Effects of polygenic risk score communication on health outcomes: systematic review and meta-analysis
Notice bibliographique
Résumé
Abstract Objective The purpose of this systematic review and meta-analysis is to summarize evidence from all RCTs to-date on the efficacy of polygenic risk score (PRS) communication in changing health outcomes. Design Systematic review and meta-analysis. Data sources Cochrane Central Register of Controlled Trials (CENTRAL) and Pubmed, from inception to March 2025. Study selection Randomized controlled trials comparing disclosure versus non-disclosure of PRS results. Intervention included comunication of risk information from ≥ 2 single nucleotide polymorphism (SNPs). Methods Pairs of reviewers conducted screening, extracted data and assessed risk of bias. Meta-analyses were conducted using inverse variance-weighting with fixed and random effects models. Otto-SR was used to verify screening and data extraction. Risk of bias was assessed with the Cochrane Risk of Bias 2 (RoB-2) tool. All measurable health-related outcomes were considered eligible. Results Of 7,830 articles retrieved, 27 RCTs were eligible. PRSs mainly predicted risks for cancer (n=9 RCTs), cardiovascular diseases (n=8), and diabetes (n=6). 21 RCTs targeted primarily healthy populations, 3 at-risk populations and 3 individuals who had already developed a disease and PRS predicted complications. 15/26 RCTs concluded in their abstracts with favorable claims about the PRS, with only 5/15 justifying it with any statistically significant results. 9/26 RCTs had high risk of bias. Meta-analysis revealed no statistically significant effects on any measured outcome, among 22 outcomes tested in 2 or more trials. Standardized mean differences (SMDs) (95% CI) for dietary outcomes were −0.11 (−0.23; 0.01) for daily energy intake, 0.08 (−0.15; 0.31) for daily fat intake and −0.11 (−0.28; 0.06) for alcohol consumption. For physical activity, SMD was −0.01 (−0.13; 0.11). Relative risks were 1.12 (0.77; 1.61) for screening attendance, 1.50 (0.98; 2.29) for statin use, and 0.95 (0.32; 2.79) for disease incidence. For psychological outcomes, SMDs were −0.02 (−0.13; 0.08) for anxiety, −0.06 (−0.23; 0.10) for worry, −0.10 (−0.40-0.19) for perceived risk, and −0.05 (−0.23; 0.13) for depression. For clinical outcomes, mean differences were −2.01 (−8.27; 4.26) for total cholesterol, −3.64 (−7.88; 0.60) for LDL cholesterol, −0.21 (−2.65; 2.23) for HDL cholesterol, −1.88 (−4.17;0.42) for diastolic blood pressure, −1.26 (−4.44; 1.92) for systolic blood pressure, −0.12 (−0.64; 0.39) for BMI and −0.33 (−0.87; 0.20) for weight. 69 outcomes had been reported in only a single trial (18/69 primary ones), and of those 2 had statistically significant results at p<0.05 as primary outcomes and 3 as secondary outcomes. Conclusions Overall, despite frequent promising claims, the disclosure of PRS typically did not lead to meaningful changes in behavioral, psychological or clinical outcomes. Systematic review registration OpenScience Framework: doi.org/10.17605/OSF.IO/28V6J Summary box Section 1: What is already known on this topic - Polygenic risk scores (PRSs) represents one of the most promising approaches of personalized medicine where communication of genetic information is expected to improve behavior and health outcomes. - Previous reviews have evaluated the communication of genetic risk but none have evaluated yet the effect of specifically PRS. - Since PRSs implementation in clinical practice is contemplated, rigorous evaluation of their impact is valuable for further implementation. Section 2: What this study adds - PRS communication effects were close to null for a large set of outcomes, demonstrating no improvement in preventive behaviours, including screening adherence or clinical measures. - High heterogeneity in RCTs in the field is present with small sample sizes, short follow-up periods, and many self-reported outcomes. - A gap is evident between the theoretical promise of PRS-guided prevention and its lack of documented real-world effectiveness.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,024 | 0,038 |
| Bibliométrie | 0,009 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».