Biomarkers

Overview

Biomarkers of aging are measurable indicators that predict biological age, healthspan, and lifespan more accurately than chronological age. These markers enable assessment of aging rate, evaluation of interventions, and prediction of age-related disease risk.

Related: [[Biological Age Assessment]] | [[Longevity Science Overview]]

[!note] 2025 Developments Two major 2025 publications established new frameworks for aging biomarkers:

  • The Lancet Healthy Longevity: Digital biomarkers across 10 physiological systems

  • Nature npj Aging: Functional parameters vs. molecular biomarkers

  • Expert Consensus Statement (Gerontology Series A): Standardized biomarkers for intervention studies

Categories of Biomarkers

1. Molecular Biomarkers

  • Epigenetic markers (DNA methylation)

  • Telomere length

  • Proteomics signatures

  • Metabolomics profiles

  • Glycomics patterns

  • Gene expression changes

2. Cellular Biomarkers

  • Senescent cell burden

  • Stem cell function

  • Immune cell profiles

  • Mitochondrial function

  • Cellular energy metabolism

3. Physiological Biomarkers

  • Blood chemistry panels

  • Inflammatory markers

  • Hormonal profiles

  • Organ function tests

  • Body composition

4. Functional Biomarkers

  • Physical performance tests

  • Cognitive assessments

  • Sensory function

  • Muscle strength and mass

  • Cardiovascular fitness

5. Digital Biomarkers (2025)

  • Wearable device metrics

  • Continuous monitoring data

  • Behavioral patterns

  • Sleep architecture

  • Activity levels

Epigenetic Biomarkers

DNA Methylation Clocks

[!tip] Gold Standard DNA methylation-based clocks represent the most accurate biological age predictors, with some achieving correlations of r > 0.95 with chronological age.

First Generation Clocks

Horvath Clock (2013)

  • Accuracy: r = 0.97 with chronological age, median error 2.9 years

  • CpG Sites: 353 specific methylation sites

  • Training: ~8,000 samples across 51 tissue types

  • Method: Elastic net regression on Illumina arrays

  • Applicability: Pan-tissue (works across different cell types)

  • Cost: $300-500 per test

  • Sample: Blood or saliva

Hannum Clock (2013)

  • CpG Sites: 71 sites

  • Tissue: Blood-specific

  • Accuracy: Strong correlation with chronological age

  • Applications: Blood-based aging assessment

Second Generation Clocks

DNAm PhenoAge (2018)

  • CpG Sites: 513 methylation sites

  • Accuracy: r = 0.94 with chronological age

  • Superior Predictions:

    • All-cause mortality

    • Cancer incidence

    • Alzheimer's disease risk

    • Overall healthspan

  • Development: Trained on clinical chemistry biomarkers + mortality data

  • Advantage: Captures biological aging better than chronological age

GrimAge (2019)

  • Components: DNA methylation surrogates for:

    • Smoking pack-years

    • 7 plasma proteins (adrenomedullin

    • β2-microglobulin

    • cystatin C

    • etc.)

  • Performance: Strongest predictor of lifespan and healthspan

  • Mortality Prediction (2025 data):

    • GrimAgeEAA: β = +0.476 ± 0.0393 (NHANES)

    • GrimAgeEAA: β = +0.511 ± 0.0775 (HRS)

    • Outperforms telomere length in mortality prediction

  • Clinical Correlations:

    • Cardiovascular disease

    • Cancer

    • Cognitive decline

    • Frailty

Third Generation Clocks

DunedinPACE (Pace of Aging, Computed from the Epigenome)

  • Innovation: Measures rate of aging (not just age estimate)

  • Units: Years of biological aging per calendar year

  • Normal Range: 0.8-1.2 (slower to faster aging)

  • Applications: Intervention tracking, personalized medicine

  • Advantage: Detects aging rate changes over short periods

DunedinPoAm (Pace of Aging, methylation)

  • Training: Dunedin longitudinal birth cohort

  • Measures: Rate of biological decline

  • Validation: Multiple health outcomes over decades

Recent Developments (2025)

EpiAge Test

  • Method: Next-generation sequencing (NGS) based

  • Sample: Saliva or blood

  • Accuracy: Comparable to established clocks

  • Advantage: More accessible than microarray-based tests

Comparative Performance (2025) Based on three US cohorts (NHANES, HRS, HANDLS):

