Workplace-Based Glucose Screening for Type 2 Diabetes in French Civil Servants: Prospective Observational Cohort Study
Notice bibliographique
Résumé
Background: Type 2 diabetes (T2D) remains one of the most underdiagnosed chronic conditions worldwide, despite its major contribution to cardiovascular and metabolic morbidity. In 2024, an estimated 589 million adults were living with diabetes globally, more than 90% of whom had T2D, and the prevalence is projected to reach 853 million by 2050. In France, approximately 4.1 million adults are affected, and nearly 1 in 4 individuals with diabetes remain undiagnosed. Therefore, early detection is essential to prevent complications. Workplace prevention strategies could improve early detection, particularly among employed adults with limited access to regular medical screening. In France, a health prevention organization has implemented a systematic glucose screening program for civil servants to identify individuals at risk of T2D or prediabetes. As the French public service includes 5.7 million workers-approximately 1 in 5 of the national workforce-this setting provides a unique opportunity to reach large, diverse, and often underserved segments of the adult population. Objective: This study aimed to assess the effectiveness of a systematic diabetes screening program as a preventive public health measure by determining the rate of newly detected diabetes cases and characterizing associated cardiometabolic risk factors within a large population of French civil servants. Methods: A retrospective observational study was conducted using data from a glucose screening program between January 2022 and February 2025. Participants with postprandial blood glucose levels >1.40 g/L were included in a follow-up cohort. Sociodemographic, clinical, and biological data were collected. Comparisons were performed using the chi-square or Fisher exact test for categorical variables and the Student t test (2-tailed) for continuous variables (P<.05). Analyses were restricted to complete cases to ensure robust comparisons. Results: Among 16,785 screened participants, 981 (5.8%) had postprandial glucose levels >1.40 g/L and 134 (0.8%) were eligible for the follow-up cohort. Participants were 59.5% (n=78) women and 40.5% (n=53) men, with a mean age of 51.3 (SD 8.9) years. Overall, 37.6% (n=50) of participants were overweight, 25.4% (n=34) were obese, 61.6% (n=77) reported insufficient physical activity, and 63.2% (n=84) had a family history of diabetes. Of the 134 eligible individuals, 70 (52.2%) completed medical follow-up, and among them, 9 (12.9%) received a confirmed diagnosis of T2D. Newly diagnosed individuals were predominantly male (n=7, 78%; P=.04) and more likely to be overweight or obese (n=9, 89%; P=.04). No significant differences in age, sex, or BMI were observed between followed and lost-to-follow-up participants. Conclusions: Systematic glucose screening in occupational or social health context identifies individuals at risk of diabetes or prediabetes and supports its integration into preventive health strategies to enhance early detection and reduce long-term complications. Larger prospective or randomized studies are warranted to confirm long-term benefits on diagnosis, care engagement, and cardiometabolic outcomes.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».