Kihealth Labs
Laboratory scientist operating ddPCR instrument, plasma sample, beta cell illustration, and clinical researchers reviewing data

Beta Intercept™ Validation

Analytical Performance. Clinical Evidence. Scientific Confidence.

The Beta Intercept™ program is being developed through a comprehensive analytical and clinical validation framework designed to establish the performance, reproducibility, and biological relevance of beta-cell-derived molecular biomarkers.

Analytical ValidationClinical EvidenceReproducibilityBiological Relevance

Validation at a Glance

A Scientifically Rigorous Assay for Measuring Beta-Cell Death

Kihealth's Beta Intercept assay uses methylation-sensitive restriction digestion and multiplex droplet digital PCR to quantify circulating differentially unmethylated insulin cfDNA—a molecular signal associated with pancreatic beta-cell death.

Analytically ValidatedClinical Evidence Expanding
Assay Snapshot
Plasma Sample
cfDNA Isolation
Methylation Analysis
Multiplex ddPCR
Beta-Cell Death Measurement
3INS Sites

Measures INS −233, −135 and +399 to create a multi-site beta-cell cfDNA signal.

0.9999R²

Average linearity across the evaluated primer-and-probe sets.

4.5copies/µL

Established analytical limit of detection.

<20%CV

Inter- and intra-assay precision met predefined acceptance criteria.

85Participants

Included in the initial reference-range and classification study, with 48 in the healthy reference group.

5-DayStability

Measurements were supported for up to five days under the evaluated room-temperature and refrigerated conditions.

Metric Glossary

Linearity (R²)

How proportionally the measured signal follows true concentration across the assay's dynamic range. An R² near 1.0 means the assay is quantitative, not merely qualitative.

Limit of Detection (LoD)

The lowest analyte concentration reliably distinguishable from a blank sample, established here at 4.5 copies/µL by Probit analysis at 95% confidence. Defines the assay's analytical sensitivity.

Coefficient of Variation (CV)

Relative standard deviation of replicate measurements, expressed as a percentage. CV < 20% across intra- and inter-run conditions demonstrates reproducible precision.

AUC (Area Under the Curve)

A receiver-operating-characteristic summary of diagnostic discrimination (0.5 = chance, 1.0 = perfect separation). Reported in the clinical-performance sections below; higher AUC = better classification of disease vs. healthy.

Performance results are derived from Kihealth Validation Report VAL-MOL-002. Initial reference-range and classification findings require confirmation in larger, independent and clinically representative populations.

What We Measure

A Blood-Based Signal of Active Beta-Cell Injury

When pancreatic beta cells are damaged or destroyed, small fragments of their DNA can be released into the bloodstream. Kihealth’s V2 assay measures beta-cell-associated, differentially unmethylated regions of the insulin gene within circulating cell-free DNA, providing a molecular signal associated with beta-cell injury and death.

Unlike measurements that primarily reflect glucose control or remaining insulin function, beta-cell-derived cfDNA is intended to provide information about the underlying biological process of beta-cell loss. This may support research into disease activity, progression and response to interventions designed to preserve beta-cell health.

Blood collection tube containing approximately 500 microliters of separated straw-colored serum above a red cell layer

Specimen

500 µL of serum, collected in a Norgen tube

Each measurement begins with a single blood draw. Serum is separated and a 500 µL aliquot in a Norgen cell-free DNA preservative tube provides the input for cfDNA isolation and the multiplex ddPCR measurement.

  1. Beta-Cell Injury

    Cellular stress, immune-mediated damage or disease progression may contribute to the injury and death of insulin-producing pancreatic beta cells.

  2. cfDNA Release

    As beta cells are damaged or destroyed, fragments of their DNA may be released into circulation as cell-free DNA.

  3. Molecular Measurement

    Kihealth analyzes beta-cell-associated methylation patterns within circulating INS cfDNA to estimate the relative abundance of the unmethylated signal across three genomic sites.

INS gene · chromosome 11p15.5Position relative to TSS (bp)
Scientific diagram of the INS gene showing beta-cell-associated methylation targets at INS −233 and INS −135 in the upstream promoter region and INS +399 in the downstream coding region.Promoter regionCoding regionINS geneTSSINS −233PromoterINS −135PromoterINS +399Coding region
Methylated CpG (filled circle)Unmethylated CpG (open square)Assay target site

Multi-Site Measurement

The assay evaluates three differentially methylated INS regions rather than relying on a single genomic site. The resulting measurement is based on the combined unmethylated INS cfDNA signal across all three targets.