  1. GrimAgeEAA: Strongest mortality predictor

  2. HannumAgeEAA: Second-best performance

  3. DunedinPACE/PoAM: Best for aging rate

  4. Horvath: Pan-tissue accuracy, moderate mortality prediction

  5. Telomere Length: Weaker than epigenetic clocks

Telomere Length

Biology:

  • Protective DNA caps on chromosome ends

  • Shorten with each cell division (50-200 base pairs/year)

  • Critical shortening triggers senescence

Measurement Methods:

  • Quantitative PCR (qPCR) - most common

  • Flow-FISH (flow cytometry)

  • Terminal restriction fragment (TRF) analysis

  • Single telomere length analysis (STELA)

Associations:

  • Cardiovascular disease

  • Cancer risk (complex relationship)

  • Cognitive decline

  • Mortality (weaker than epigenetic clocks)

2025 Research Findings:

  • Inflammaging mediates telomere-health associations

    • Faster telomere attrition → lower grip strength (β = 0.98

    • p = 0.035)

    • Association completely attenuated when adjusted for inflammation (p = 0.86)

    • Suggests inflammation drives telomere attrition

  • Inferior to epigenetic clocks for mortality prediction

  • Best used in combination with other biomarkers

Limitations:

  • High inter-individual variation

  • Tissue-specific differences

  • Influenced by genetics (~80% heritable)

  • Single time-point measurements less informative

Blood-Based Biomarkers

Inflammatory Markers

[!warning] Inflammaging Chronic low-grade inflammation ("inflammaging") is a hallmark of aging and predictor of multiple age-related diseases and mortality.

C-Reactive Protein (CRP)

Type: Acute phase reactant produced by liver

Normal Ranges:

  • Low risk: <1.0 mg/L

  • Moderate risk: 1.0-3.0 mg/L

  • High risk: >3.0 mg/L

Associations:

  • Cardiovascular disease (strongest predictor)

  • All-cause mortality

  • Alzheimer's disease

  • Metabolic syndrome

  • Frailty

Interpretation:

  • Values >10 mg/L suggest acute infection/inflammation

  • Track changes over time, not single values

  • Responds to lifestyle interventions

Interleukin-6 (IL-6)

Normal Range: <5 pg/mL (varies by assay)

Role in Aging:

  • Pro-inflammatory cytokine

  • Increases 2-4 fold from age 20-80

  • Predictor of disability and mortality

Associations:

  • Cardiovascular disease

  • Diabetes

  • Cancer

  • Sarcopenia (muscle loss)

  • Cognitive decline

Tumor Necrosis Factor-Alpha (TNF-α)

Normal Range: <8.1 pg/mL (varies)

Functions:

  • Pro-inflammatory signaling

  • Immune cell activation

  • Cell death regulation

Aging Impact:

  • Elevated in chronic inflammation

  • Muscle wasting

  • Insulin resistance

  • Neurodegenerative diseases

Metabolic Markers

Glucose Homeostasis

Fasting Glucose

  • Optimal: 70-85 mg/dL

  • Normal: <100 mg/dL

  • Prediabetes: 100-125 mg/dL

  • Diabetes: ≥126 mg/dL

Hemoglobin A1c (HbA1c)

  • Optimal: <5.4%

  • Normal: <5.7%

  • Prediabetes: 5.7-6.4%

  • Diabetes: ≥6.5%

  • Superior: 3-month glucose average, predicts complications

Fasting Insulin

  • Optimal: <5 μIU/mL

  • Elevated: >10 μIU/mL

  • Indicates insulin resistance before glucose rises

HOMA-IR (Insulin Resistance)

  • Formula: (Fasting Glucose × Fasting Insulin) / 405

  • Optimal: <1.0

  • Insulin resistant: >2.5

Lipid Panel

Total Cholesterol

  • Optimal: <200 mg/dL

  • Note: Less important than particle size/number

LDL Cholesterol

  • Optimal: <100 mg/dL (lower for high-risk)

  • Better: ApoB or LDL particle number

HDL Cholesterol

  • Optimal: >60 mg/dL

  • Risk factor if <40 mg/dL (men) or <50 mg/dL (women)

Triglycerides

  • Optimal: <100 mg/dL

  • Elevated: >150 mg/dL

  • Strong predictor of metabolic health

ApoB (Apolipoprotein B)

  • Better predictor than LDL-C

  • Optimal: <90 mg/dL

  • Counts all atherogenic particles

Kidney Function

Creatinine

Normal Ranges:

  • Men: 0.7-1.3 mg/dL

  • Women: 0.6-1.1 mg/dL

Aging Impact:

  • Muscle mass affects levels

  • May appear normal despite reduced kidney function

  • Use in eGFR calculation

Blood Urea Nitrogen (BUN)

Normal Range: 7-20 mg/dL

Elevated BUN:

  • Kidney dysfunction

  • Dehydration

  • High protein diet

  • Catabolic states

Cystatin C

[!tip] Superior Marker Cystatin C is a stronger predictor of mortality risk than creatinine-based eGFR.

Advantages:

  • Not affected by muscle mass

  • Earlier detection of kidney decline

  • Better predictor of cardiovascular events and mortality

Normal Range: 0.6-1.0 mg/L

Use in Biological Age:

  • Included in enhanced PhenoAge calculations

  • Strongest single predictor in some studies

eGFR (Estimated Glomerular Filtration Rate)

Calculation: Uses creatinine, age, sex, race

Normal Range: >90 mL/min/1.73m²

Stages of Kidney Disease:

  • Stage 1: ≥90 (normal)

  • Stage 2: 60-89 (mild decrease)

  • Stage 3a: 45-59 (mild-moderate)

  • Stage 3b: 30-44 (moderate-severe)

  • Stage 4: 15-29 (severe)

  • Stage 5: <15 (kidney failure)

Liver Function

Albumin

Normal Range: 3.5-5.5 g/dL

Functions:

  • Protein nutritional status

  • Liver synthetic function

  • Oncotic pressure maintenance

Low Albumin:

  • Malnutrition

  • Liver disease

  • Kidney disease

  • Inflammation

  • Mortality predictor

Alkaline Phosphatase (ALP)

Normal Range: 30-120 U/L (varies by age)

Sources:

  • Liver

  • Bone

  • Intestine

  • Placenta (pregnancy)

Elevated ALP:

  • Bone disorders (Paget's disease, fractures)

  • Liver disease (cholestasis)

  • Associated with increased mortality in elderly

Alanine Aminotransferase (ALT)

Normal Range: 7-56 U/L

Indication:

  • Liver cell damage/inflammation

  • Non-alcoholic fatty liver disease (NAFLD)

  • Metabolic health

Optimal: Lower end of normal range (<30 U/L)

Complete Blood Count (CBC)

White Blood Cell Count (WBC)

Normal Range: 4,000-11,000 cells/μL

Components:

  • Neutrophils, lymphocytes, monocytes, eosinophils, basophils

Aging:

  • Chronic elevation associated with inflammation

  • Immunosenescence affects distribution

  • High-normal WBC predicts mortality

Red Blood Cell Indices

Mean Cell Volume (MCV)

  • Normal: 80-100 fL

  • Low (microcytic): Iron deficiency, thalassemia

  • High (macrocytic): B12/folate deficiency, alcohol, liver disease

  • Included in PhenoAge calculation

Red Cell Distribution Width (RDW)

  • Normal: 11.5-14.5%

  • Measures variation in RBC size

  • Strong mortality predictor

  • Elevated in inflammation, nutritional deficiencies, bone marrow disorders

  • Included in PhenoAge calculation

Hemoglobin

  • Men: 13.5-17.5 g/dL

  • Women: 12.0-15.5 g/dL

  • Anemia associated with frailty and mortality

Novel Blood Biomarkers

Glycomics

IgG Glycosylation Patterns

  • Predict immune aging and inflammation

  • Glycan age correlates with biological aging

  • Responds to diet and exercise interventions

  • Emerging biomarker class (2025)

Applications:

  • Immune system aging

  • Inflammatory status

  • Intervention response

Proteomics

Plasma Protein Signatures

  • Hundreds of proteins change with age

  • SomaLogic platform: ~1,300 proteins

  • Olink platform: ~3,000 proteins

  • Used in GrimAge development

Key Proteins:

  • Growth differentiation factor 15 (GDF-15): stress, mitochondrial dysfunction

  • Beta-2 microglobulin: kidney function, immune activation

  • Cystatin C: kidney function

  • Adrenomedullin: vascular function

Metabolomics

Small Molecule Profiles

  • Amino acids, lipids, organic acids

  • Metabolic health assessment

  • Mitochondrial function indicators

Functional Biomarkers

[!note] Clinical Significance 2025 research emphasizes that functional biomarkers with excellent mortality correlation and extensive clinical data should not be overlooked in favor of molecular markers.

Physical Performance

Grip Strength

Measurement: Hand dynamometer (kg)

Norms (approximate):

  • Men 40-49: 40-50 kg

  • Women 40-49: 25-30 kg

  • Declines ~1% per year after 50

Significance:

  • Strong predictor of all-cause mortality

  • Cardiovascular disease

  • Disability

  • Hospitalization

  • Cognitive decline

2025 Finding:

  • Association with telomere length mediated by inflammation

  • Directly reflects systemic biological aging

Walking Speed

Measurement: Time to walk 4-6 meters at usual pace

Threshold: <0.8 m/s indicates high mortality risk

Significance:

  • Predicts survival in elderly

  • Cardiovascular fitness

  • Neurological function

  • Overall vitality

Easy to measure: Accessible in any clinical setting

Chair Stand Test

Method: Time to stand from chair 5 times without arms

Norms:

  • <11 seconds: excellent

  • 15 seconds: increased risk

Measures:

  • Lower body strength

  • Fall risk

  • Functional independence

Standing Balance

Tests:

  • Side-by-side stand

  • Semi-tandem stand

  • Full tandem stand

Duration: Hold for 10 seconds each

Predicts:

  • Fall risk

  • Neurological health

  • Mobility decline

Cardiovascular Fitness

VO2 Max

Definition: Maximum oxygen consumption during exercise

Measurement:

  • Direct: Metabolic cart during exercise test

  • Estimated: Fitness tracker algorithms

Significance:

  • Strongest predictor of cardiovascular mortality

  • Declines ~10% per decade

  • Modifiable through exercise

Elite vs. Poor:

  • 50-year-old with VO2max of 30-year-old: Biological age advantage

Resting Heart Rate

Optimal: 50-70 bpm (lower in athletes)

Associations:

  • Cardiovascular health

  • Autonomic nervous system function

  • Metabolic health

  • Mortality risk (elevated RHR)

Heart Rate Variability (HRV)

Measurement: Variation in time between heartbeats

Significance:

  • Autonomic nervous system balance

  • Stress resilience

  • Recovery capacity

  • Decreases with age

Measurement:

  • RMSSD (root mean square of successive differences)

  • SDNN (standard deviation of NN intervals)

  • Wearable devices now provide

Body Composition

Body Mass Index (BMI)

Calculation: weight (kg) / height (m)²

Categories:

  • Underweight: <18.5

  • Normal: 18.5-24.9

  • Overweight: 25-29.9

  • Obese: ≥30

Limitations:

  • Doesn't distinguish muscle vs. fat

  • Use with waist circumference

Waist Circumference

High Risk:

  • Men: >102 cm (40 inches)

  • Women: >88 cm (35 inches)

Indicator:

  • Visceral fat (metabolically active)

  • Metabolic syndrome

  • Cardiovascular risk

  • All-cause mortality

Waist-to-Height Ratio

Calculation: Waist circumference / Height

Target: <0.5

Advantage: Accounts for height variation

Muscle Mass

Measurement:

  • DEXA scan (gold standard)

  • Bioelectrical impedance (BIA)

  • Anthropometry

Sarcopenia:

  • Age-related muscle loss

  • Begins in 30s-40s

  • Accelerates after 60

  • Associated with frailty, falls, mortality

Digital Biomarkers (2025)

Wearable-Derived Metrics

[!tip] Continuous Monitoring Digital biomarkers enable continuous, real-world assessment across 10 physiological systems (Lancet Healthy Longevity, 2025).

Sleep Metrics

Key Parameters:

  • Total sleep time

  • Sleep efficiency

  • REM and deep sleep percentage

  • Sleep fragmentation

  • Sleep-wake timing

Aging Associations:

  • Sleep quality declines with age

  • Reduced deep sleep

  • Increased fragmentation

  • Cardiovascular and cognitive health

Activity Patterns

Metrics:

  • Step count

  • Active minutes

  • Sedentary time

  • Activity intensity distribution

Significance:

  • Physical activity level

  • Metabolic health

  • Cardiovascular fitness

  • Mortality predictor

Continuous Glucose Monitoring (CGM)

Advanced Metrics:

  • Time in range (70-140 mg/dL)

  • Glucose variability

  • Post-prandial responses

  • Overnight glucose

Applications:

  • Metabolic health assessment

  • Dietary intervention testing

  • Diabetes prevention

Organ-Specific Biomarkers

Cardiovascular System

Blood Pressure

  • Systolic BP: optimal <120 mmHg

  • Diastolic BP: optimal <80 mmHg

  • Most frequently used in biological age models

Pulse Wave Velocity (PWV)

  • Arterial stiffness measure

  • Gold standard: carotid-femoral PWV

  • Increases with age

  • Predicts cardiovascular events

Coronary Artery Calcium (CAC) Score

  • CT scan measurement

  • Quantifies atherosclerosis

  • Strong predictor of cardiac events

  • Zero score: very low risk

NT-proBNP

  • Heart failure biomarker

  • Increases with cardiac stress

  • Predicts cardiovascular events

Brain and Cognition

Cognitive Testing

  • Memory (verbal, visual)

  • Processing speed

  • Executive function

  • Attention

Brain Imaging

  • MRI volumetrics (hippocampal volume)

  • White matter hyperintensities

  • Amyloid PET (Alzheimer's)

  • Tau PET

Biomarkers:

  • Plasma p-tau217 (Alzheimer's prediction)

  • Neurofilament light chain (NfL): neurodegeneration

  • BDNF: neuroplasticity

Bone Health

Bone Mineral Density (BMD)

  • DEXA scan

  • T-score: <-2.5 = osteoporosis

  • Fracture risk prediction

Biochemical Markers:

  • Vitamin D (25-OH)

  • Parathyroid hormone (PTH)

  • Bone-specific alkaline phosphatase

Lung Function

FEV1 (Forced Expiratory Volume)

  • Volume exhaled in 1 second

  • Declines with age

  • Predicts mortality

  • Reduced in COPD, asthma

FVC (Forced Vital Capacity)

  • Total exhaled volume

  • FEV1/FVC ratio diagnostic

Composite Biological Age Algorithms

PhenoAge

Components:

  1. Chronological age

  2. Albumin (↓ worse)

  3. Creatinine (↑ worse)

  4. Glucose (↑ worse)

  5. CRP (↑ worse)

  6. Lymphocyte % (↓ worse)

  7. Mean cell volume (↑ worse)

  8. RDW (↑ worse)

  9. ALP (↑ worse)

  10. WBC (↑ worse)

Calculation: Publicly available formula

Interpretation:

  • PhenoAge < Chronological Age: biologically younger

  • PhenoAge > Chronological Age: biologically older

  • Each year difference correlates with mortality risk

Klemera-Doubal Method (KDM)

Approach: Statistical method combining biomarkers

Advantages:

  • Most validated in systematic reviews

  • Best mortality predictor

  • Accounts for biomarker variability

  • Weighted combination

Biomarkers Used: Flexible (typically 8-15 biomarkers)

Status: Recommended by 2025 systematic review of 56 studies

AI/ML-Based Models

Recent Developments (2025):

  • 27 clinical factors

  • Multiple algorithm comparison

  • Elastic-Net Cox: C-Index = 0.778

  • Outperforms traditional PhenoAge (C-Index = 0.750)

Training Data:

  • UK Biobank: 306,116 participants, 60 biomarkers

  • Long-term follow-up

  • Mortality outcomes

Algorithms:

  • Elastic Net (best performance)

  • Random Forest

  • Gradient Boosting

  • Support Vector Machine

  • Ridge regression

  • LASSO

Biomarker Validation Criteria

Essential Characteristics

  1. Reproducibility: Consistent results across labs and time

  2. Predictive Value: Correlates with aging outcomes

  3. Sensitivity: Detects changes in aging rate

  4. Specificity: Distinguishes biological from chronological aging

  5. Practicality: Cost-effective, accessible, minimally invasive

Evidence Levels

Tier 1: Validated

  • Epigenetic clocks (Horvath, GrimAge, PhenoAge)

