New‐onset diabetes in “long <scp>COVID</scp>”
Notice bibliographique
Résumé
Studies published in the Journal of Diabetes and elsewhere demonstrate the increased likelihood of new-onset diabetes (NOD) during the acute phase1-7 or shortly after recovering from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2),8-10 the virus causing coronavirus disease 2019 (COVID -19). Findings from these studies are supported by a recent Mendelian randomization analysis establishing a causal link between SARS-CoV-2 infection and NOD.11 Emerging evidence shows that NOD is also observed in the post-acute COVID-19 phase, the so-called long COVID.12, 13 In a retrospective cohort study of 47 780 discharged COVID-19 patients (mean age 65 years) in England, the rate of NOD was 29 (95% CI, 26-32) per 1000 person-years over a mean follow-up of 4.6 months.14 In another retrospective cohort study of three data sources from a large United States health plan, among 193 113 COVID-19 patients aged ≤65 years, NOD was the sixth most common post-acute clinical sequelae over a median follow-up of 2.9 months.15 Possible mechanisms explaining the occurrence of NOD with SARS-CoV-2 infection during the acute phase are cytolytic effects of the virus on pancreatic β-cells,16 activation of the hypothalamic-pituitary-adrenal and sympathoadrenal axes causing an increase in counterregulatory hormones, activation of the renin-angiotensin system resulting in unopposed deleterious actions of angiotensin II, and enhanced autoimmunity.17, 18 However, it is yet to be determined whether these mechanisms persist in the post-acute phase for the development of NOD in long COVID. It is essential to screen COVID-19 patients for NOD during acute illness and after recovery for several reasons. Globally, 50% of adults remain undiagnosed, and this figure reaches up to 60% in some low- and middle-income countries.19 Therefore, some of the NOD in hospitalized COVID-19 patients could reflect previously undiagnosed diabetes discovered incidentally by increased testing.5 Secondly, acute infections can cause stress hyperglycemia, which may resolve once the infection and the coexistent inflammation subside.20 Further, COVID-19 patients are increasingly being treated with glucocorticoids that are known to induce hyperglycemia.21 As with stress hyperglycemia, blood glucose levels may return to the pre-illness stage after stopping steroids. Finally, autoantibodies against pancreatic β-cells triggered by respiratory viral infections usually develop over several months or years to cause type 1 diabetes.22 The COVID-19 pandemic has now persisted for over a year, and researchers across the globe are studying its long-term effects.2, 12-15, 23, 24 It is now high time to consider NOD as a metabolic clinical sequela of SARS-CoV-2 infection to understand the role of COVID-19 in driving the diabetes pandemic. No funding received. The authors declare no potential conflict of interest.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».