  • INS −233

    Upstream promoter region

    Beta-cell-associated methylation site

  • INS −135

    Upstream promoter region

    Beta-cell-associated methylation site

  • INS +399

    Downstream coding region

    Complementary third measurement site

Assay Design

Normalized Against an Intra-Chromosomal Reference

The assay uses LMO1 as a reference target for normalization. LMO1 is located on chromosome 11, the same chromosome as INS, and was selected to provide a concentration-matched reference for the assay’s multiplex measurement.

LMO1 is used as an analytical reference—not as a marker of beta-cell injury.

What the result represents

The assay reports the average percentage of unmethylated INS cfDNA detected across the three evaluated sites. An increased signal may be consistent with elevated beta-cell turnover or injury and should be interpreted within the assay’s intended use and appropriate clinical or research context.

Circulating unmethylated INS cfDNA is a molecular biomarker associated with beta-cell death. An elevated measurement is not, by itself, a diagnosis of diabetes or another medical condition and should be interpreted alongside clinical history, established laboratory measurements and professional clinical judgment.

See How the Assay Works
Doctors and nurses consulting with patients in a bright, modern clinical setting

Clinical Validation Program

Building the Clinical Evidence for Beta Intercept™

The Beta Intercept clinical program is designed to evaluate how circulating beta-cell-derived INS cfDNA relates to disease stage, progression and treatment response across type 1 diabetes, prediabetes and type 2 diabetes.

Through longitudinal studies and translational research, Kihealth is advancing the evidence needed to move from analytically validated molecular measurement toward defined clinical applications.

Development Pathway

1Completed

Discovery

Biological target identification, biomarker research and assay-concept development.

2Completed

Analytical Validation

Evaluation of accuracy, precision, linearity, analytical sensitivity, specificity, recovery, carryover and specimen stability.

3Current Stage

Clinical Validation

Evaluation of the relationship between the molecular measurement and defined clinical populations, disease states and established clinical measures.

4Current Stage

Clinical Utility

Research to determine whether the test can meaningfully support clinical decisions, patient stratification or longitudinal monitoring.

5Planned

Regulatory Development

Development of the evidence, documentation and quality framework appropriate to the intended use and applicable regulatory pathway.

6Future

Scaled Clinical Deployment

Potential integration into defined clinical, research and therapeutic-development workflows following the required validation and regulatory activities.

Regulatory and commercialization planning may occur in parallel with evidence development. This pathway illustrates overall program maturity rather than a strictly sequential process.

Beta Intercept Programs

Type 1 Diabetes Clinical Validation

Beta Intercept™ T1D

Measuring Beta-Cell Injury Across Type 1 Diabetes

Beta Intercept T1D is designed to evaluate circulating unmethylated INS cfDNA as a molecular marker of beta-cell injury across the development and progression of type 1 diabetes.

Clinical-Research Priorities

  • 01Pediatric and youth type 1 diabetes cohorts
  • 02Disease-onset and progression studies
  • 03Longitudinal beta-cell injury monitoring
  • 04Comparison with autoantibodies and established clinical measures
  • 05Correlation with C-peptide, A1c and other indicators of beta-cell function
  • 06Evaluation within observational and interventional studies
  • 07Treatment-response and beta-cell-preservation research
Prediabetes and Type 2 Diabetes Clinical Validation

Beta Intercept™ T2D

Evaluating Beta-Cell Stress Across Metabolic Disease

Beta Intercept T2D is designed to study how beta-cell-associated INS cfDNA changes across metabolic dysfunction, prediabetes and type 2 diabetes.

Clinical-Research Priorities

  • 01Prediabetes and early metabolic dysfunction
  • 02Type 2 diabetes progression
  • 03Beta-cell stress and loss
  • 04Relationship to insulin resistance and glycemic measures
  • 05Longitudinal metabolic monitoring
  • 06Exploratory monitoring during GLP-1–based therapy
  • 07Treatment-response and disease-modification research

GLP-1 monitoring is an exploratory research application. The assay has not been validated to guide GLP-1 treatment decisions.

Shared Research Framework

A Longitudinal and Translational Evidence Model

Longitudinal Studies

Repeated molecular measurements are used to investigate how the beta-cell cfDNA signal changes over time, across disease stages and in response to clinical or therapeutic interventions.

  • Repeated specimen collection
  • Molecular trend analysis
  • Disease-stage correlation
  • Treatment-response evaluation
  • Relationship to clinical outcomes

Translational Research

Molecular results are evaluated alongside established clinical, laboratory and research measures to understand how beta-cell biology relates to disease progression and patient outcomes.

  • A1c and glucose measures
  • C-peptide and insulin-production measures
  • Autoantibody status where appropriate
  • Metabolic and clinical characteristics
  • Therapeutic exposure
  • Longitudinal clinical outcomes

From Molecular Signal to Clinical Evidence

Together, Beta Intercept T1D and Beta Intercept T2D provide a structured clinical-development framework for evaluating beta-cell-derived cfDNA across autoimmune and metabolic disease. The objective is to establish where this molecular signal can provide meaningful information beyond conventional measurements of glucose control and remaining beta-cell function.

Beta Intercept T1D and Beta Intercept T2D include applications that remain under clinical investigation. Analytical validation does not independently establish clinical validity or clinical utility. Research findings must be confirmed in appropriately designed, independent and representative studies before specific clinical claims or intended uses are established.

The Assay Workflow

From Blood Sample to Molecular Signal

Kihealth’s Beta Intercept assay combines controlled specimen processing, cfDNA quality assessment, methylation-sensitive digestion and multiplex droplet digital PCR to measure beta-cell-associated unmethylated INS cfDNA.

Six controlled steps · One normalized molecular result

Six-step Kihealth assay workflow showing blood collection and plasma preparation, cell-free DNA isolation, quality control, methylation-sensitive digestion, multiplex droplet digital PCR and generation of a normalized percentage of unmethylated insulin cfDNA.

Why the Multi-Step Workflow Matters

Low-abundance cfDNA measurements require disciplined control across specimen preparation, molecular processing and data normalization. Each step is designed to reduce analytical variability and support a reliable measurement of the beta-cell-associated INS cfDNA signal.

Explore the Analytical Performance

This workflow summarizes the analytical method described in Kihealth Validation Report VAL-MOL-002. The resulting molecular measurement should be interpreted within the assay’s intended use and appropriate clinical or research context.

Analytical Validation

Performance Established Across Core Analytical Characteristics

Kihealth’s V2 assay was evaluated across eight core analytical characteristics using predefined study designs, controls and acceptance criteria. The validation program assessed whether the method could reliably, reproducibly and specifically measure differentially unmethylated INS cfDNA under the evaluated laboratory conditions.

Analytical Acceptance Criteria Met
01

Within 20%

Results within the predefined 20% acceptance range

Accuracy

Measurements remained within the predefined 20% acceptance range during the five-day accuracy assessment.

Acceptance Criterion Met
02

CV <20%

Precision

Inter-assay and intra-assay measurements met the predefined coefficient of variation criterion.

Acceptance Criterion Met
03

R² >0.9999

Linearity

Observed copy-number measurements showed a highly linear relationship across the evaluated concentration range.

Acceptance Criterion Met
04

4.5 copies/µL

Limit of Detection

The lowest concentration reported as reliably and repeatedly detectable was 4.5 copies per microliter.

Acceptance Criterion Met
05

Target-Specific

Analytical Specificity

In-silico and experimental studies supported specific measurement of the intended INS methylation targets.

Acceptance Criterion Met
06

65.73%

4.52% CV

Mean recovery of input DNA during extraction

Extraction Recovery

The cfDNA isolation workflow demonstrated consistent recovery across the evaluated specimens.

Acceptance Criterion Met
07

<1%

Positive droplets in no-template control wells

Carryover

Representative testing demonstrated minimal detectable carryover between high-concentration samples and no-template controls.

Acceptance Criterion Met
08

5 Days

Supported at room temperature and 2–8°C

Specimen Stability

Plasma cfDNA measurements were supported through five days under the evaluated room-temperature and refrigerated conditions.

D0D3D5D7SUPPORTED WINDOWVARIABILITY BOUNDARY
Acceptance Criterion Met

Validation Across the Full Analytical Workflow

The validation program evaluated performance from specimen processing and DNA recovery through target detection, multiplex measurement, normalization and result generation. Together, these studies support the reliability of the V2 assay under its defined analytical conditions.

Reproducible MeasurementLow-Copy DetectionControlled Sample Processing
Review the Initial Reference-Range Study

Analytical performance results are derived from Kihealth Validation Report VAL-MOL-002 and reflect the materials, study designs and laboratory conditions evaluated in that report. Analytical validation establishes the performance of the measurement method; it does not independently establish clinical utility, regulatory approval or performance in every intended-use population.

