New‐onset diabetes in “long <scp>COVID</scp>”
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".