Science & Research

Praxiom Health is built on a simple idea: biological age is measurable, modifiable, and shows up early in the mouth, the blood, and daily function—not just in how many birthdays you have had. Our Bio‑Age engine translates cutting‑edge biomarker and aging‑clock research into a single, clinically usable score for patients and clinicians.​

1. Multi‑System View of Biological Age

Modern aging science has moved beyond single markers to composite “clocks” that integrate information across many systems. Large plasma proteomics studies have created organ‑specific and whole‑body aging clocks that outperform traditional risk factors in predicting mortality and disease. Next‑generation DNA methylation clocks do the same at the epigenetic level, capturing cell‑level aging processes across 11 physiological systems from a single blood test.​

Key references (examples):

  • Proteomic aging clocks and organ‑specific aging models (Cell Metabolism 2025, PLoS Medicine 2024, npj Aging 2025).​

  • Systems‑level epigenetic clocks such as Systems Age and DunedinPACE (Nature Aging 2025, Alzheimer’s & Dementia 2024).​

  • Telomere length and epigenetic age acceleration as independent predictors of mortality and stroke risk (Aging Cell 2025, Journal of Stroke & Cerebrovascular Diseases 2026).​

2. Biomarkers That Matter for Longevity

Praxiom’s framework is anchored in biomarker domains repeatedly linked to healthspan, frailty, and mortality.​

  • Inflammation & “inflammageing”
    IL‑6, hs‑CRP, GDF‑15, CXCL9 and composite inflammation scores robustly predict frailty, cognitive decline, and mortality. Systematic reviews confirm IL‑6 as a core inflammaging biomarker in community‑dwelling adults.​

  • Metabolic and glycemic health
    HbA1c alone misses key aging signals; continuous glucose metrics like Time‑in‑Range and glycemic variability correlate with cellular senescence, oxidative stress, and cognitive decline. Metabolomic age scores (e.g., MileAge) further capture aging‑related metabolic patterns beyond single markers.​

  • Proteins and hormones of aging
    GDF‑15, FGF‑21, NAD‑related pathways, IGF‑1, and plasma proteomic indices track systemic stress, metabolic flexibility, and longevity across large cohorts. Human GDF‑15 “knockout” data refine what truly healthy ranges look like for long‑term health.​

  • Telomeres and epigenetic clocks
    Telomere length and advanced DNA‑methylation clocks provide complementary views of biological aging and are consistently associated with mortality and age‑related disease.​

3. Oral and Microbiome Signals

Praxiom adds something traditional longevity platforms miss: an explicit oral and microbiome dimension.​

  • Chronic periodontitis drives systemic inflammation and is linked to neurodegeneration, cognitive decline, and frailty in older adults.​

  • Oral and salivary microbiome diversity changes with age and correlates with systemic cytokines and aging phenotypes, making it a practical early biomarker of biological aging.​

  • Gut microbiome diversity and composition are repeatedly associated with healthy longevity, metabolic health, and inflammaging.​

Recent work in elderly cohorts shows that shifts in oral and gut microbial diversity track with systemic fatty acids, inflammatory markers, and age‑related disease risk.​

4. Function and “Real‑World” Aging

Biological age is not only molecular—it is visible in how people move and function.​

  • Handgrip strength and gait speed are among the most robust predictors of mortality, frailty, heart failure outcomes, and “exceptional ager” phenotypes.​

  • Allostatic load indices and sleep‑inflammation studies show that chronic stress and poor sleep translate into accelerated brain and white‑matter aging.​

Praxiom incorporates these functional and stress‑load concepts as part of a practical, clinic‑friendly view of aging rather than treating them as separate from lab values.​

5. How Praxiom Uses This Science

Praxiom does not simply copy a single academic clock; it integrates these validated domains into a pragmatic scoring model.​

  • We start from high‑grade evidence (systematic reviews, large cohorts, Mendelian randomization) to assign weights to biomarker families—oral inflammation, systemic inflammation, glucose dynamics, proteomics, epigenetics, functional status, and stress load.​

  • The algorithm favors multi‑marker patterns (e.g., IL‑6 + CRP + GDF‑15; HbA1c + CGM variability; periodontal status + pathogen load + oral microbiome diversity) rather than single cut‑offs, reflecting how modern clocks work.​

  • Over time, longitudinal data (how your profile changes) is treated as more important than any single snapshot.​

Praxiom’s Bio‑Age score is therefore best thought of as a translational layer: it brings state‑of‑the‑art aging science into a form that dentists, physicians, and patients can understand and act on visit by visit.

 

PubMed links that inform the Praxiom model: