Vitals Knowledge Vault

Glucose Variability Detection Model

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Glucose Variability Detection Model

What This Model Does

Infers Glycemic Variability and Metabolic Flexibility patterns from CGM data in non-diabetic adults for Vitals coaching purposes. This is a metabolic phenotyping tool, not a diagnostic instrument.

Input Requirements

  • CGM data: glucose readings (mg/dL) with timestamps
  • Minimum wear duration: 14 days (7 days minimum, 14 preferred)
  • Minimum 3 days of data for CV/SD calculation
  • Meal event tagging (optional but improves postprandial analysis)

Core Detection Logic

Metric Calculation

def glucose_variability_detection(cgm_readings):
    """
    cgm_readings: list of {'glucose': float, 'timestamp': datetime}
    Returns: dict of computed metrics and flag status
    """
    import statistics
    
    glucose_vals = [r['glucose'] for r in cgm_readings]
    n = len(glucose_vals)
    
    mean_glucose = statistics.mean(glucose_vals)
    sd = statistics.stdev(glucose_vals)
    cv = (sd / mean_glucose) * 100  # Coefficient of Variation
    
    # TIR: time in 70–140 mg/dL
    tir = sum(70 <= g <= 140 for g in glucose_vals) / n * 100
    
    # TAR: time above 140 and 180 mg/dL
    tar_140 = sum(g > 140 for g in glucose_vals) / n * 100
    tar_180 = sum(g > 180 for g in glucose_vals) / n * 100
    
    # TBR: time below 70 mg/dL
    tbr = sum(g < 70 for g in glucose_vals) / n * 100
    
    # MAGE: mean amplitude of glycemic excursions
    # (requires meal-tagging to compute accurately; simplified proxy below)
    mage_approx = sd  # SD is a proxy for MAGE in the absence of meal tagging
    
    return {
        'mean_glucose': round(mean_glucose, 1),
        'sd': round(sd, 1),
        'cv': round(cv, 1),
        'tir': round(tir, 1),
        'tar_140': round(tar_140, 1),
        'tar_180': round(tar_180, 1),
        'tbr': round(tbr, 1),
        'mage_proxy': round(mage_approx, 1)
    }

Metabolic Concern Flags

FlagThresholdAction
CV elevated>20%Pattern coaching — dietary/activity intervention
TIR low<85%Metabolic concern — investigate causes
TAR >180 elevated>5% of timeClinical referral
TBR <70 sustainedAny sustained periodClinical referral — hypoglycemia risk
Fasting >126 mg/dLConsistent across daysClinical referral — possible diabetes
Random >200 mg/dLConsistent across daysClinical referral — possible diabetes

Metabolic Flexibility Detection

Postprandial Recovery (Meal Response)

def postprandial_recovery(cgm_data, meal_timestamps):
    """
    Estimate metabolic flexibility via post-meal glucose recovery.
    
    Recovery time: minutes for glucose to return to ±10% of pre-meal baseline.
    
    Slower recovery → potentially impaired metabolic flexibility / insulin resistance.
    
    Note: PREDICT study (PMID 34241823) shows ~30–50% day-to-day reproducibility
    of individual meal responses. Treat individual meal scores as hypothesis-generating,
    not validated.
    """
    results = []
    for meal_ts in meal_timestamps:
        # Get pre-meal baseline (30 min window before meal)
        pre_meal = [r for r in cgm_data 
                    if meal_ts - 1800 <= r['timestamp'] <= meal_ts - 300]
        # Get post-meal readings (up to 3 hours)
        post_meal = [r for r in cgm_data 
                     if meal_ts <= r['timestamp'] <= meal_ts + 10800]
        
        if not pre_meal or not post_meal:
            continue
        
        baseline = statistics.mean([r['glucose'] for r in pre_meal])
        threshold = baseline * 1.10  # 10% above baseline
        
        recovery_readings = [r for r in post_meal if r['glucose'] <= threshold]
        
        if recovery_readings:
            recovery_time_min = (
                recovery_readings[0]['timestamp'] - meal_ts
            ).total_seconds() / 60
        else:
            recovery_time_min = None  # Did not recover within 3h window
        
        results.append({
            'meal_time': meal_ts,
            'baseline': round(baseline, 1),
            'peak': round(max(r['glucose'] for r in post_meal), 1),
            'recovery_min': round(recovery_time_min, 0) if recovery_time_min else None,
            'flag': recovery_time_min is None or recovery_time_min > 120  # >2h = slow
        })
    
    return results

Evidence-Based Confidence

SignalConfidenceSource
CV >20% as metabolic concernModerateHR ~1.3–1.8 per SD; PMID 36099500
TIR <85% as metabolic concernModerateMetabolic dysfunction markers; PMID 35111000
Individual meal response scoreLowReproducibility 30–50%; PMID 34241823
Postprandial recovery timeLow–ModerateMechanistically plausible; not independently validated

What This Model Cannot Detect

  • Type 2 diabetes diagnosis (requires clinical glucose criteria, not CGM patterns)
  • Specific nutrient responses (meal reproducibility too low)
  • Optimal dietary recommendations from CGM alone
  • Causally that “glucose spikes cause damage” in non-diabetics

Confounds and Limitations

  1. Day-to-day variability: Individual CGM metrics have significant day-to-day variability; do not act on single-day readings
  2. Meal tagging accuracy: Postprandial analysis requires accurate meal timestamps — self-reported meals are error-prone
  3. Reproducibility ceiling: Even identical meals produce different glucose responses 30–50% of the time (PREDICT; PMID 34241823)
  4. No non-diabetic TIR targets: Diabetic TIR targets do not apply; non-diabetic “normal” ranges are not clinically validated
  5. Stress/sleep effects: Acute stress, poor sleep, illness, and medications affect glucose patterns independently of diet

Evidence Boundary Label

⚠️ EVIDENCE BOUNDARY This model applies CGM data to metabolic phenotyping in non-diabetic adults. It does not constitute clinical glucose monitoring, diabetes screening, or medical advice. All coaching must be framed as pattern discovery rather than clinical assessment. Clients with abnormal glucose values must be referred to a licensed healthcare provider for clinical evaluation.

Sources

  • PMID 36099500 — Glycemic variability as independent T2D predictor
  • PMID 35111000 — TIR and metabolic dysfunction association
  • PMID 33410452 — Glycemic variability and cardiovascular risk
  • PMID 34241823 — PREDICT meal response reproducibility study

Last updated: 2026-04-21 (BATCH74 vault conversion)

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