  • Functional tests (grip strength, walking speed)

  • Standard blood panels with algorithms (PhenoAge, KDM)

Tier 2: Promising

  • Telomere length (in combination)

  • Proteomics signatures

  • Wearable-derived metrics

  • Organ-specific imaging

Tier 3: Emerging

  • Glycomics

  • Metabolomics

  • Microbiome markers

  • Senescent cell burden (in development)

Practical Implementation

For Clinical Use

Minimum Assessment:

  • Standard blood panel (CBC, CMP, CRP)

  • Blood pressure

  • BMI and waist circumference

  • Grip strength

  • Calculate: PhenoAge

Enhanced Assessment:

  • Add: HbA1c, lipid panel, cystatin C

  • Functional tests (walking speed, chair stand)

  • Calculate: KDM or AI-based model

Advanced Assessment:

  • Epigenetic clock (saliva/blood sample)

  • Wearable data integration

  • Organ-specific imaging

  • Comprehensive: Multi-modal biological age

For Research

Longitudinal Studies:

  • Multiple time points (baseline, 6mo, 1yr, 2yr)

  • Track intervention effects

  • Population-specific validation

Intervention Trials:

  • Primary endpoint: biological age change

  • Secondary: individual biomarker changes

  • Quality of life and functional outcomes

Limitations and Considerations

Individual Variation

  • Genetics account for 20-30% of aging rate

  • Lifestyle factors: 70-80% influence

  • Baseline differences affect interpretation

  • Population-specific models needed

Measurement Challenges

Technical:

  • Lab-to-lab variation

  • Timing effects (circadian, seasonal)

  • Fasting vs. non-fasting

  • Medication effects

Biological:

  • Acute illness skews results

  • Hydration status

  • Recent exercise

  • Stress

Interpretation Caution

  • Single time-point limited value

  • Trends more meaningful than absolute values

  • Context matters (acute vs. chronic changes)

  • Not all biomarkers respond equally to interventions

Future Directions

Emerging Biomarker Classes

  1. Single-cell technologies: Cell-specific aging signatures

  2. Spatial transcriptomics: Tissue-level aging patterns

  3. Circulating cell-free DNA: Non-invasive tissue assessment

  4. Exosome profiling: Intercellular communication

  5. Microbiome markers: Gut-aging axis

Technology Integration

AI/ML Advances:

  • Multi-omic integration

  • Personalized biomarker panels

  • Real-time aging rate calculation

  • Intervention optimization algorithms

Consumer Accessibility:

  • At-home testing kits

  • Smartphone-based assessments

  • Continuous monitoring devices

  • Democratization of longevity medicine

Standardization Efforts

Regulatory:

  • FDA biomarker qualification

  • Clinical trial acceptance

  • Insurance coverage pathways

Scientific:

  • Biomarkers of Aging Consortium

  • International harmonization

  • Reference population databases

  • Intervention response standards

Key Research Resources

2025 Landmark Publications

  1. Digital Biomarkers Review

  2. Functional vs. Molecular

  3. Expert Consensus

  4. Comparative Analysis

Conferences and Organizations

Biomarkers of Aging Conference

  • October 20-21, 2025, Boston

  • Organized by Biomarkers of Aging Consortium

  • Field-defining annual event

Key Organizations:

Online Resources

Glossary

Epigenetic Clock: DNA methylation-based predictor of biological age

Epigenetic Age Acceleration (EAA): Difference between epigenetic and chronological age

Inflammaging: Chronic low-grade inflammation associated with aging

Immunosenescence: Age-related decline in immune system function

Proteostasis: Maintenance of protein homeostasis

Senescence: Irreversible cell cycle arrest with altered secretory phenotype

Biomarker: Measurable indicator of biological state or condition

Healthspan: Period of life spent in good health

C-Index: Concordance index, measures predictive accuracy (0.5 = random, 1.0 = perfect)

Related Documents

  • [[Biological Age Assessment]] - Implementation methods and calculations

  • [[Longevity Science Overview]] - Aging mechanisms and hallmarks

  • [[Longevity Interventions]] - How to improve biomarkers

  • [[Recent Longevity Research 2025]] - Latest scientific findings

Last updated: 2025-12-09 Related tags: #biomarkers #aging #testing #measurement #research

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