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Record W3023253952 · doi:10.1002/ejhf.1845

The Intersecting Role of Glycaemia and Cardiac Function in the Development of Heart Failure Among Patients with Type 2 Diabetes Mellitus After an Acute Coronary Syndrome

2020· letter· en· W3023253952 on OpenAlexaffabout
Malik Elharram, João Pedro Ferreira, Abhinav Sharma

Bibliographic record

VenueEuropean Journal of Heart Failure · 2020
Typeletter
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineEjection fractionAcute coronary syndromeHeart failureInternal medicineCardiologyDiabetes mellitusPopulationType 2 Diabetes MellitusLixisenatideType 2 diabetesLiraglutideMyocardial infarctionEndocrinology

Abstract

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This article refers to ‘Hyperglycaemia, ejection fraction and the risk of heart failure or cardiovascular death in patients with type 2 diabetes and a recent acute coronary syndrome’ by S.H. Shin et al., published in this issue on pages 1133–1143. Risk assessment is of paramount importance in patients with diabetes who are survivors of an acute coronary syndrome (ACS). These patients have an early high-risk window for early cardiovascular (CV) events, including heart failure (HF), where mortality is exceedingly high.1 With a rapidly evolving landscape in treatment and prevention of CV disease among patients with type 2 diabetes mellitus (T2DM), there is a need to better identify high-risk patients, in whom initiating therapy targeting subsequent ischaemic events and HF may be life-saving. As oral hyperglycaemic agents, such as sodium–glucose co-transporter 2 (SGLT2) inhibitors and some glucagon-like peptide 1 (GLP-1) receptor agonist, reduce the risk of CV events among patients with T2DM with established atherosclerotic CV disease,2, 3 strategies to risk stratify for ischaemic and HF outcomes in a post-ACS population are a clinically important endeavour. In this issue of the Journal, Shin et al.4 add to our knowledge of risk assessment by examining the relationship between chronic hyperglycaemia and left ventricular ejection fraction (LVEF) in a contemporary cohort of T2DM with a recent ACS that participated in the ELIXA (Evaluation of Lixisenatide in Acute Coronary Syndrome) trial.5 In this secondary analysis, the authors examined 4091 patients in whom LVEF and glycated haemoglobin (HbA1c) was available after the index ACS. The authors assessed the relationship between baseline HbA1c and LVEF for a composite outcome of first occurrence of either CV death or hospitalization for HF (HFH). The authors showed that both an elevated HbA1c and a reduced LVEF were independently associated with an increased risk for HFH and/or CV death with the highest risk group seen in patients with an HbA1c >8% and LVEF <40%. The authors reported that chronic hyperglycaemia appeared to have a stronger association with CV death rather than HFH, which could be related to an increased risk for recurrent ischaemic events (Figure 1A). This finding could reflect the direct alteration of a vulnerable vascular endothelium in the post-ACS period to oxidative stress from prolonged hyperglycaemia leading to inflammation and thrombus formation.6 The degree of chronic hyperglycaemia did not appear to be independently associated with HFH alone after multivariable adjustment including LVEF (which remained significantly associated with HFH after multivariable adjustment). This could be a consequence of the duration of follow-up (median follow-up 25.7 months) or could reflect the hypothesis that the prognostic information provided by measures of glycaemic control are attenuated by other clinical features (e.g. LVEF). The impact of chronic hyperglycaemia on cardiac systolic or diastolic function, as mediated through endothelial dysfunction and fibrosis,7 might have been better captured with longer-term follow-up of these patients. These results reinforce the principle that a reduced LVEF still remains as one of the strongest predictors of HF outcomes among patients with T2DM after an ACS.8 There are some additional consideration of this analysis worth exploring. There was only a small proportion of patients with poorly controlled diabetes (18% with a HbA1c >9%). It is well known that both the micro- and macrovascular consequences of T2DM increase substantially with an elevated HbA1c, with the highest risk in patients with an HbA1c >9%.9 The relationship between HbA1c and LVEF might require a more significant degree of hyperglycaemia or perhaps chronic hyperglycaemia for a longer duration in order to impair systolic or diastolic function. Furthermore, while HbA1c may serve as a measure of chronic hyperglycaemia during the post-ACS period, inflammation and myocardial injury can persist over hours to weeks after an infarction,10 which could confound the measure of hyperglycaemia. While the authors attempted to adjust for the time interval of HbA1c measurement, the possibility for residual confounding may still exist. Furthermore, while HbA1c serves as an average marker of glucose control in the preceding 2 to 3 months, it negates fluctuations in glucose measures, which may serve as a more important prognostic factor than measures of HbA1c. Indeed, studies have shown that the variability in glucose levels and acuity of hyperglycaemia may be more damaging to the vascular endothelium than chronic hyperglycaemia.11 There was a low proportion of CV events, particularly for HFH (only 4.4%), which limits the individual power for the subgroup analysis as stratified by HbA1c and LVEF. Furthermore, the reclassification measures for HbA1c and LVEF were not described. These results suggest several avenues for clinical management and future investigation (Figure 1B). First, given the high risk of CV events, the present analysis from the ELIXA trial suggests that among patients with T2DM who are post-ACS, the degree of glycaemic control should not impact the decision to initiate antihyperglycaemic therapies that can reduce the risk of HF and CV events (namely SGLT2 inhibitors or GLP-1 receptor agonists). While large randomized clinical trials have shown no benefit of intensive glycaemic control in reducing macrovascular events,12, 13 the CV benefit of SGLT2 inhibitors2 and some GLP-1 receptor agonists1 appears to be largely independent of the degree of glycaemic control.14 Additional studies into the mechanism of why glycaemic control plays a prognostic role in CV death but not in HFH may help to clarify the relationship of how dysglycaemia contributes to the pathogenesis of HF. Among patients with T2DM post-ACS, the role of early initiation (either during the index ACS event or shortly thereafter) of antihyperglycaemic agents that can reduce CV risk should also be evaluated – especially among patients showing reduced cardiac function. The mechanism of disease progression might be more modifiable with simultaneous initiation of multiple classes of antihyperglycaemic agents and this strategy also warrants further investigation. Understanding the role of preserved vs. reduced ejection fraction in stratifying for the risk of recurrent myocardial infarction vs. HFH in patients with T2DM post-ACS needs to be clarified as this may help to define the specific types of antihyperglycaemic therapy that should be initiated. Finally, in patients with T2DM, given the low use of evidence-based and guideline-recommended antihyperglycaemic therapies to reduce the risk of CV events, there is a need to further leverage existing digital technology15 to identify patients in whom early initiation of SGLT2 inhibitors and GLP-1 receptor agonists are warranted. Conflict of interest: A.S. reports personal support from the Fonds de Recherche Sante – Quebec (FRSQ) Junior 1 clinician scientist award, McGill University Lucien Award, Bayer, Canadian Cardiovascular Society, Alberta Innovates Health Solution, Roche Diagnostics, Takeda, Boehringer Ingelheim, and Akcea. The other authors have nothing to disclose.

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 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)0.005
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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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Citations1
Published2020
Admission routes2
Has abstractyes

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