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Record W3210797516 · doi:10.1097/xce.0000000000000255

The risk factors potentially influencing risk of serious illness/death in people with diabetes, following SARS-CoV-2 infection: What needs to be done from here

2021· article· en· W3210797516 on OpenAlexaff
Adrian Heald, Mark Livingston, Gabriela Moreno, Martin Gibson

Bibliographic record

VenueCardiovascular Endocrinology & Metabolism · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsHealth Sciences Centre
Fundersnot available
KeywordsMedicineDiabetes mellitusPandemicDiseaseObesityIntensive care medicineOdds ratioPediatricsCoronavirus disease 2019 (COVID-19)Internal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Since early 2020 the whole world has been challenged by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus coronavirus disease 2019 (COVID-19) and the associated global pandemic [1]. People with diabetes are particularly at high risk of becoming seriously unwell after contracting this virus [2,3]. As yet we do not fully understand the underlying factors which contribute to such risk and their respective contributions to outcomes. In this regard, Barron et al. [4] reported an odds ratio (OR) for in-hospital COVID-19-related death of 2.03 (1.97–2.09) in people with type 2 diabetes mellitus (T2DM). A body of work is emerging in relation to the reasons why people with diabetes are more likely to become seriously unwell and in some cases die following a COVID-19 infection. Two recent landmark articles [5,6] have brought into sharp focus the factors that mediate increased risk of serious consequences of a COVID-19 infection in relation to hospital admission and mortality for vaccinated individuals and for nonvaccinated individuals in England. For the postvaccination study [6] the risk algorithm explained 74.1% of the variation in time to COVID-19 related death. An early COVID-19 pandemic study from Mexico [7] concluded that early-onset T2DM conferred an increased risk of hospitalization and obesity conferred an increased risk for ICU admission and intubation. The predictive score for COVID-19 lethality included age ≥65 years, diabetes, early-onset diabetes, obesity, age <40 years, chronic kidney disease, hypertension, immunosuppression and significantly discriminated lethal from nonlethal COVID-19 cases. We previously showed that in one area of the UK, the month-by-month mortality rate for people with T2DM was up to 2.2 times higher than that in the same month averaged over the previous 5 years in the early stages of the COVID-19 pandemic, with age the strongest independent predictor of death [8]. Work is continuing to understand how much a previous diagnosis of diabetes type 1 diabetes mellitus (T1DM) or T2DM increases the risk of becoming seriously unwell following a COVID-19 infection vs. absence of diabetes as a diagnosis. Since these early studies, the advent of treatment of hospitalized patients with Dexamethasone has significantly reduced mortality rates across the world. In patients hospitalized with COVID-19, the use of dexamethasone resulted in lower 28-day mortality among those who were receiving either invasive mechanical ventilation or oxygen alone at randomization [9]. Potential pathogenetic links between the SARS-CoV-2 virus and diabetes include the influence of glucose homeostasis and potentially altered immune status on the progression of the viral infection once established [10]. COVID-19 infection aggravates inflammation and alters immune system responses, leading to difficulties in blood glucose control. COVID-19 infection also increases the risk of thromboembolism and is more likely to induce cardiopulmonary failure in patients with diabetes than in patients without diabetes [11]. All of these mechanisms are now believed to contribute to the poor prognosis of some patients with preexisting diabetes and a COVID-19 infection. In conclusion, as the COVID-19 pandemic moves into a new phase in Europe and some other parts of the world, much remains to be determined about why having diabetes increases the risk of serious consequences of a COVID-19 infection. The COVID-19 global pandemic poses considerable health hazards, for patients with diabetes. The estimation of risk will require the combination of population based studies using primary and secondary care data and studies that evaluate the immunologic, metabolic and haematologic response following a COVID-19 infection at both a specific tissue and a whole body level, while keeping in mind that basic strategies such as optimizing pharmacologic management for patients at elevated cardiometabolic risk, with targeted choice of glucose lowering, antihypertensive and lipid lowering medications are as relevant to current and future research as to everyday clinical practice. Acknowledgements There was no external funding for this study. The data that support the findings of this study are available from the corresponding author upon reasonable request. Conflicts of interest There are no conflicts of interest.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.305
Teacher spread0.284 · 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 teacher head, not a consensus.

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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