Part 11: Heart & Muscle Biochemistry

Cardiac Metabolism

The heart is the most metabolically active organ in the body. Beating roughly 100,000 times per day, it consumes approximately 6 kg of ATP daily — more than 20 times its own weight. Yet the heart stores only enough ATP for about 10 seconds of contraction, making continuous oxidative phosphorylation absolutely essential for survival.

Why fatty acids? Think of cardiomyocytes as diesel engines — they prefer the fuel with the highest energy density. One molecule of palmitate (C16:0) yields ~106 ATP via complete β-oxidation, compared to ~30–32 ATP from one glucose molecule. Since the heart cannot rest, it needs the most efficient fuel available.

Glucose Switching Under Stress

When the heart faces oxygen limitation (ischemia, heart failure), it must rapidly shift from fatty acid to glucose oxidation. This metabolic switch is critical because:

Key regulatory mechanisms driving the glucose switch:

• AMPK activation: Falling ATP/AMP ratio activates AMP-activated protein kinase → stimulates GLUT4 translocation and glucose uptake
• Malonyl-CoA decrease: AMPK phosphorylates acetyl-CoA carboxylase (ACC) → reduced malonyl-CoA → relieves CPT-I inhibition (paradoxically can increase FA oxidation short-term)
• HIF-1α induction: Hypoxia-inducible factor 1α upregulates glycolytic enzymes and glucose transporters
• PDH complex activation: Decreasing acetyl-CoA from β-oxidation relieves PDH inhibition → pyruvate oxidation increases

Skeletal Muscle Energy Systems

Skeletal muscle is the most adaptable tissue in the body — it can increase energy expenditure 100-fold within seconds during maximal exercise. Unlike the heart, which relies on continuous oxidative metabolism, skeletal muscle uses three overlapping energy systems that activate in sequence based on exercise intensity and duration.

ATP-PCr System (Phosphagen System)

The phosphocreatine (PCr) system is the fastest energy system — it can regenerate ATP at rates faster than any other pathway, making it essential for explosive movements like sprinting, jumping, and Olympic weightlifting.

The key reaction is catalysed by creatine kinase (CK):

Creatine supplementation is the most well-researched ergogenic aid in sports nutrition. Loading doses (20 g/day × 5 days) followed by maintenance (3–5 g/day) increase intramuscular PCr stores by 20–40%, improving performance in repeated short-burst activities. Creatine is synthesised endogenously from glycine, arginine, and methionine in the liver and kidneys.

Anaerobic Glycolysis

When PCr stores are depleted and the effort continues at high intensity, anaerobic glycolysis becomes the dominant ATP source. This pathway converts glucose (from blood or muscle glycogen) to lactate, generating 2 ATP per glucose without requiring oxygen.

Aerobic Oxidative System

For exercise lasting longer than ~2 minutes, the aerobic oxidative system becomes the dominant ATP supplier. This system integrates glycolysis, β-oxidation, the TCA cycle, and oxidative phosphorylation to generate large amounts of ATP — but at a slower rate than anaerobic pathways.

import numpy as np
import matplotlib.pyplot as plt

# Energy system contribution vs exercise duration
duration_seconds = np.array([2, 5, 10, 30, 60, 120, 300, 600, 1800, 3600])
duration_labels = ['2s', '5s', '10s', '30s', '1m', '2m', '5m', '10m', '30m', '60m']

# Approximate % contribution of each system
atp_pcr = np.array([90, 70, 50, 15, 5, 2, 1, 0, 0, 0])
anaerobic = np.array([8, 25, 35, 60, 50, 35, 15, 8, 3, 1])
aerobic = np.array([2, 5, 15, 25, 45, 63, 84, 92, 97, 99])

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Stacked area chart
axes[0].stackplot(range(len(duration_labels)), atp_pcr, anaerobic, aerobic,
                  labels=['ATP-PCr', 'Anaerobic Glycolysis', 'Aerobic Oxidative'],
                  colors=['#BF092F', '#3B9797', '#16476A'], alpha=0.85)
axes[0].set_xticks(range(len(duration_labels)))
axes[0].set_xticklabels(duration_labels, fontsize=9)
axes[0].set_ylabel('% Energy Contribution')
axes[0].set_xlabel('Exercise Duration')
axes[0].set_title('Energy System Contributions vs Duration')
axes[0].legend(loc='center right', fontsize=8)

