Review article

E-cigarette aerosols as systemic metabolic disruptors: integrated mitochondrial, circadian, and neurobehavioral mechanisms

Where this started

Most discussions on e-cigarettes still center on pulmonary and cardiovascular harm. That focus is important, but while reading across the literature, another pattern kept appearing. Some studies pointed to mitochondrial injury. Others focused on oxidative stress, lipid dysregulation, adipose inflammation, circadian disruption, appetite control, or reward signaling.

Each mechanism made sense on its own.

But the bigger question became harder to ignore:

• Are these separate effects, or connected parts of a broader metabolic disruption network?
• Why metabolism?

Metabolism is not just about body weight. It includes how cells produce energy, handle lipids, regulate insulin sensitivity, synchronize with circadian time, and coordinate hunger, reward, and energy expenditure.

That matters because metabolic dysfunction can begin before obvious changes in body weight appear. A person may not gain weight immediately, but lipid handling, mitochondrial efficiency, insulin signaling, and circadian-metabolic timing may already be shifting.

This made metabolism a useful lens for integrating findings that otherwise looked fragmented.

What the evidence kept pointing to

Across cellular, animal, and early human studies, several recurring signals emerged.

• Mitochondrial respiration becomes less efficient
  
• Reactive oxygen species and redox imbalance increase
  
• Glutathione defenses are disrupted
  
• AMPK signaling and lipid oxidation are altered
  
• Adipose tissue shows inflammatory and lipolytic changes
  
• Hepatic lipid handling becomes dysregulated
  
• Circadian clock signaling is disturbed
  
• Reward, appetite, and hunger-related pathways may be modified

What stood out was not one isolated pathway. It was the possibility that these pathways reinforce each other.

Mitochondrial stress can amplify oxidative stress. Oxidative stress can impair metabolic signaling. Impaired lipid handling can increase tissue stress. Circadian disruption can worsen metabolic timing. Reward and appetite changes can further bias energy balance.

These mechanisms suggest a feed-forward model of metabolic inflexibility.

The part that complicates the narrative

The evidence does not support a simple statement like:

“E-cigarettes cause weight gain.”

The picture is more complex.

Human data are still limited, mostly observational, and affected by confounding from dual use, prior smoking, diet, activity, and weight-control motivations. Another complication is that body weight is a blunt endpoint. Some studies do not show consistent changes in overall weight, but more concerning signals appear in central adiposity, insulin resistance, lipid remodeling, and metabolic regulation.

This means body weight alone may underestimate risk.

Why aerosol composition matters

Not all effects can be reduced to nicotine.

E-cigarette aerosols contain heated solvents, aldehydes, metals, flavorant-derived compounds, and other reactive chemicals. Device power, coil composition, puffing behavior, solvent ratio, and flavor chemistry all influence the final aerosol. That variability matters.

Different devices and liquids may create different metabolic stress profiles. This makes the field difficult to interpret using one-size-fits-all exposure categories. A “vape exposure” is not a single biological exposure. It is a moving chemical mixture.

Why this perspective matters

Looking at e-cigarettes through a metabolic systems lens changes the question.

Instead of asking only:

• Does vaping affect the lungs?
  
• Does it affect the heart?
  
• Does it affect body weight?

The question becomes:

What happens when mitochondrial, circadian, lipid, inflammatory, and neurobehavioral systems are repeatedly pushed out of balance?

From that angle, seemingly separate findings begin to align.

Pulmonary exposure can generate systemic redox stress. Mitochondrial dysfunction can affect energy balance. Circadian disruption can alter metabolic timing. Reward and appetite pathways can influence feeding behavior. Adipose and liver dysfunction can propagate insulin resistance. The outcome may not be one disease pathway, but a network-level shift toward metabolic vulnerability.

What still feels unresolved

Several gaps remain.

• Human studies need better longitudinal designs
  
• Exposure patterns need to reflect real-world use
  
• Device and formulation variability need tighter characterization
  
• Dose-response relationships are still unclear
  
• Sex-specific and developmental effects need more attention
  
• Reversibility after cessation remains poorly defined
  
• Metabolic outcomes should go beyond BMI and body weight

The field also needs studies that measure multiple systems at once: mitochondria, redox state, lipid flux, insulin sensitivity, circadian timing, appetite, and energy expenditure. Without that integration, the biology will continue to look more fragmented than it may actually be.

Where this leads

This review does not claim that every user will develop metabolic disease. It argues that e-cigarette aerosols should be investigated as potential systemic metabolic disruptors.

If this model is correct, then metabolic risk may emerge from the interaction of several systems rather than from one dominant mechanism.

That shift matters. It moves the discussion from isolated toxic effects toward network biology, where mitochondria, redox balance, adipose tissue, liver, circadian clocks, and reward circuits are treated as connected parts of the same metabolic architecture.

Discussion point

Which node do you think is most important in e-cigarette-related metabolic disruption?

• Mitochondrial dysfunction
  
• Oxidative stress
  
• AMPK and lipid metabolism
  
• Adipose inflammation
  
• Circadian disruption
  
• Reward and appetite pathways
  
• Cross-tissue metabolic communication

Reference

Sailis AB. E-cigarette aerosols as systemic metabolic disruptors: integrated mitochondrial, circadian, and neurobehavioral mechanisms. Toxicology Mechanisms and Methods. 2026. https://doi.org/10.1080/15376516.2026.2658739

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