Seeing the Connections
Medicine Has Missed
For decades, medical research has advanced by specialization—studying the heart, the immune system, the brain in isolation. But patients aren't collections of separate organs. They are integrated systems.
Cross-System Medicine asks: What patterns emerge when we analyze biological systems together rather than apart?
Our Mission
"Building on the foundational work of thousands of researchers, we apply cross-system analysis to identify patterns that predict clinical outcomes—enabling earlier detection, better risk stratification, and more targeted intervention."
The Challenge of Variable Expressivity
Medicine faces a fundamental puzzle: identical genetic changes produce dramatically different outcomes.
A patient with 22q11.2 deletion syndrome shares the same chromosomal deletion with 200,000 others worldwide. Yet outcomes range from minimal impact to severe cardiac defects, profound immune deficiency, and schizophrenia in 25-30% of cases.
Current single-system approaches explain only a fraction of this variance.
A Cross-System Perspective
Literature Synthesis
Systematically integrating findings across research domains—cardiology, immunology, neurology, psychiatry—to identify connections invisible to single-field analysis.
Pattern Recognition
Computational analysis of multi-system data to identify statistical regularities that correlate with clinical outcomes.
Hypothesis Generation
Pre-registered, testable predictions that can be validated or falsified through prospective studies in existing patient cohorts.
Clinical Translation
Evidence-based screening protocols and clinical guidelines that clinicians can implement today.
Built on Decades of Research
Cross-System Medicine emerges from established scientific traditions:
Systems Biology
Understanding organisms as integrated networks rather than collections of parts.
Kitano, Science, 2002; Hood et al., Science, 2004
Network Medicine
Mapping disease modules that span multiple pathways and organs.
Barabási et al., Nat Rev Genet, 2011
Psychoneuroimmunology
Documenting the bidirectional communication between brain and immune system.
Ader et al., 1995; Irwin & Cole, 2011
Multi-System Biomarkers
Emerging recognition that cross-organ signatures outperform single-system markers.
Furman et al., Nat Med, 2019
Research Programs
We apply cross-system analysis across multiple conditions, with 22q11.2 deletion syndrome as our flagship test case.
22q11.2 Deletion Syndrome
The most common chromosomal microdeletion syndrome offers a unique window into cross-system interactions. Same genetic deletion, dramatically different outcomes—the perfect test case for our approach.
- TLR9 convergence model for autoimmunity
- Brain-immune axis framework for psychosis prediction
- Novel IBD susceptibility hypothesis
- Evidence-based screening protocols
Autoimmune Conditions
Cross-system triggers and shared pathways
Neurodegeneration
Immune markers preceding cognitive decline
Mental Health
The inflammation-depression-anxiety axis
Cardiovascular Disease
Immune components of heart disease
Cancer
Cross-system patterns in oncology
Chronic Pain
Neuroinflammation and central sensitization
Global Health Applications
The same cross-system perspective that reveals patterns within the body also reveals patterns across global crises.
Key insight: Intervention at upstream positions produces multiplied downstream benefits. Cross-system analysis identifies optimal intervention points.
Research Hypotheses
Pre-registered, testable hypotheses grounded in peer-reviewed literature synthesis.
TLR9 Pathway Convergence
A mechanistic framework explaining elevated autoimmune risk in 22q11.2DS
Brain-Immune Axis in Psychosis
Framework for prevention and early intervention
Cross-System Patterns
Unified framework connecting manifestations across conditions
Join the Research
Cross-system analysis requires cross-disciplinary collaboration. We seek partners with:
Patient Data Access
Multi-system longitudinal cohorts for hypothesis testing
Clinical Expertise
Domain specialists to guide interpretation and implementation
Validation Cohorts
Independent datasets for replication studies
Our Commitment
Pre-Registration
All hypotheses specified before data analysis
Falsifiability
Clear success/failure criteria for every prediction
Transparency
Methods, data, and results openly available
Humility
We present correlations, not certainties
Also from 22b1