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Record W4385689264 · doi:10.1101/2023.08.07.23293778

Diabetes following SARS-CoV-2 infection: Incidence, persistence, and implications of COVID-19 vaccination. A cohort study of fifteen million people

2023· preprint· en· W4385689264 on OpenAlexaboutno aff
Kurt Taylor, Sophie V. Eastwood, Venexia Walker, Geneviève Cézard, Rochelle Knight, Marwa Al Arab, Yinghui Wei, Elsie Horne, Lucy Teece, Harriet Forbes, Alex Walker, Louis Fisher, Jon Massey, Lisa Hopcroft, Tom Palmer, Jose Cuitun Coronado, Samantha Ip, Simon Davy, Iain Dillingham, Caroline E Morton, Felix Greaves, John Macleod, Ben Goldacre, Angela Wood, Nish Chaturvedi, Jonathan A C Sterne, Rachel Denholm

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
FundersNational Institute for Health Research Applied Research Collaboration WestDepartment of Health and Social CareMedical Research CouncilOregon Health and Science UniversityKnight Cancer Institute, Oregon Health and Science UniversityScottish GovernmentChief Scientist Office, Scottish Government Health and Social Care DirectorateInnovative Medicines InitiativeHealth and Social Care Research and Development DivisionPublic Health AgencyUniversity of BristolUniversity of ManchesterNIHR Cambridge Biomedical Research CentreEconomic and Social Research CouncilStroke AssociationCancer Research UKUniversity College LondonUniversity of CambridgeBritish Heart FoundationNational Institute for Health and Care ResearchEngineering and Physical Sciences Research CouncilUK Research and InnovationNIHR Bristol Biomedical Research CentreDiabetes UK
KeywordsMedicineIncidence (geometry)VaccinationHazard ratioCohortCoronavirus disease 2019 (COVID-19)Diabetes mellitusCohort studyType 2 diabetesPediatricsInternal medicineImmunologyDiseaseConfidence intervalInfectious disease (medical specialty)Endocrinology

Abstract

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Abstract Background Type 2 diabetes (T2DM) incidence is increased after diagnosis of COVID-19. The impact of vaccination on this increase, for how long it persists, and the effect of COVID-19 on other types of diabetes remain unclear. Methods With NHS England approval, we studied diabetes incidence following COVID-19 diagnosis in pre-vaccination (N=15,211,471, January 2020-December 2021), vaccinated (N =11,822,640), and unvaccinated (N=2,851,183) cohorts (June-December 2021), using linked electronic health records. We estimated adjusted hazard ratios (aHRs) comparing diabetes incidence post-COVID-19 diagnosis with incidence before or without diagnosis up to 102 weeks post-diagnosis. Results were stratified by COVID-19 severity (hospitalised/non-hospitalised) and diabetes type. Findings In the pre-vaccination cohort, aHRS for T2DM incidence after COVID-19 (compared to before or without diagnosis) declined from 3.01 (95% CI: 2.76,3.28) in weeks 1-4 to 1.24 (1.12,1.38) in weeks 53-102. aHRS were higher in unvaccinated than vaccinated people (4.86 (3.69,6.41)) versus 1.42 (1.24,1.62) in weeks 1-4) and for hospitalised COVID-19 (pre-vaccination cohort 21.1 (18.8,23.7) in weeks 1-4 declining to 2.04 (1.65,2.51) in weeks 52-102), than non-hospitalised COVID-19 (1.45 (1.27,1.64) in weeks 1-4, 1.10 (0.98,1.23) in weeks 52-102). T2DM persisted for 4 months after COVID-19 for ∼73% of those diagnosed. Patterns were similar for Type 1 diabetes, though excess incidence did not persist beyond a year post-COVID-19. Interpretation Elevated T2DM incidence after COVID-19 is greater, and persists longer, in hospitalised than non-hospitalised people. It is markedly less apparent post-vaccination. Testing for T2DM after severe COVID-19 and promotion of vaccination are important tools in addressing this public health problem. Research in context Evidence before this study We searched PubMed for population-based observational studies published between December 1st 2019 and July 12th 2023 examining associations between SARS-CoV-2 infection or COVID-19 diagnosis (search string: SARS-CoV-2 or COVID* or coronavirus*) and subsequent incident diabetes (search term: diabetes). Of nineteen relevant studies; eight had a composite outcome of diabetes types, six stratified by diabetes type and five pertained to type-1-diabetes (T1DM) only. We did not identify any studies relating to gestational or other types of diabetes. Eleven studies were from the US, three from the UK, two from Germany, one from Canada, one from Denmark and one from South Korea. Most studies described cumulative relative risks (for infection versus no infection) one to two years post-SARS-CoV-2 infection of 1.2 to 2.6, though four studies found no associations with T1DM after the post-acute period. All studies lacked the power to compare diabetes relative risk by type, severity, and vaccination status in population subgroups. One study examined relative risks by vaccination status, but this used a composite outcome of diabetes and hyperlipidaemia and was conducted in a predominantly white male population. Two studies of T1DM found no evidence of elevated risk beyond 30 days after COVID-19 diagnosis, whilst two reported elevated risks at six months. Two studies of type 2 diabetes (T2DM) examined relative risks by time period post-infection: one study of US insurance claims reported a persistent association six months post-infection, whereas a large UK population-based study reported no associations after 12 weeks. However, the latter study used only primary care data, therefore COVID-19 cases were likely to have been under-ascertained. No large studies have investigated the persistence of diabetes diagnosed following COVID-19; key to elucidating the role of stress/steroid-induced hyperglycaemia. Added value of this study This study, which is the largest to address the question to date, analysed linked primary and secondary care health records with SARS-CoV-2 testing and COVID-19 vaccination data for 15 million people living in England. This enabled us to compare the elevation in diabetes incidence after COVID-19 diagnosis by diabetes type, COVID-19 severity and vaccination status, overall and in population subgroups. Importantly, excess diabetes incidence by time period since infection could also be quantified. Since healthcare in the UK is universal and free-at-the-point-of-delivery, almost the entire population is registered with primary care. Therefore the findings are likely to be generalisable. We found that, before availability of COVID-19 vaccination, a COVID-19 diagnosis (vs. no diagnosis) was associated with increased T2DM incidence which remained elevated by approximately 30% beyond one year after diagnosis. Though still present (with around 30% excess incidence at eight weeks), these associations were substantially attenuated in unvaccinated compared with vaccinated people. Excess incidence was greater in people hospitalised with COVID-19 than those who were not hospitalised after diagnosis. T1DM incidence was elevated up to, but not beyond, a year post COVID-19. Around 73% of people diagnosed with incident T2DM after COVID-19 still had evidence of diabetes four months after infection. Implications of all the available evidence There is a 30-50% elevated T2DM incidence post-COVID-19, but we report the novel finding that there is elevated incidence beyond one-year post-diagnosis. Elevated T1DM incidence did not appear to persist beyond a year, which may explain why previous studies disagree. For the first time in a general-population dataset, we demonstrate that COVID-19 vaccination reduces, but does not entirely ameliorate, excess diabetes incidence after COVID-19. This supports a policy of universal vaccination and suggests that other public health activities, such as enhanced diabetes screening after severe COVID-19, may be warranted, particularly in unvaccinated people.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.107
GPT teacher head0.434
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations6
Published2023
Admission routes1
Has abstractyes

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