The impact of insulin resistance on long-term outcomes in heart failure: a systematic review
Bibliographic record
Abstract
Introduction Insulin resistance (IR) is a metabolic condition in which the cells in the body become less responsive to insulin, the blood glucose regulation hormone. While typically associated with type 2 diabetes mellitus (T2DM), IR worsens cardiovascular disease (CVD) progression. 1 Heart failure (HF) is highly prevalent in the UK, contributing to 2% of NHS hospital bed stays and 5% of emergency admissions. 2 Although HF is an established T2DM complication, it can occur in patients with IR independent of diabetes. 3 While the link between IR and HF is well documented, the impact of IR on HF prognosis remains underexplored. Thus, this systematic review assessed the role of IR in HF outcomes. Materials and Methods A systematic literature review was conducted with adherence to PRISMA guidelines. 4 Databases used included PubMed, Ovid Medline and Cochrane Library. Search terms included mesh (insulin resistance, heart failure, Mortality, hospitalisation) and non-mesh (Long-term outcomes) terms. Boolean operations and truncations were used to refine results. Inclusion criteria included studies assessing IR in patients with HF (both preserved and reduced ejection fractions), and studies that reported long-term outcomes (≥6 months) addressing mortality, hospitalisations or functional decline. Exclusion criteria included studies focusing solely on T2DM without IR analysis, studies with an extremely small sample size (<50), or with high methodological bias, or with follow-up periods, where relevant. Tools, such as the Newcastle Ottawa scale and Cochrane’s risk of bias, were used to minimise bias and data were extracted into a table for comparison. Results and Discussion The studies included in this review are summarised in Table 1 with their relevant findings. Overall, they indicate a strong association between IR and adverse long-term HF outcomes. A range of study designs were included. However, many were retrospective rather than prospective, limiting the ability to establish causality in long-term outcomes. More high-quality prospective studies are needed because they better establish disease progression over time. Long-term outcomes were evaluated through measures such as hospitalisation rates, mortality, disease severity and functional decline. Some studies included participants from Japan and Vietnam, improving ethnic diversity but with genetic and metabolic differences being possible confounders. Despite limitations, this review highlights the use of IR as a key factor in long-term outcomes in HF. Conclusion Given its strong association with HF outcomes, IR should be integrated into risk stratification tools and considered for incorporation into National Institute of Health and Care Excellence (NICE) guidelines for HF prognosis and management. Multicentre prospective studies will help further validate the role of IR in HF risk assessment and strengthen its integration into clinical practice.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| 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".