# Muscle fibre comparison
fibre_types = ['Type I\n(Slow Ox)', 'Type IIa\n(Fast Ox-Gly)', 'Type IIx\n(Fast Gly)']
mitochondria = [5, 3, 1]
glycolytic = [1, 3, 5]
fatigue_resist = [5, 3, 1]

x = np.arange(len(fibre_types))
width = 0.25
axes[1].bar(x - width, mitochondria, width, label='Mitochondrial Density', color='#16476A')
axes[1].bar(x, glycolytic, width, label='Glycolytic Capacity', color='#BF092F')
axes[1].bar(x + width, fatigue_resist, width, label='Fatigue Resistance', color='#3B9797')
axes[1].set_xticks(x)
axes[1].set_xticklabels(fibre_types, fontsize=9)
axes[1].set_ylabel('Relative Score (1–5)')
axes[1].set_title('Muscle Fibre Type Characteristics')
axes[1].legend(fontsize=8)

plt.tight_layout()
plt.savefig('muscle_energy_systems.png', dpi=150, bbox_inches='tight')
plt.show()
print("Energy systems: ATP-PCr (0-10s), Anaerobic glycolysis (10s-2min), Aerobic (>2min)")

Actin-Myosin Cross-Bridge Cycle

Muscle contraction is powered by the sliding filament mechanism, where thick filaments (myosin) pull thin filaments (actin) toward the centre of the sarcomere. The molecular engine driving this is the cross-bridge cycle — a repeating four-step process that converts chemical energy (ATP) into mechanical work.

Calcium & Excitation-Contraction Coupling

Excitation-contraction (E-C) coupling is the process that converts an electrical signal (action potential) into a mechanical response (muscle contraction). Calcium ions (Ca²⁺) are the critical link — they act as the molecular switch that turns contraction on and off.

Cardiac Ischemia & Biomarkers

Myocardial ischemia occurs when coronary blood flow is insufficient to meet the heart's metabolic demands — usually due to atherosclerotic plaque rupture with thrombus formation. The biochemical consequences are rapid and devastating, making biomarker detection critical for early diagnosis and treatment.

import numpy as np
import matplotlib.pyplot as plt

# Cardiac biomarker kinetics after myocardial infarction
hours = np.linspace(0, 336, 500)  # 0 to 14 days in hours

def biomarker_curve(hours, rise_start, peak_time, peak_value, decay_rate):
    """Model biomarker rise and decay after MI onset"""
    curve = np.zeros_like(hours)
    for i, h in enumerate(hours):
        if h < rise_start:
            curve[i] = 0
        elif h < peak_time:
            curve[i] = peak_value * ((h - rise_start) / (peak_time - rise_start)) ** 1.5
        else:
            curve[i] = peak_value * np.exp(-decay_rate * (h - peak_time))
    return curve

troponin = biomarker_curve(hours, 3, 18, 50, 0.015)
ckmb = biomarker_curve(hours, 4, 18, 30, 0.04)
myoglobin = biomarker_curve(hours, 1, 7, 20, 0.1)

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(hours / 24, troponin, color='#BF092F', linewidth=2.5, label='hs-Troponin I')
ax.plot(hours / 24, ckmb, color='#16476A', linewidth=2, label='CK-MB', linestyle='--')
ax.plot(hours / 24, myoglobin, color='#3B9797', linewidth=2, label='Myoglobin', linestyle='-.')
ax.axhline(y=5, color='gray', linestyle=':', alpha=0.6, label='Upper Reference Limit')
ax.fill_between(hours / 24, 0, 5, alpha=0.05, color='green')
ax.set_xlabel('Days After MI Onset', fontsize=12)
ax.set_ylabel('Relative Concentration (arbitrary units)', fontsize=12)
ax.set_title('Cardiac Biomarker Kinetics After Myocardial Infarction', fontsize=13, fontweight='bold')
ax.legend(fontsize=10, loc='upper right')
ax.set_xlim(0, 14)
ax.set_ylim(0, 55)
plt.tight_layout()
plt.savefig('cardiac_biomarkers.png', dpi=150, bbox_inches='tight')
plt.show()
print("Troponin peaks at 12-24h, remains elevated 7-14 days (gold standard)")
print("CK-MB peaks at 12-24h, returns to normal in 2-3 days (reinfarction detection)")
print("Myoglobin peaks earliest (6-9h) but is non-specific")

Exercise Biochemistry

Exercise triggers some of the most dramatic biochemical changes in the human body — a single bout of endurance exercise can activate over 900 genes in skeletal muscle. Understanding the molecular adaptations to exercise explains why physical activity is the single most effective intervention against metabolic disease, cardiovascular disease, neurodegeneration, and even cancer.

