Objective:

To evaluate whether reported alcohol consumption scenarios are consistent with observed biomarker patterns using a physiological digital twin model, addressing challenges in health care and forensic settings.

Approach:

Key Findings:

• The model predicts biomarker profiles that align with observed data in controlled scenarios.
• Combining multiple biomarkers improves differentiation between similar drinking patterns.
• The model's predictions are influenced by input assumptions, leading to broader prediction ranges, which may limit its reliability in real-world applications.

Interpretation:

The digital twin model offers a novel approach to reconstructing alcohol intake scenarios, but its current validation limits its generalizability and reliability in real-world applications, necessitating further research.

Limitations:

• Limited validation against a single independent drinking scenario restricts generalizability across diverse real-world patterns.
• Does not fully account for interindividual variability in biomarker dynamics, which may affect accuracy.
• Observed discrepancies in biomarker elimination patterns indicate incomplete physiological representation, impacting model reliability.

Conclusion:

While the model shows promise for forensic applications and future research, it is not yet validated for clinical decision-making or legal use, underscoring the need for further validation.

Sources:

• Scientific Reports
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