Analytical Specificity

Designed to Identify Beta-Cell-Associated INS cfDNA

Kihealth’s Beta Intercept assay combines target-specific primer-and-probe design with methylation-sensitive restriction digestion to measure three differentially methylated regions of the INS gene. In-silico sequence analysis and experimental control studies supported the assay’s analytical specificity under the evaluated conditions.

IN-SILICO SEQUENCE ANALYSIS + EXPERIMENTAL VERIFICATION

Scientific diagram showing the three INS methylation targets at −233, −135 and +399 on chromosome 11, selective HpaII digestion of unmethylated INS DNA, preservation of methylated INS DNA and normalization using the LMO1 reference target.

01

Targeted Genomic Regions

The assay measures three beta-cell-associated regions of the INS gene and uses LMO1 as an intra-chromosomal reference target.

CHROMOSOME 11 — SCHEMATIC, NOT TO SCALEINSPrimary measurement targetLMO1Normalization reference

INS GENE — LINEAR SCHEMATIC

PROMOTER REGIONCODING REGIONTSSTranscription start siteINS −233Upstream promoterINS −135Upstream promoterINS +399Downstream codingupstream (−)downstream (+)
SITEPOSITION RELATIVE TO TSSROLE
INS −233−233 (upstream promoter)Measurement target
INS −135−135 (upstream promoter)Measurement target
INS +399+399 (downstream coding)Measurement target
LMO1Separate chromosome 11 locusAnalytical reference

The multi-site design evaluates three differentially methylated INS regions rather than relying on a single genomic target. LMO1 is used as an analytical reference target and is not interpreted as a marker of beta-cell injury.

WHAT THE SPECIFICITY STUDIES SHOWED

Target-Specific Sequence Design

RESULT

In-silico sequence analysis identified no significant alternative binding sites outside the intended chromosome 11 regions under the validation report’s predefined criteria.

WHY IT MATTERS

This supports the designed primer-and-probe sets binding to the intended genomic regions.

Defined Restriction-Site Architecture

RESULT

Sequence analysis confirmed one relevant HpaII restriction site within each evaluated INS amplicon and no corresponding HpaII site within the LMO1 reference amplicon.

WHY IT MATTERS

This supports selective methylation-sensitive digestion of the three INS measurement targets while preserving the reference target.

Experimental Methylation Discrimination

RESULT

Experimental control studies demonstrated digestion of the evaluated unmethylated genomic DNA, with no detectable digestion of the corresponding methylated control.

WHY IT MATTERS

This supports the assay’s ability to distinguish the evaluated unmethylated and methylated INS target states.

Specificity Established Through Design and Experimental Testing

Together, the sequence-alignment analysis, restriction-site evaluation and controlled digestion studies support the analytical specificity of the Kihealth V2 assay for measuring differentially unmethylated INS cfDNA under the conditions evaluated in Validation Report VAL-MOL-002.

Explore the Initial Reference-Range Study

Analytical-specificity findings are based on the in-silico and experimental studies described in Kihealth Validation Report VAL-MOL-002. These results support assay specificity under the evaluated conditions and do not independently establish clinical utility, performance in every biological context or regulatory approval.

Initial Reference-Range Study

Translating Molecular Measurement Into Interpretable Results

Kihealth evaluated the distribution of average unmethylated INS cfDNA within an initial study cohort to develop preliminary reporting bands for the beta-cell-associated molecular signal.

The analysis combined healthy-reference percentiles with cohort-based threshold evaluation to distinguish lower, intermediate and elevated measurements.

Initial Reference-Range Study — Independent Validation Continuing

Preliminary Interpretation Spectrum

Average unmethylated INS cfDNA (%) across three evaluated INS sites.

  • Markers

    15.0%
    Sensitivity-oriented preliminary decision threshold
    16.34%
    Healthy-reference 95th percentile
    18.95%
    Healthy-reference 97.5th percentile
    19.0%
    Conservative elevated-signal threshold

Moderately Elevated

15.0%–18.9%
Interpretation
Elevated relative to the lower-signal band; clinical correlation and appropriate follow-up may be considered.
Scientific Basis
Spans the cohort-derived sensitivity-oriented threshold (15.0%) through the healthy reference group's 95th (16.34%) and 97.5th (18.95%) percentiles.
Suggested Report Language
“Moderately elevated relative to the initial reference population.”

