Published: 2026-02-05 12:42
Non-Contrast CT May Offer New Way to Quantify Adipose Tissue Activity
A new research development suggests that non-contrast computed tomography (CT) scans could potentially be used to quantify metabolic activity in adipose tissue, a function traditionally reserved for positron emission tomography (PET) scans. This innovative approach, published in npj Digital Medicine, could offer a more accessible and less invasive method for assessing metabolic health and guiding obesity management strategies.
The ability to accurately measure adipose tissue activity without the need for specialised PET imaging could have significant implications for clinical practice, particularly within the NHS, where CT scans are far more readily available and cost-effective.
Understanding Adipose Tissue and Metabolic Health
Adipose tissue, commonly known as body fat, plays a crucial role beyond simple energy storage. Different types of fat have distinct metabolic functions.
White adipose tissue (WAT) primarily stores energy, while brown adipose tissue (BAT) and beige adipose tissue (a form of WAT that can acquire BAT-like characteristics) are metabolically active, burning calories to generate heat through a process called thermogenesis.
The activity of BAT and beige fat is linked to energy expenditure and can influence metabolic health, including glucose homeostasis and insulin sensitivity. Individuals with higher levels of active brown or beige fat tend to have a lower risk of obesity and related metabolic disorders, such as type 2 diabetes and cardiovascular disease.
Currently, assessing the metabolic activity of adipose tissue, particularly BAT, often relies on imaging techniques like 18F-fluorodeoxyglucose (FDG) PET/CT. FDG-PET measures glucose uptake, which is a proxy for metabolic activity.
While effective, PET scans involve ionising radiation from the radiotracer, are expensive, and have limited availability, making them unsuitable for routine screening or widespread clinical application.
A Novel Approach Using Non-Contrast CT
The new research explores the possibility of inferring adipose tissue activation from standard non-contrast CT scans. This method leverages advanced computational techniques, likely involving artificial intelligence (AI) and machine learning, to analyse subtle features within CT images that correlate with metabolic activity.
While CT scans primarily provide anatomical information based on tissue density, sophisticated algorithms can extract complex patterns and textures from these images. These ‘radiomic’ features may hold clues about the underlying cellular composition and metabolic state of the adipose tissue, allowing researchers to predict its activation status without direct metabolic tracer uptake measurement.
The potential benefit lies in utilising existing imaging data. Many patients undergo non-contrast CT scans for various diagnostic purposes, meaning that information about their adipose tissue activity could potentially be extracted retrospectively or as part of a routine scan, without additional procedures or radiation exposure.
Potential Clinical Implications for UK Healthcare
If validated and integrated into clinical workflows, this approach could significantly impact several areas of UK healthcare:
Enhanced Obesity Management
- Personalised Interventions: Clinicians could gain a deeper understanding of an individual’s metabolic phenotype beyond simple body mass index (BMI) or body fat percentage. Identifying patients with lower active fat levels might guide more targeted lifestyle interventions or pharmacological treatments aimed at boosting thermogenesis.
- Risk Stratification: Better assessment of adipose tissue activity could help identify individuals at higher risk of developing metabolic complications, allowing for earlier preventative strategies.
Metabolic Disease Monitoring
- Type 2 Diabetes: Monitoring changes in adipose tissue activity could provide insights into disease progression or response to treatment in patients with type 2 diabetes.
- Cardiovascular Health: Active fat plays a role in lipid metabolism; its assessment could contribute to a more comprehensive cardiovascular risk profile.
Drug Development and Efficacy Monitoring
For pharmaceutical companies developing new treatments for obesity or metabolic disorders, a non-invasive, widely available method to quantify adipose tissue activity could streamline clinical trials and provide a practical tool for monitoring treatment efficacy in real-world settings.
Improved Accessibility and Cost-Effectiveness
The widespread availability of CT scanners across the NHS means this technology could be integrated relatively easily. This would reduce the reliance on more expensive and less accessible PET facilities, potentially lowering healthcare costs and improving patient access to advanced metabolic assessments.
A comparison of current and potential methods highlights the advantages:
| Feature | FDG-PET/CT (Current Gold Standard) | Non-Contrast CT (Proposed Method) |
|---|---|---|
| Measures | Direct glucose uptake (metabolic activity) | Inferred metabolic activity from tissue characteristics |
| Radiation Exposure | From radiotracer and CT component | From CT component only (often pre-existing scan) |
| Cost | High | Low (if using existing scans) |
| Availability | Limited (specialised centres) | Widespread in NHS |
| Invasiveness | Requires radiotracer injection | Non-invasive (if using existing scans) |
| Clinical Use | Primarily research, specific diagnostics | Potential for routine screening, widespread monitoring |
Challenges and Future Directions
While promising, this research is still in its early stages. Several challenges must be addressed before widespread clinical adoption:
- Validation: The method requires extensive validation in larger, diverse patient cohorts to confirm its accuracy and generalisability across different populations and disease states.
- Standardisation: Ensuring consistency across various CT scanner models, imaging protocols, and AI algorithms will be crucial for reliable results.
- Regulatory Approval: Any AI-driven diagnostic tool would require rigorous regulatory approval, particularly for its use in clinical decision-making.
- Clinical Integration: Developing user-friendly interfaces and integrating the analysis into existing radiology reporting systems will be necessary for practical implementation.
This research represents a significant step towards a more comprehensive and accessible assessment of metabolic health. By potentially unlocking metabolic insights from routine non-contrast CT scans, clinicians could gain a powerful new tool in the fight against obesity and its associated metabolic diseases, offering a pathway towards more personalised and effective patient care in the UK.
Source: Nature
