Our recommendation is to pick one reference lab and stay with it, recording the method and the assay-specific reference interval alongside every result.

Analytical noise, biological noise, and the reference change value

The number that tells you whether a change is real is the reference change value (RCV). For a two-sided 95% criterion:

RCV = 2.77 * sqrt(CVa^2 + CVi^2)

where CVa is the analytical coefficient of variation of the assay and CVi is the within-subject biological coefficient of variation. Both are published quantities for most common analytes, and studies of healthy individuals sampled repeatedly over months are the source of the CVi estimates and of the derived quality goals that laboratories use to judge whether their imprecision is acceptable.¹ Sodium has a CVi near 0.6 percent, so a 3 mmol/L move is large. High-sensitivity C-reactive protein (hs-CRP) has a CVi in the tens of percent, so a doubling between two draws can be pure noise.

The practical consequence is that a single draw of a high-variance analyte is close to uninformative. Three draws a week or two apart, averaged, is worth more than one draw of thirty extra markers. Build your requisition around that arithmetic.

Inflammation markers and what hs-CRP can and cannot tell you

hs-CRP (LOINC 30522-7) is the most widely available inflammation marker and the one with the deepest outcome literature, and it is what we would measure first. It is also acutely sensitive to anything that perturbs the acute-phase response. Examples are a cold, a dental procedure, a hard training session and poor sleep. Interleukin-6, tumor necrosis factor alpha, and adipokines such as leptin and adiponectin carry related but non-redundant information. In the LABS cohort of patients undergoing bariatric surgery, serum inflammatory and adiposity markers were measured alongside metabolic outcomes. The pattern of associations across markers was not uniform, which carries a general lesson: inflammation is not one axis with one best readout.²

If you want a single answer for personal tracking, use hs-CRP. Measure it at least three times at baseline in a period when you are not ill, and treat the trimmed mean as your baseline. Values above roughly 10 mg/L usually reflect an acute process rather than a chronic one. Interpreting them is a clinical question and does not belong to self-analysis.

Platform matters more than panel size

Two categories of assay dominate the interesting end of blood measurement. Targeted LC-MS/MS gives absolute quantification with defined limits of detection and is the reference for steroids, vitamin D metabolites, and many small molecules. Affinity proteomics gives breadth: Olink’s Proximity Extension Assay reads out on a sequencer and reports relative NPX units on a log2 scale, while SomaScan uses modified aptamers and reports relative fluorescence units. Both give you thousands of protein measurements from about 50 microliters of plasma. Neither gives absolute concentrations, cross-platform agreement for the same protein is often mediocre, and neither is a clinical diagnostic. Treat those data as a longitudinal self-comparison, where each protein is compared against your own prior values on the same platform.

The Alzheimer’s plasma biomarkers are the clearest current example of what a validated, clinically useful blood assay looks like. Phosphorylated tau 217 measured in a primary care setting identified amyloid pathology with accuracy well above that of clinical evaluation by physicians without biomarkers.³ Getting there required years of work on assay standardization, cutoff selection, and defining the intended-use population. That is precisely the work the field has argued must be done before a research biomarker becomes a clinical test.⁴ Most blood biomarkers you will see advertised have not had that work done. Multi-gene blood signatures for colorectal cancer, for instance, have reported strong discrimination in discovery cohorts for well over a decade without displacing colonoscopy or stool-based screening.⁵ Discovery is cheap now, largely because computational screening of existing omics datasets can nominate candidates at scale.⁶ Validation is the bottleneck, and blood-based cancer detection in particular has to contend with tumor-derived signals diluted into several liters of plasma.⁷

Pre-analytics: where most personal biomarker data goes wrong

Handling errors produce larger artifacts than most of the biology you are trying to detect.

• Glucose falls several percent per hour in a serum separator tube left at room temperature. Use a sodium fluoride/potassium oxalate tube (gray top) or insist on rapid separation.
• Potassium rises with hemolysis and with delayed centrifugation. A tourniquet held long, or fist clenching, inflates it further.
• Posture shifts plasma volume by roughly 10 percent between supine and standing, which moves every protein-bound analyte in concentration terms. Sit quietly for 10 to 15 minutes before the draw, every time.
• Cortisol and testosterone have strong diurnal rhythms. Fix the draw time to within about 30 minutes across visits.
• Plasma and red cell volume can themselves be estimated from routine hematology and protein markers, which is a reminder that hemoconcentration is a measurable confounder rather than an unknowable one.⁸

Capillary and point-of-care devices are improving fast and will eventually make high-cadence sampling practical, including microfluidic chips that multiplex kidney markers from a finger stick.⁹ Today, venous draws at a single lab remain the better substrate for a time series.

Getting the data into a form you can compute on

Ask the lab for HL7 FHIR Observation resources or an HL7 v2 ORU^R01 feed rather than a PDF. Each result should carry a LOINC code, a value, and a UCUM unit. It should also carry the reference interval and the specimen collection timestamp. Store one row per observation, and normalize units at ingest (ferritin in ng/mL versus µg/L is the same number, hemoglobin in g/dL versus g/L is not). Give each row the following columns.