Two Complementary Threshold Principles

15% Threshold

Sensitivity-Oriented Decision Point

The 15% threshold was selected using cohort-based ROC analysis as a preliminary decision point intended to identify measurements above the lower-signal reporting band with high sensitivity.

19% Threshold

Reference-Distribution Rule-In Point

The 19% threshold aligns closely with the healthy reference group's observed 97.5th percentile of 18.95%, supporting its use as a more conservative elevated-signal threshold in the initial analysis.

The 15% threshold is a cohort-derived decision threshold, while the 19% threshold is closely anchored to the upper distribution of the initial healthy reference group. They serve different interpretive purposes.

From Continuous Measurement to Defined Reporting Bands

The assay produces a continuous molecular measurement—the average percentage of unmethylated INS cfDNA detected across three evaluated INS sites. The preliminary reporting bands organize that continuous measurement into lower, intermediate and elevated signal categories to support consistent interpretation and future clinical study.

  1. 01Multi-site INS cfDNA measurement
  2. 02Reference-population distribution
  3. 03Preliminary interpretive thresholds

The reference-range and decision-threshold findings are based on Kihealth's initial study cohort. The thresholds were developed and evaluated within the same analysis and have not yet been confirmed in a separate, independent population. Results require interpretation alongside clinical history, established laboratory measurements and professional clinical judgment. Predictive performance may differ across populations, disease stages and intended uses.

Quality by Design

Controls Built Into Every Run

Quality controls are integrated throughout the Kihealth Beta Intercept™ assay — from specimen assessment and molecular processing through droplet digital PCR and result normalization. These controls help identify contamination, confirm expected assay behavior and support consistent, interpretable measurements.

Specimen QualityAssay ControlsNormalized Measurement

  1. 01 · Specimen level

    cfDNA Quality Assessment

    Quality assessment helps verify that the extracted specimen is appropriate for downstream digestion and ddPCR measurement. The resulting information also supports standardized sample input and identification of specimens that may not meet established laboratory requirements.

    What it monitors
    Input concentration and fragment-size characteristics of the extracted cfDNA.
    What an unexpected result may indicate
    May indicate insufficient input, degraded material or genomic-DNA carryover in the specimen.
    How it supports run review
    Helps reduce variability associated with insufficient input, degraded material or genomic-DNA contamination.

Quality-Control Architecture

Five Coordinated Layers of One Quality System

Quality is evaluated at the specimen, reaction, plate, measurement and result level — not as a final inspection step.

  1. Layer 1

    Specimen Quality

    cfDNA concentration and fragment assessment

  2. Layer 2

    Reaction Quality

    Technical replicate agreement

  3. Layer 3

    Plate Quality

    Positive and no-template control performance

  4. Layer 4

    Measurement Quality

    Target-specific multiplex quantification

  5. Layer 5

    Result Quality

    LMO1-supported normalization and review

Converged Outcome

Controlled, Interpretable Molecular Measurement

Results Are Reviewed Within a Defined Quality Framework

A result is interpreted only after the associated specimen, controls, replicate measurements and reference-target behavior have been evaluated against the laboratory's established quality requirements. Results that do not meet the applicable criteria are reviewed, investigated or repeated according to laboratory procedures.

The quality-control framework summarizes key elements described in Kihealth Validation Report VAL-MOL-002. Specific control ranges, acceptance criteria and laboratory procedures are maintained within Kihealth's controlled quality documentation.

Laboratory scientists reviewing molecular biomarker data on a large monitor beside racks of blood collection tubes

Continuous Validation

Evidence Designed to Grow With the Platform

Validation is not a single endpoint. Kihealth is building a continuously expanding evidence base designed to evaluate assay performance across larger, more diverse and more clinically representative populations.

Evidence-Development Milestones

  1. 01Complete

    Initial Analytical Validation

    Core analytical performance was evaluated across accuracy, precision, linearity, limit of detection, specificity, recovery, carryover and specimen stability.

    • Supporting evidence: Kihealth Validation Report VAL-MOL-002
  2. 02Complete

    85-Participant Evidence Base

    The completed evidence base expanded the initial analytical work through reference-range development and early clinical characterization.

    • Reference-population analysis
    • Preliminary decision thresholds
    • Initial population-level comparisons
    • Clinical-variable correlation
    • Cohort characterization
  3. 03Current

    250 Participants

    The active evidence milestone is expanding clinical representation and testing the stability of early findings across a broader population.

