Is prediabetes a risk factor for severe <scp>COVID</scp>‐19?
Notice bibliographique
Résumé
Several studies, including those published in the Journal of Diabetes, have investigated the association between coronavirus disease 2019 (COVID-19) and type 2 diabetes.1-3 In general, these studies have shown that type 2 diabetes is a common comorbidity in hospitalized COVID-19 patients, and those with type 2 diabetes often present with a poor clinical profile and experience severe outcomes of COVD-19. Further, emerging evidence increasingly suggests that COVID-19 may unmask previously undiagnosed diabetes as well as cause new-onset diabetes.4-6 However, relatively little is known about prediabetes and COVID-19.7 In a multicenter study by Sourji et al at 10 hospital sites in Austria, of 238 COVID-19 patients, 47 (19.7%) had prediabetes (admission glycosylated hemoglobin [HbA1c] 5.7%-6.4%), of which 17% were admitted to the intensive care unit and 14.9% died during hospitalization.8 In a study by Bhatti et al of 410 COVID-19 patients admitted to a single hospital in Dubai, UAE, 10 (2.4%) had prediabetes (prior diagnosis or admission HbA1c 5.7%-6.4%), of which 20% had in-hospital mortality.9 In a study by Smith et al of 184 patients hospitalized for COVID-19 at a single hospital in New Jersey, USA, 44 (23.9%) had prediabetes (admission HbA1c 5.7%-6.4%), of which 15.9% required invasive mechanical ventilation.10 In a study by Wang et al conducted among 605 COVID-19 patients admitted at two hospitals in Wuhan, China, 100 (16.5%) had prediabetes (admission fasting plasma glucose [FPG] 6.1-6.9 mmol/L).11 About 48% of these patients developed complications (eg, acute respiratory distress syndrome, acute cardiac injury) within 28 days of hospitalization. Besides, Kaplan-Meier survival curves showed that those with prediabetes had significantly lower rates of survival within 28 days of hospitalization than those with FPG <6.1 mmol/L (P < .0001). In a hospital-based study by Tee et al in Singapore among 240 male migrant workers infected with COVID-19, 21 (8.8%) had prediabetes (admission HbA1c 5.7%-6.4% and/or 2-hour post load plasma glucose 7.8-11.0 mmol/L).12 In this study, compared with normoglycemia, prediabetes was significantly associated with a higher risk of pneumonia (crude odds ratio [OR] 10.8; 95% CI, 3.65-32.1), hyponatremia (crude OR 8.83; 95% CI, 1.17-66.6), and hypokalemia (crude OR 4.58; 95% CI, 1.52-13.82). These findings suggest that those with prediabetes are likely to develop severe outcomes of COVID-19. This could be, at least in part, due to exacerbation of the underlying pathophysiology of prediabetes, including chronic low-grade inflammation, impaired innate immunity, poor adaptive immune response to infections, and pro-coagulative state.7 Age, comorbidities (eg, hypertension), and obesity may also contribute to the risk of severe outcomes. However, none of these studies have examined the risk factors for disease severity and in-hospital mortality in patients with prediabetes. These shortcomings call for further research in this area. Prediabetes is highly prevalent in most populations globally, and worryingly, the majority of people with prediabetes are unaware of their diagnosis.13 Thus, it is essential to screen all COVID-19 patients at the time of hospital admission with HbA1c and/or plasma glucose to identify those with prediabetes14 so that they can be closely monitored and appropriate treatment can be initiated early to improve their prognosis. No funding received. None declared.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».