import numpy as np
import matplotlib.pyplot as plt

# Exercise intensity and fuel source utilisation
intensity_pct = np.array([20, 30, 40, 50, 60, 70, 80, 90, 100])

# Approximate % contribution at each intensity of VO2max
fat_pct = np.array([85, 80, 70, 55, 42, 28, 15, 5, 2])
cho_muscle = np.array([5, 8, 15, 25, 35, 45, 55, 60, 55])
cho_blood = np.array([5, 7, 10, 12, 15, 18, 20, 20, 18])
protein_pct = np.array([5, 5, 5, 8, 8, 9, 10, 15, 25])

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Fuel utilisation vs exercise intensity
axes[0].stackplot(intensity_pct, fat_pct, cho_muscle, cho_blood, protein_pct,
                  labels=['Fat Oxidation', 'Muscle Glycogen', 'Blood Glucose', 'Protein/AA'],
                  colors=['#3B9797', '#BF092F', '#16476A', '#666666'], alpha=0.85)
axes[0].axvline(x=65, color='white', linestyle='--', linewidth=1.5, alpha=0.8)
axes[0].annotate('Crossover\nPoint (~65%)', xy=(65, 50), fontsize=9,
                color='white', ha='center', fontweight='bold')
axes[0].set_xlabel('Exercise Intensity (% VO₂max)', fontsize=11)
axes[0].set_ylabel('Energy Contribution (%)', fontsize=11)
axes[0].set_title('Fuel Source vs Exercise Intensity', fontsize=12, fontweight='bold')
axes[0].legend(loc='upper left', fontsize=8)

# Training adaptation: mitochondrial density
weeks = np.arange(0, 13)
untrained_mito = np.full(13, 3.0)
trained_mito = 3.0 + 2.5 * (1 - np.exp(-0.2 * weeks))
vo2max_improvement = np.array([40, 41, 42.5, 44, 45, 46, 47, 48, 48.5, 49, 49.5, 50, 50])

ax2 = axes[1]
ax2_twin = ax2.twinx()
ax2.bar(weeks - 0.2, trained_mito, 0.4, color='#16476A', alpha=0.8, label='Mito Density (trained)')
ax2.bar(weeks + 0.2, untrained_mito, 0.4, color='#cccccc', alpha=0.6, label='Mito Density (untrained)')
ax2_twin.plot(weeks, vo2max_improvement, color='#BF092F', linewidth=2.5, marker='o',
              markersize=4, label='VO₂max (mL/kg/min)')
ax2.set_xlabel('Training Weeks', fontsize=11)
ax2.set_ylabel('Relative Mitochondrial Density', fontsize=11, color='#16476A')
ax2_twin.set_ylabel('VO₂max (mL/kg/min)', fontsize=11, color='#BF092F')
ax2.set_title('Endurance Training Adaptations', fontsize=12, fontweight='bold')
ax2.legend(loc='upper left', fontsize=8)
ax2_twin.legend(loc='center right', fontsize=8)

plt.tight_layout()
plt.savefig('exercise_biochemistry.png', dpi=150, bbox_inches='tight')
plt.show()
print("Crossover point: ~65% VO2max (fat → carb dominance)")
print("12 weeks of endurance training: ~25% increase in VO2max")

Practice Exercises

Exercise 1: Cardiac ATP Budget — If the heart consumes ~6 kg of ATP per day and stores only ~5 grams at any time, calculate the ATP turnover rate (how many times the total ATP pool is recycled per minute). Why does this make the heart uniquely vulnerable to mitochondrial poisons?

Solution:

• 6,000 g ATP/day ÷ 5 g stored = 1,200 turnovers per day
• 1,200 ÷ 1,440 min/day = ~0.83 turnovers per minute = entire pool replaced roughly every 72 seconds
• The heart has effectively zero tolerance for ATP production failure — even brief interruption (e.g., cyanide, carbon monoxide, ischemia) causes immediate functional impairment because stored ATP supports only ~10 seconds of contraction
• This explains why mitochondrial poisons (Complex I inhibitors like rotenone, Complex III inhibitors like antimycin A) are rapidly lethal — the heart arrests before other organs fail

Exercise 2: Energy Systems & Sports — A 400-metre sprint takes approximately 45 seconds and a marathon lasts about 3 hours. For each event, rank the three energy systems by contribution (%) and explain why the 400m is considered the most biochemically demanding event in athletics.

