Biomarker Signatures and the Future of Aging: Why Patterns Matter More Than Points

What if your lab results could tell the story of how you're aging—not just whether you're “normal”?

In traditional medicine, we’re trained to look for abnormalities. Is your CRP too high? Is your IGF-1 too low? But more and more research is showing that one lab result rarely tells the full story. It’s the pattern—the constellation of biomarker shifts across systems—that reveals whether your body is thriving, adapting, or heading toward breakdown.

A pivotal 2017 study by Paola Sebastiani and colleagues, published in Aging Cell, captures this beautifully. The authors analyzed 19 blood biomarkers in over 4,700 participants aged 30 to 110 years old, not to see who had high or low levels—but to uncover distinct biological aging patterns, or what they called biomarker signatures.

🧪 Biomarker Categories Used (n = 19)

The study grouped these biomarkers into five key domains reflecting core physiological systems:

• Inflammation: hsCRP, IL-6, WBC, NT-proBNP, monocytes

• Hematological: RDW, MCV, hemoglobin, transferrin receptor

• Metabolic/Diabetes: HbA1c, sRAGE, adiponectin, IGF-1

• Endocrine: SHBG, DHEA

• Renal: Albumin, creatinine, cystatin C

Some of these markers tend to increase with age (↑), others decline (↓), but the real insight was in how combinations of changes—not individual values—signaled different trajectories of aging and health.

✳️ Notable Signature Examples

Out of 26 distinct biomarker signatures discovered, three stand out:

• Signature 1 (Referent / Average Aging): Biomarkers matched expected values for age and sex. Neither favorable nor concerning—just average.

• Signature 2 (Healthy Aging): Characterized by lower inflammation, better kidney function, and preserved cognitive and physical performance. People in this group lived longer and had less chronic disease. This signature may represent a resilient biological phenotype.

• Signatures 3, 5, 6, 14, etc. (Unfavorable): These patterns combined low IGF-1, high CRP or HbA1c, and signs of renal or hormonal dysregulation. Individuals in these clusters had higher risks of morbidity, frailty, and mortality over the 8-year follow-up.

🧠 ERM and the Power of Patterns

This brings us to Exposure-Related Malnutrition (ERM)—a systems-based model of metabolic stress, undernutrition, and chronic adaptation. ERM isn’t just about weight loss. It’s about persistent trade-offs—inflammation that burns through nutrients, endocrine shifts that sacrifice repair for survival, and metabolic slowdown that prioritizes short-term survival over long-term vitality.

Sebastiani’s findings provide robust scientific support for the ERM idea that maladaptation and early-stage decline show up as biomarker patterns—not isolated values.

Rather than waiting for disease to strike or sarcopenia to appear, we can begin identifying subclinical stages of decline by interpreting biomarker patterns through a systems lens.

🔎 Mapping Sebastiani’s Signatures to ERM Stages

To bring these insights together, we propose the following framework for mapping Sebastiani’s clusters onto stages of ERM:

ERM Stage	Biomarker Signature Characteristics (Example)	Sebastiani Cluster
Stage 0 (Resilient)	Low inflammation, preserved IGF-1, good renal/endocrine balance	Signature 2
Stage 1 (Early adaptation)	Slight rise in inflammation, mild dysregulation in SHBG or albumin	Cluster 1–3
Stage 2 (Compensated malnutrition)	Elevated CRP, HbA1c, SHBG, lower DHEA/IGF-1, poor grip/FEV1	Clusters 5, 6
Stage 3 (Decompensation)	High inflammation, elevated RDW, poor kidney function, cognitive decline	Cluster 14, 15, 17

This model allows clinicians and researchers to track the energy cost of adaptation—a core theme in both aging and ERM—before overt disease manifests.

💡 Why This Matters

If we keep focusing on thresholds and single-point "abnormal" values, we’ll keep missing the people who are biologically declining while still looking “normal.” These are the patients stuck in Stage 1 or 2 ERM—compensating, but quietly losing ground.

With a pattern-based approach, informed by studies like Sebastiani’s, we can detect these trajectories earlier, understand the body's adaptive trade-offs, and design interventions that restore resilience before breakdown occurs.

🔮 The Future of Diagnostics

This study is a reminder that we don’t need more tests—we need better interpretation. That means:

• Looking at systems, not silos

• Prioritizing pattern recognition over point values

• Recognizing that resilience is measurable—and reversible

Because the future of aging and chronic disease prevention lies in seeing the signal before the symptoms.

If you're interested in applying these insights to clinical staging, public health screening, or recovery programs, feel free to reach out or explore our ongoing work on the ERM framework. Let’s move beyond the lab report—and into the pattern.

Sebastiani, P., Thyagarajan, B., Sun, F., Schupf, N., Newman, A. B., Montano, M., & Perls, T. T. (2017). Biomarker signatures of aging. Aging Cell, 16(2), 329–338. https://doi.org/10.1111/acel.12557

#Biomarker Signatures, #Biological Aging, #Exposure-Related Malnutrition (ERM), #Pattern Recognition, #Systems Biology