• LOINC
• value
• unit
• method
• lab identifier
• collection datetime

Useful anchors while you build the schema: HbA1c is 4548-4, serum creatinine 2160-0, apolipoprotein B 1884-6. Once the series exists, compute the RCV for each analyte and flag deltas that exceed it rather than eyeballing the lab’s reference range, which describes a population and not you.

Genetics adds a second layer of context. Polygenic scores tested against thousands of clinical laboratory results across an electronic health record system associate with specific analytes in interpretable ways, which means your genome can tell you where your personal set point is likely to sit relative to the population interval.¹⁰ That is useful for deciding which markers to watch closely, and it does not substitute for measuring them.

Questions people also ask

What is the best biomarker blood test? There is no single best one. For general metabolic and cardiovascular tracking, the markers with the strongest evidence and the best measurement properties are the following.

• apolipoprotein B
• lipoprotein(a) once in a lifetime
• HbA1c
• fasting insulin and glucose
• hs-CRP
• creatinine and cystatin C for kidney filtration
• ALT
• a complete blood count

Precision and repetition matter more than adding markers.

Is there a blood panel that tests for everything? No. Even a 7,000-protein affinity panel plus clinical chemistry misses most metabolites, most lipid species, and anything expressed only in tissue. Breadth also raises the multiple-comparison problem: run 3,000 analytes once and dozens will fall outside their reference intervals by chance.

Which blood test is the best biomarker for inflammation? hs-CRP, for availability, standardization, and outcome data, with the caveat that its within-person variation is high and a single value says little. Interleukin-6 and adipokines add complementary information, and the marker set that best describes inflammation differs by the metabolic context being studied.²

How often should I test? For a baseline, three draws separated by one to two weeks under matched conditions. After that, quarterly is enough for most stable analytes, and more frequent sampling is justified when you are deliberately changing something and want to detect a shift larger than the reference change value.

When do I need a clinician? Any out-of-range result that could reflect disease, any trend you plan to act on, and any test with a clinical decision threshold attached. Interpretation of a plasma biomarker against a diagnostic cutoff belongs with a physician who can place it in the context of your history and examination.

Footnotes

1. Najib Aziz, Roger Detels, Joshua J. Quint, et al. Biological variation of immunological blood biomarkers in healthy individuals and quality goals for biomarker tests. BMC Immunology, 2019. https://doi.org/10.1186/s12865-019-0313-0 ↩

2. Robert W. O’Rourke, Geoffrey S. Johnson, Jonathan Q. Purnell, et al. Serum biomarkers of inflammation and adiposity in the LABS cohort: associations with metabolic disease and surgical outcomes. International Journal of Obesity, 2018. https://doi.org/10.1038/s41366-018-0088-z ↩ ↩²

3. Sebastian Palmqvist, Pontus Tideman, Niklas Mattsson-Carlgren, et al. Blood Biomarkers to Detect Alzheimer Disease in Primary Care and Secondary Care. JAMA, 2024. https://doi.org/10.1001/jama.2024.13855 ↩

4. Davide Angioni, J. Delrieu, O. Hansson, et al. Blood Biomarkers from Research Use to Clinical Practice: What Must Be Done? A Report from the EU/US CTAD Task Force. The Journal of Prevention of Alzheimer’s Disease, 2022. https://doi.org/10.14283/jpad.2022.85 ↩

5. Mark Han, Choong Tsek Liew, Hong Wei Zhang, et al. Novel Blood-Based, Five-Gene Biomarker Set for the Detection of Colorectal Cancer. Clinical Cancer Research, 2008. https://doi.org/10.1158/1078-0432.ccr-07-1801 ↩

6. Hao-Teng Chang. Biomarker Discovery using dry-lab technologies and high-throughput Screening. Biomarkers in Medicine, 2016. https://doi.org/10.2217/bmm-2016-0111 ↩

7. Samir M. Hanash, Christina S. Baik, Olli Kallioniemi. Emerging molecular biomarkers—blood-based strategies to detect and monitor cancer. Nature Reviews Clinical Oncology, 2011. https://doi.org/10.1038/nrclinonc.2010.220 ↩

8. Louisa Margit Lobigs, Pierre‐Edouard Sottas, Pitre Collier Bourdon, et al. The use of biomarkers to describe plasma‐, red cell‐, and blood volume from a simple blood test. American Journal of Hematology, 2016. https://doi.org/10.1002/ajh.24577 ↩

9. Joaquin F. Diforti, Thomas Cunningham, Zaira Zegalo, et al. Transforming Renal Diagnosis: Graphene‐Enhanced Lab‐On‐a‐Chip for Multiplexed Kidney Biomarker Detection in Capillary Blood. Advanced Sensor Research, 2024. https://doi.org/10.1002/adsr.202400061 ↩

10. Jessica K. Dennis, Julia M. Sealock, Peter Straub, et al. Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Genome Medicine, 2021. https://doi.org/10.1186/s13073-020-00820-8 ↩