Diabetes, Impaired Fasting Glucose, and Heart Failure: Its not All About the Sugar
Bibliographic record
Abstract
The linkage between diabetes and the development of cardiovascular disease is well established, including the development of a distinct diabetic cardiomyopathy, the co-existence of coronary artery disease (CAD) and the high prevalence (up to 40%) of diabetes in patients presenting with acute heart failure (AHF).1 Patients with diabetes are at higher risk of developing heart failure (HF)2 and, once present, it is associated with poor clinical outcomes.1 However, the adequacy of glycaemic control for patients with both diabetes and HF remains controversial with no clear association of glycated haemoglobin (HbA1c) and clinical events.3 Abnormalities in glucose regulation in patients without diabetes, such as impaired fasting glucose (IFG), abnormal oral glucose tolerance, or an elevated HBA1c, is present in up to 23% of HF patients.4 Furthermore, IFG is associated with worse outcomes in the setting of acute HF.4 Thus, there are three major questions that need addressing when dissecting issues relating to glucose regulation in patients with HF: (i) Is diabetes independent and directly driving the complications seen in patients with HF, or are the associated co-morbidities (CAD, hypertension, and renal disease) the driving force?; (ii) Why do patients with concomitant diabetes and HF do worse than patients without diabetes?; (iii) Can any diabetes-related therapy modify the outcomes for patients with HF? At a population level, the presence of diabetes increases the adjusted relative risk of the development of HF in men and women by 1.82 and 3.75, respectively.2 Despite these associations, there are conflicting results regarding the prognostic value of diabetes in patients with established HF. In both acute and chronic HF risk prediction models, diabetes is not consistently a significant contributor in predicting short and long-term mortality.6, 7 Animal models point to number of mechanisms whereby diabetes, independent of CAD risk factors, can contribute to the development of HF such as impaired calcium handling, increased myocardial fibrosis, increased renin–angiotensin–aldosterone activation, endothelial dysfunction, mitochondrial dysfunction, and increased advanced-glycosylated end product deposition (Figure 1).5 In addition, the relationship between diabetes, IFG, and HF may not be unidirectional. The pathogenesis of both diabetes and HF appears to be strongly influenced by inflammation with biomarker studies demonstrating that, for example, GDF-15 (a marker of inflammation and remodelling), predicts the development of both HF8 and diabetes.9 Is it possible that inflammation plays a central role in the development, pathogenesis, and propagation of HF and diabetes? Furthermore, evidence that HF may contribute to the development of diabetes also exists – this requires exploration and replication.10 Is this a true central mechanism, a chance finding, or simply a harvesting effect (i.e. the phenomenon whereby outcomes occur sooner in individuals in whom it would have happened anyway?). Work by Go et al.11 and Gotsman et al.12 attempts to address these issues (summarized in table 1). These studies corroborate previous work and reveal a high prevalence of CAD and associated risk factors, such as hypertension, peripheral vascular disease, and obesity in patients with HF. Both studies look at a broad multi-ethnic population that may be underrepresented in previous studies and confirm the association that all-cause mortality is higher in patients with diabetes than in those without diabetes. However, diabetes does not seem to alter the risk of mortality in patients with a preserved ejection fraction (HF-PEF; EF >50%) possibly suggesting that these individuals experience adverse outcomes from different causes. The question remains, do IFG and diabetes differ in their relationship to outcomes? The work by Gotsman et al.12 suggests that IFG and diabetes impart the same risk of adverse outcomes in HF. Potentially, does this imply that both diabetes or IFG contribute to, or are influenced, by structural, metabolic, or inflammatory dysregulation, which also associates with the development of HF (Figure 1)? Patients with EF <50% and diabetes have a higher mortality risk compared with non-diabetics Patients with HF-PEF with or without diabetes have a similar mortality risk of mortality Patients with diabetes with only mild to moderate LVEF impairment (EF 49–30%) have equivalent mortality risk compared with non-diabetes patients with severe LV impairments (EF <30%) Patients with diabetes have a higher all-cause mortality risk and cardiac-related complications Patients with IFG have mortality risk as high as patients with diabetes Patients in the lowest strata of IFG also have poor outcomes While Go et al.,11 Gotsman et al.,12 and others1 suggest poor outcomes in the setting of diabetes and HF, it is unclear what the major driver of mortality is. Potential confounders of the mechanism of death exist, as diabetics have multiple comorbidities such as renal disease, and an increased risk of infections. It is unclear if these patients with diabetes are dying from HF-related complications, progression of CAD, or complications related to cardiac and non-cardiac comorbidities. Unfortunately, neither study presented the cause of mortality for their diabetic and non-diabetic population, and cause of death studies can be challenging. In addition, tight glucose control with either oral or insulin-based antidiabetic regiments have failed to show substantial reduction in CV- and HF-related outcomes, and in some cases these have increased clinical event rates; hence we cannot recommend any modification to the clinical management for these patients for this goal. While poor glycaemic control appears to be a risk factor for the development of worsening HF, glucose-lowering therapies have shown disappointing results in preventing HF-related events. For example, targeted reduction of fasting glucose with long-acting insulin did not reduce CV events or the development of HF in patients with IFG or diabetes,13 and the use of dipeptidyl peptidase-4 inhibitors improves glycaemic control but increases the rate of hospitalizations for HF.14 Thiazolidinediones such as rosiglitazone have been associated with fluid retention and hence are of limited use in HF. The use of metformin in the setting of diabetes and HF has been associated with a reduction in all-cause mortality, hospital readmissions, and HF readmission in a number of cohort studies15 but these findings have not been replicated in large prospective trials. The use of liraglutide is currently being tested in patients with both HF and stable diabetes (clinicaltrials.gov NCT01800968) to reduce early post-AHF events. Given that HF is one of the most common causes of hospital admissions and readmissions, and the high mortality and adverse outcomes associated with diabetes and HF, further research is needed to help treat these patients and to allow them to live longer and healthier lives.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".