    • Reference-interval reassessment
    • Decision-threshold stability
    • Age and sex subgroup analysis
    • Disease-stage comparisons
    • Additional clinical-variable correlations
    • Assessment of missing, invalid and excluded results
  4. 04Targeted

    500 Participants

    A larger evidence base is targeted to support more detailed population and disease-subgroup analyses.

    • Performance across defined clinical subgroups
    • Pediatric and adult population comparisons
    • T1D and T2D program-specific analyses
    • Longitudinal molecular trends
    • Treatment-exposure stratification
    • Evaluation across participating clinical sites
  5. 05Targeted

    1,000 Participants

    The 1,000-participant milestone is intended to support a more mature assessment of generalizability, population variability and potential intended-use applications.

    • Confirmation of population-specific findings
    • Greater demographic and clinical representation
    • Multisite performance assessment
    • Independent validation analyses
    • Refined reference intervals
    • Refined clinical decision thresholds
    • Evidence supporting future clinical and regulatory development
  6. 06Planned

    Periodic Reassessment

    Following the defined enrollment milestones, Kihealth intends to reassess the evidence at regular intervals and when meaningful new clinical data become available.

    • Reference distributions
    • Decision thresholds
    • Population-specific performance
    • Clinical correlations
    • Longitudinal findings
    • Assay or workflow changes
    • Newly defined intended-use populations

Continuing Validation Priorities

01Planned

Independent Cohort Validation

Evaluate key findings in populations that were not used to develop the original reference intervals, thresholds or clinical hypotheses.

02Ongoing

More Diverse Clinical Populations

Expand representation across age, sex, race, ethnicity, disease stage, metabolic characteristics and relevant treatment exposures.

03Ongoing

Longitudinal Disease Studies

Use repeated molecular measurements to study how beta-cell-associated INS cfDNA changes across disease onset, progression and clinical follow-up.

04Ongoing

Correlation With Established Clinical Measures

Evaluate relationships between INS cfDNA and established measures such as A1c, C-peptide, glucose measurements and islet autoantibodies where clinically appropriate.

05Targeted

Interventional and Treatment-Response Studies

Study whether changes in the molecular signal correspond with therapeutic interventions intended to preserve beta-cell function, reduce beta-cell stress or modify disease progression.

Clinical Evidence Dashboard

Understanding Beta-Cell Biology Through Complementary Biomarkers

Beta Intercept™ is being developed to measure circulating unmethylated INS cfDNA — a molecular signal associated with pancreatic beta-cell injury and death.

The Clinical Evidence Dashboard illustrates how this emerging signal may complement A1c, C-peptide and islet autoantibodies. Each biomarker answers a different clinical question and provides a different view of disease biology.

Clinical research team studying beta-cell biology and molecular biomarkers on data displays in a sunlit modern laboratory

Illustrative clinical-evidence framework

The models and visualizations shown in this dashboard are conceptual. They demonstrate the clinical questions being evaluated through the Beta Intercept clinical-validation program and do not represent completed comparative-performance claims unless specifically identified as study results.

Four biomarkers. Four different questions.

01

Beta Intercept Molecular Signal

What it measures
Circulating differentially unmethylated INS cfDNA associated with beta-cell injury and death.
Clinical question
Is there evidence of active beta-cell injury?
Potential contribution
May provide molecular visibility into beta-cell damage before changes are fully reflected by conventional measures of glucose control or remaining insulin function.
Development status
Analytically validated; clinical validation ongoing.
02

C-Peptide

What it measures
Endogenous insulin secretion and remaining beta-cell function.
Clinical question
How much insulin-producing capacity remains?
Clinical contribution
Helps assess residual beta-cell function and endogenous insulin production.
Key distinction
C-peptide measures remaining function. Beta Intercept is being evaluated as a signal of active cellular injury. The two measurements may provide complementary information.
03

A1c

What it measures
Average glycemic exposure over approximately the preceding two to three months.
Clinical question
How well has blood glucose been controlled over time?
Clinical contribution
Provides an established measure of glycemic exposure and diabetes management.
Key distinction
A1c reflects the downstream effect of glucose dysregulation. It does not directly measure beta-cell injury or the rate of beta-cell loss.
04

Islet Autoantibodies

What they measure
Evidence of an autoimmune response directed against pancreatic islet targets.
Clinical question
Is islet autoimmunity present?
Clinical contribution
Supports the identification, classification and staging of autoimmune type 1 diabetes risk.
Key distinction
Autoantibodies identify autoimmune activity and risk context. They do not directly quantify the amount or rate of active beta-cell death.

