Subclinical COVID-19 Infection in Indian Children with New-Onset Type 1 Diabetes
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".