Nutrigenomics and Ethics Interface: Direct-to-Consumer Services and Commercial Aspects
Bibliographic record
Abstract
A growing variety and number of genetic tests are advertised and sold directly to consumers (DTC) via the Internet, including nutrigenomic tests and associated products and services. Consumers have more access to genetic information about themselves, but access does not entail certainty about the implications of test results. Potential personal and public health harms and benefits are associated with DTC access to genetic testing services. Early policy responses to direct-to-consumer (DTC) genetic testing often involved calls for bans, and some jurisdictions prohibited DTC genetic tests. Recent policy responses by oversight bodies acknowledge expansion in the range of DTC tests available and suggest that a "one-size-fits-all" regulatory approach is not appropriate for all genetic tests. This review discusses ethical and regulatory aspects of DTC genetic testing, focusing particularly on nutrigenomic tests. We identify policy options for regulating DTC genetic tests, including full or partial prohibitions, enforcement of existing truth-in-advertising laws, and more comprehensive information disclosure about genetic tests. We advocate the latter option as an important means to improve transparency about current evidence on the strengths and limits of gene-disease associations and allow consumers to make informed purchasing decisions in the DTC marketplace.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".