Complementary biological intelligence

No single biomarker provides a complete picture of beta-cell biology.

Beta Intercept is being studied alongside established biomarkers to determine whether their combined interpretation can provide a more complete view of:

  • Autoimmune risk
  • Active beta-cell injury
  • Remaining beta-cell function
  • Glycemic exposure
  • Disease progression
  • Treatment response

Beta Intercept™ T1D

From Autoimmunity to Functional Decline

Type 1 diabetes develops through a biological continuum. Islet autoantibodies may appear before symptomatic disease, while C-peptide and A1c provide information about remaining function and glycemic impact.

Beta Intercept T1D is evaluating whether beta-cell-derived INS cfDNA can add a direct molecular signal associated with active beta-cell injury during this progression.

Autoantibodies

Identify autoimmune risk and support disease staging.

Beta Intercept

Being evaluated as a molecular signal associated with active beta-cell injury.

C-Peptide

Measures remaining endogenous insulin-producing function.

A1c

Reflects the downstream effect of glycemic dysregulation.

Complementary Signals Across Type 1 Diabetes Progression

Conceptual model — not clinical performance data
Islet autoantibody statusC-peptideBeta Intercept molecular signalA1c
Relative signalAutoimmune RiskEarly DiseaseClinical OnsetEstablished T1D

This conceptual model illustrates how biomarkers may reflect different aspects of T1D biology at different points in disease development. The curves are illustrative and do not represent observed clinical-study data.

Comparative biomarker matrix

Different Measurements. Complementary Clinical Context.

BiomarkerPrimary biological dimensionMeasurement typeRole
Beta InterceptBeta-cell injury and deathMolecular cfDNA signalPotential role under evaluationEarlier biological visibility and longitudinal injury monitoring
C-PeptideRemaining beta-cell functionEndogenous insulin-secretion markerEstablished roleAssessment of insulin-producing capacity
A1cGlycemic exposureGlycated hemoglobinEstablished roleDiagnosis, monitoring and assessment of longer-term glucose control
Islet AutoantibodiesIslet autoimmunityImmune markerEstablished roleT1D risk identification, classification and staging

The clinical-value hypothesis

Seeing Disease Biology From More Than One Perspective

Beta Intercept is not intended to replace A1c, C-peptide, autoantibodies or professional clinical judgment.

The clinical-development hypothesis is that a molecular signal associated with active beta-cell injury may add information that is not fully captured by measurements of autoimmunity, remaining function or glycemic exposure alone.

The Beta Intercept clinical-validation program is evaluating whether this complementary information may support:

01

Earlier Biological Visibility

Identify a molecular signal associated with beta-cell injury before substantial functional loss or persistent glycemic deterioration is evident.

02

Patient Stratification

Help distinguish patients who may have similar conventional clinical measurements but different underlying patterns of beta-cell injury.

03

Longitudinal Monitoring

Evaluate whether repeated INS cfDNA measurements can characterize changes in beta-cell injury over time.

04

Treatment-Response Research

Study whether changes in the molecular signal correspond with therapeutic interventions intended to preserve beta-cell health or modify disease progression.

05

More Complete Clinical Context

Combine information about autoimmunity, active injury, remaining function and glycemic exposure to create a more complete view of disease biology.

The big takeaway

Together, these measurements may provide a more complete picture of disease biology than any single biomarker alone.

Beta Intercept™ is designed to join A1c, C-peptide and islet autoantibodies — each answering a different clinical question and revealing a different layer of beta-cell biology across the full course of diabetes.

Four biomarkers. Four questions.

01
A1c
shows the glycemic impact.
02
C-peptide
shows how much insulin-producing function remains.
03
Autoantibodies
show whether islet autoimmunity is present.
04
Beta Intercept
Shows whether active beta-cell injury is occurring.

Scientific disclaimer

The comparative models and disease-trajectory graphics displayed in this section are conceptual and are provided to explain the clinical-development rationale for Beta Intercept. They do not represent completed head-to-head performance studies or establish clinical superiority, diagnostic accuracy, clinical utility or regulatory approval.

Beta Intercept T1D and Beta Intercept T2D remain under clinical investigation. Any future clinical-performance claims will require appropriately designed studies, prespecified analyses, scientific review and confirmation in representative populations. Results must be interpreted alongside established clinical information and professional clinical judgment.

Explore the Validation Evidence

Review the Science. Examine the Evidence. Advance the Research.

Kihealth is committed to transparent, evidence-based scientific communication. Explore the analytical validation, foundational research and clinical-development programs supporting the Beta Intercept platform.

Certain technical materials are available through controlled access to protect proprietary assay methods, internal quality procedures and confidential development information.

01

Request the Validation Report

Access a detailed summary of the analytical studies supporting Kihealth's Beta Intercept beta-cell cfDNA assay.

The report covers:

  • Accuracy
  • Precision
  • Linearity
  • Limit of detection
  • Analytical specificity
  • Extraction recovery
  • Carryover
  • Specimen stability
  • Initial reference-range development
  • Analytical quality controls
Request the Validation Report
02

View Scientific Publications

Explore the peer-reviewed research and scientific evidence underlying beta-cell-derived cfDNA, DNA methylation analysis and the measurement of pancreatic beta-cell death.

Foundational publications link directly to the publisher, DOI or PubMed record. Kihealth-generated posters, white papers and technical summaries are clearly labeled according to their publication and review status.

View Scientific Publications
03

Discuss a Research Collaboration

Partner with Kihealth to evaluate biomarkers, analyze longitudinal disease biology and develop evidence for the next generation of diagnostics and precision therapeutics.

Potential collaborations include:

  • Retrospective biobank studies
  • Prospective observational studies
  • Interventional-trial biomarker studies
  • Pharmaceutical biomarker programs
  • Academic research collaborations
  • Treatment-response studies
  • Independent clinical-validation studies
Discuss a Research Collaboration

Certain technical and proprietary materials are available through controlled access. Release may require verification of the requestor, confirmation of an appropriate scientific or business purpose, and execution of a confidentiality agreement. Public materials may be requested directly through Scientific Affairs.

Important Scientific and Regulatory Information

The information presented on this page is provided for scientific, educational and informational purposes.

01

Analytical Performance

Analytical-performance results are derived from Kihealth Validation Report VAL-MOL-002 and reflect the materials, study designs, methods and laboratory conditions evaluated in that report. Analytical validation establishes the performance of the measurement method under defined conditions. It does not independently establish clinical validity, clinical utility or performance in every intended-use population.

02

Initial Clinical Findings

Reference ranges, decision thresholds and preliminary clinical-performance estimates are based on the study populations and analyses identified on this page. Unless expressly stated otherwise, these findings have not been confirmed in a separate, independent and clinically representative population. Performance may differ according to population characteristics, disease prevalence, disease stage, specimen handling, clinical setting and intended use. Positive and negative predictive values are influenced by the prevalence of the condition within the evaluated population.

03

Conceptual Models

Certain disease-trajectory graphics, biomarker comparisons and clinical-use frameworks are conceptual. They are intended to explain the scientific rationale and clinical-development strategy for Beta Intercept and do not represent completed comparative-performance studies.

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Clinical Interpretation

An elevated unmethylated INS cfDNA measurement is not, by itself, a diagnosis of type 1 diabetes, type 2 diabetes or another medical condition. Results should be interpreted alongside clinical history, physical findings, established laboratory measurements and professional clinical judgment. Beta Intercept is not intended to replace A1c, C-peptide, islet autoantibodies, glucose testing or other established diagnostic and monitoring methods. The clinical-development program is evaluating whether the molecular signal can provide complementary information about beta-cell injury and death.

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Products Under Development

Certain products, biomarkers, reporting thresholds, clinical applications and intended uses described on this page remain under development or clinical investigation. Some may be available only for research use, through approved studies or within defined laboratory settings. References to planned studies, enrollment milestones, regulatory activities, clinical applications or commercial deployment represent objectives and are not guarantees of completion, approval or commercial availability.

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Regulatory Status

Nothing on this page should be interpreted as a representation that a product, biomarker, clinical claim or intended use has received FDA clearance, FDA approval or authorization from another regulatory authority unless that status is expressly and accurately stated.

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Research and Treatment Decisions

The information presented on this page does not constitute medical advice and should not be used independently to diagnose disease, select treatment, change medication or make other patient-care decisions. Research findings should be interpreted within the applicable protocol, study design, statistical analysis and intended research context.

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Scientific Updates

Kihealth may update its scientific conclusions, reference intervals, decision thresholds and clinical-development priorities as additional evidence becomes available. Material changes should be scientifically reviewed, version controlled and documented before publication or implementation.

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Professional Review

All public validation claims, clinical-performance statements and regulatory descriptions should undergo appropriate review by Kihealth's scientific, laboratory, medical, regulatory and legal advisors before publication.

Questions About the Evidence?

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