MétaCan
Menu
Retour à la cohorte
Enregistrement W4287507288 · doi:10.4103/jod.jod_115_21

Subclinical COVID-19 Infection in Indian Children with New-Onset Type 1 Diabetes

2021· article· en· W4287507288 sur OpenAlexaboutno aff
Sharad Pendsey, Sanket Pendsey, Seema S. Chalkhore

Notice bibliographique

RevueJournal of Diabetology · 2021
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueDiabetes and associated disorders
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineType 1 diabetesPediatricsDiabetic ketoacidosisSubclinical infectionDiabetes mellitusReferralCoronavirus disease 2019 (COVID-19)Internal medicineFamily medicine

Résumé

récupéré en direct d'OpenAlex

Within our outpatient referral center for treatment of primarily type 2 diabetes in adults, we run a non-governmental organization (NGO) for children with type 1 diabetes mellitus (T1DM) providing free health care, insulin, monitoring devices, and many other required supports. Children with T1DM are referred from about a 300 km radius of our center situated in Nagpur, Central India. Prior to the pandemic, every month a mean of five children with new-onset T1DM were referred for further management. However, in June and July 2021 we found a sudden increase in the number of new-onset T1DM from five to eleven children per month, that is, a 120% increase [Figure 1]. All these children were in the age group of 3–16 years. They were all diagnosed with presumed T1DM based on clinical and biochemical parameters and young age of onset. This sudden rise in the number of TIDM cases made us suspect a possible association between COVID-19 and new-onset T1DM. During March, April, and May of 2021, Central India was going through a severe second wave of COVID-19. To assess for a possible causal relationship between the increase in T1DM cases and COVID-19, we measured COVID-19 specific IgG antibodies in all 22 children.Figure 1: Month-wise number of new-onset T1DM registered at our centerOf the 22 children, there were 11 males and females each. Fifteen (68%) presented with diabetic ketoacidosis (DKA), eight were admitted in private hospitals, and seven in teaching medical colleges and hospitals. Seven children presented with osmotic symptoms and blood glucose levels above 400 mg/dL and were treated in an ambulatory setting with multiple injections of insulin. Once clinically stable, all of these 22 children were referred to our center. One child with DKA on admission tested positive for COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR). None of the other children had symptoms suggestive of COVID-19 infection prior to their diagnosis of T1DM. All twenty-two children with new-onset T1DM were tested for COVID-19 specific IgG antibodies, with 16 being positive. SARS-COV-2 IgG is an automated semi-quantitative assay for the detection of immunoglobulin G (IgG) specific for the SARS-CoV-2 receptor-binding domain (RBD) of the spike protein in human serum or plasma (lithium heparin) using the ELFA (enzyme-linked fluorescent assay). In India, vaccination in children against COVID-19 infection has not been started. Eleven children had a history of contact with COVID-19 positive patients in their family or neighborhood. All of these children belonged to low socio-economic strata and were living in crowded homes and densely populated localities. Data on new-onset T1DM in children during coronavirus disease COVID-19 pandemic particularly in children is limited.[1] Many viruses have been associated with the etiology of type 1 diabetes, including enteroviruses especially Coxsackie B1 and B4, mumps, rubella, and cytomegalovirus, with enteroviruses most implicated.[2] However, a great deal of evidence has emerged regarding the COVID-19 pandemic with new-onset type 2 diabetes.[2] A Finnish study noted that there was an increase in the number of children admitted to pediatric intensive care units with T1DM and severe DKA during the first COVID-19 wave, but did not find an increase in the number of new-onset T1DM. Similar observations were seen in Italian, German, and Australian studies.[3] Data from Canada also did not show a rise in the number of children with new-onset T1DM but frequency of DKA at onset of T1DM was significantly higher in the pandemic period.[4] A study from the United Kingdom (UK) suggested that the incidence of new-onset T1DM had increased.[1] Our data come from the second wave of the COVID-19 pandemic. We speculate that COVID-19 not only increases the risk of DKA in those with T1DM but also could potentially induce new-onset T1DM, given the increase in observed incidence. All previous publications we could find were during the first wave of the COVID-19 pandemic. To the best of our knowledge, this is the first study that shows a relationship between the second wave of the COVID-19 pandemic and an increase in new-onset T1DM. Retrospective analysis of our data revealed that there was also a spike of new-onset T1DM in the month of December 2020 after the first wave of the COVID-19 pandemic in Central India, but this was previously unrecognized by us. Unsworth et al.[1] in their multicenter study in the UK postulated that SARS-CoV2 exposure contributed to the increased cases of new-onset T1DM by precipitating or accelerating T1DM onset. More studies are required to study the association of COVID-19 and new-onset T1DM. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. Acknowledgement The authors would like to acknowledge valuable guidance received from pediatric endocrinologists: Dr. Graham Ogle, Sydney, Australia, Dr. Ahmet Alexandra, Ottawa, and Dr. Caroline Zuijdwijk, Ottawa, Canada.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,030
Score d'incertitude au seuil0,059

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,275
Écart entre enseignants0,266 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of DiabetologyMême sujetDiabetes and associated disordersTravaux en français237 207