About
GlycanAge is a biological age testing service that measures immune system age by analyzing glycans, the sugar structures attached to antibodies in your blood. The company's flagship test uses a simple at-home finger-prick blood sample to assess IgG glycosylation patterns, which reflect chronic inflammation and biological aging. Unlike traditional biomarkers that fluctuate with short-term stress or illness, GlycanAge measures glycans attached to antibodies that circulate for approximately three months, capturing sustained biological changes rather than acute noise.
The test is backed by over 30 years of glycoscience research and more than 350 scientific publications. GlycanAge analyzes samples through a five-step laboratory process: isolating IgG antibodies, releasing attached glycans, applying fluorescent labeling, measuring individual glycan quantities, and calculating a biological age score. The company prioritizes accuracy with multiple tests per sample and an error margin under 1 percent.
GlycanAge serves both direct consumers and healthcare practitioners. For consumers, the company pairs test results with a 40-minute one-on-one consultation with in-house glycan specialists. For practitioners and clinics, GlycanAge provides education and support for interpreting glycan data with their clients. The company is expanding into hospital settings across Europe and the Middle East, with its first pilot site at St Catherine Specialty Hospital in Zagreb.
Longevity Contribution
- Only biological age test that measures chronic inflammation through IgG glycan analysis, a layer of biology that influences immune function and overall health
- Responds to lifestyle interventions like caloric restriction and weight loss, unlike epigenetic clocks that remain static
- Measures sustained biological changes over three-month windows rather than short-term fluctuations, reducing sensitivity to acute stress or minor illness
- Integrates genetic, epigenetic, and environmental aspects of aging with an error margin under 1 percent through multiple tests per sample