InterceptIQ unifies ultra-deep cell-free DNA sequencing, fragmentomic and methylation analysis, and machine-learned tissue-of-origin inference — engineered to detect active disease biology before clinical signal.
Cell-free DNA · Molecular network · AI intelligence — read from a single tube of blood.
Kihealth's proprietary Intercept IQ™ platform was designed to generate biological intelligence across prevention, clinical care, research, and therapeutic innovation.
The Kihealth platform is designed to help identify biological signals associated with metabolic dysfunction before traditional markers become abnormal.
"The future of healthcare begins before symptoms appear."
Providing physicians with deeper biological information to support earlier and more informed care decisions.
Advanced diagnostics can help identify, stratify, and monitor patient populations participating in clinical studies.
As precision medicine advances, diagnostics become increasingly important in identifying patients, supporting clinical development, and evaluating biological response.
"Tomorrow's therapies require tomorrow's diagnostics."
The rapid growth of metabolic therapeutics creates demand for objective biological measurements beyond traditional metrics.
Repeated testing allows healthcare providers and researchers to monitor biological trends over time rather than relying on isolated measurements.
The Intercept IQ™ platform was designed as a scalable molecular diagnostic architecture capable of supporting future disease applications and expanded clinical utility.
Kihealth is building more than a diagnostic test. We are developing a platform designed to generate biological intelligence across prevention, clinical care, therapeutic innovation, research, and population health.
"The future of healthcare will be driven by earlier detection, deeper biological insight, and more informed decision-making. Kihealth intends to help power that future."
Six tightly coupled stages convert routine plasma into a CLIA-grade readout of active disease biology. Scroll to walk the pipeline.
A single standard Norgen tube. No imaging, no biopsy, no specialized collection.
Centrifugation separates plasma from cellular components within minutes of draw, preserving fragile cell-free signal.
Sub-nanogram cfDNA fragments — released by dying cells across every tissue — are captured and prepared for deep sequencing.
30–120× whole-genome bisulfite sequencing resolves methylation, fragmentomic, and end-motif signatures unique to each tissue and disease state.
Multi-task models trained on hundreds of thousands of prospectively collected samples infer tissue-of-origin and active disease biology from 2.4M features per sample.
Physician-facing reports surface tissue-of-origin, disease state, and confidence — backed by analytical and clinical validation, in language clinicians act on.
Disease begins as cellular injury, becomes molecular change, then biomarker shift, then symptom. InterceptIQ™ reads the molecular signal at the moment of cellular injury — long before any clinical test would register a result.
The natural history of Type 1 Diabetes plotted along its molecular and clinical timeline. cfDNA signal rises the moment β-cells begin to die — years before HbA1c moves.
Homeostasis
Autoimmune attack begins
cfDNA signal rises
Autoantibodies appear
Hyperglycemia, polyuria, weight loss
HbA1c, fasting glucose, OGTT
cfDNA β-cell methylation
Years 1–3 before symptoms
Pancreatic islet β-cells maintain insulin secretion. No autoimmune activity, no measurable tissue injury.
T-cell infiltration triggers β-cell apoptosis. Dying cells release fragments of methylated DNA into circulation.
Unmethylated INS gene cfDNA fragments and β-cell-specific methylation marks accumulate in plasma — years before glucose dysregulation.
GAD65, IA-2, and ZnT8 autoantibodies become detectable. β-cell mass loss accelerates past 50%.
Clinical symptoms emerge once functional β-cell reserve is largely exhausted. Patient presents to primary care.
Standard-of-care diagnosis confirms T1D. By this point, >80% of β-cell mass is irreversibly lost.
Illustrative T1D natural history. Lead-time estimates supported by Akirav et al., Herold et al., and ongoing prospective InterceptIQ™ cohorts. Indicative — not for diagnostic use.
The InterceptIQ pipeline is a continuous instrument loop. Every stage is quality-controlled, traceable, and engineered to preserve fragmentomic detail from picogram-level input through clinical reporting.
Proprietary low-input library chemistry preserves fragmentomic detail from <1 ng of cfDNA.
Deep whole-genome bisulfite sequencing at 30×–120×, scaled across hundreds of thousands of samples.
Federated AI infers tissue-of-origin and disease state from 2.4M features per sample.
Five tightly coupled layers — wet lab to clinical report — each instrumented, quality-controlled, and engineered for regulatory and payer review.
Rather than relying on a single biomarker, InterceptIQ™ interrogates multiple tissue-specific methylation loci across the insulin gene. The joint signal raises specificity, lowers false-positive rate, and produces a clinically interpretable disease intelligence output.
Open chromatin in pancreatic β-cells; hypermethylated in non-β tissue.
β-cell specific demethylation; conserved across human islet donors.
Independent confirmatory locus; lowers false-positive rate vs. single-site assays.
Calibrated weighted combination — auditable, interpretable, and reproducible across cohorts.
Our models are trained on prospectively collected, IRB-approved cohorts and audited for confounding, leakage, and demographic generalization. Every classifier ships with an interpretability layer that maps predictions back to biological features — critical for regulatory review and clinician trust.
See peer-reviewed methods →Sample KH-PLT-0421 · synthetic readout for illustration
Tissue-of-origin
Methylation atlas
47 cell types
Fragmentomic readout
cfDNA · bp
Methylation grid · chr11