Solution:

• 400m sprint (~45s): Anaerobic glycolysis (~60%), ATP-PCr (~25%), Aerobic (~15%). The 400m sits in the "worst of both worlds" — too long for PCr stores, too fast for adequate aerobic supply, maximum lactate production
• Marathon (~3 hours): Aerobic oxidative (~98%), Anaerobic glycolysis (~1.5%), ATP-PCr (~0.5%). Predominantly fat oxidation with glycogen usage increasing toward the end ("hitting the wall" at ~30 km = glycogen depletion)
• The 400m is most demanding because: (1) anaerobic glycolysis produces maximum H⁺ accumulation (pH ~6.8), (2) PCr is fully depleted, (3) lactate reaches 20–25 mmol/L (vs 1 mmol/L at rest), (4) both phosphagen and glycolytic systems are maximally taxed simultaneously

Exercise 3: Cross-Bridge Cycle & Disease — Explain why hypertrophic cardiomyopathy (HCM), caused by mutations in β-myosin heavy chain or cardiac myosin-binding protein C, leads to diastolic dysfunction. How does this relate to the cross-bridge cycle?

Solution:

• HCM mutations (e.g., MYH7 R403Q) typically produce "gain of function" in myosin — increased ATPase activity, faster cross-bridge cycling, and prolonged time in the force-generating state
• This means more cross-bridges are engaged at any time → increased contractility (hypercontractile state) but impaired relaxation (diastolic dysfunction) because myosin takes longer to detach
• Increased ATP consumption from hyperactive myosin → energy deficit → compensatory hypertrophy → fibrosis → arrhythmia risk (leading genetic cause of sudden cardiac death in young athletes)
• Mavacamten (approved 2022) — a cardiac myosin inhibitor that reduces the number of active cross-bridges, treating the molecular defect rather than the symptoms

Exercise 4: Troponin Interpretation — A patient presents to A&E with chest pain. Serial troponin measurements: 0h = 25 ng/L, 3h = 180 ng/L. The 99th percentile URL is 14 ng/L. Is this an acute MI? What if the values were 25 ng/L at 0h and 28 ng/L at 3h?

Solution:

• Scenario 1 (25 → 180 ng/L): Both values above URL (14 ng/L) AND significant rise/fall pattern (δ = 155 ng/L, >50% change). This is consistent with acute myocardial infarction (Type 1 MI) — dynamic troponin elevation with clinical context of chest pain
• Scenario 2 (25 → 28 ng/L): Both above URL but with stable/chronic elevation (δ = 3 ng/L, <20% change). This is chronic myocardial injury — suggests conditions like heart failure, CKD, stable CAD, or myocarditis. Does NOT meet criteria for acute MI (requires >20% change with hs-cTn assays)
• Key principle: Rise and/or fall pattern distinguishes acute injury (MI) from chronic elevation (stable disease). A single elevated troponin is insufficient for MI diagnosis

Exercise 5: Exercise Prescription — Using your knowledge of AMPK, PGC-1α, and mTOR signalling, design a weekly exercise programme for a Type 2 diabetic patient that maximises insulin sensitivity while preserving muscle mass. Explain the molecular rationale for your timing choices.

Solution:

• Programme: 3× aerobic sessions (Mon/Wed/Fri, 30–45 min at 60–70% VO₂max) + 2× resistance sessions (Tue/Thu, major compound lifts)
• Aerobic rationale: Activates AMPK → insulin-independent GLUT4 translocation (24–72h effect) → improved glucose disposal. PGC-1α activation → mitochondrial biogenesis → enhanced fat oxidation → reduced intramyocellular lipids (lipotoxicity)
• Resistance rationale: Mechanical tension activates mTORC1 → protein synthesis → preserved/increased muscle mass → larger glucose sink
• Timing separation: Keep aerobic and resistance on separate days (or aerobic AM / resistance PM with ≥6 hours gap) to minimise AMPK–mTOR interference (AMPK inhibits mTORC1 via TSC2 phosphorylation)
• Post-resistance nutrition: 20–40g protein within 2 hours → maximise muscle protein synthesis (MPS) via leucine-mediated mTORC1 activation

Heart & Muscle Worksheet

Conclusion & Next Steps

Heart and skeletal muscle represent two extremes of the metabolic spectrum — the heart operates as a tireless aerobic engine fuelled primarily by fatty acids, while skeletal muscle dynamically switches between three energy systems depending on demand. The cross-bridge cycle and calcium signalling provide the molecular machinery for contraction, while cardiac biomarkers reveal the biochemical fingerprints of ischemic damage. Exercise emerges as the most powerful metabolic intervention available, activating pathways (AMPK, PGC-1α, mTOR) that improve every aspect of human health.