Association of social isolation, loneliness and risk of cardiovascular diseases: Meta-analysis of cohort studies
Bibliographic record
Abstract
Abstract Background The association between social isolation, loneliness and risk of cardiovascular diseases (CVD) is not fully understood. This meta-analysis aims to explore social isolation and loneliness whether increases the risk of CVD. Methods Data sources was PubMed and Embase from inception to 10 February 2025. The risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Hazard ratios (HR) with 95% confidence intervals (CI) were pooled using a random-effect model, and publication bias was assessed with funnel plots and Egger’s test. Results This meta-analysis included six cohort studies with a total of 5,253,128 participants, spanning a follow-up period of 4 to 11.3 years from publications between 1996 and 2022. All studies were of high quality (NOS score ≥ 7). The pooled analysis revealed a heightened risk of CVD among individuals experiencing social isolation or loneliness (HR = 1.17, 95% CI 1.10–1.25, I2 = 85.1%, P < 0.001). Subgroup analysis indicated that patients with a history of social isolation had a slightly higher risk of CVD compared to those with loneliness [HR = 1.39, 95% CI 1.15–1.68, I2 = 90.2%, P = 0.001]. Additionally, the risk of CVD was slightly elevated during the 4–7 year follow-up compared to 7–9 years and 10–11 years [HR = 1.87, 95% CI 1.67–2.10, I2 = 0%, P < 0.001]. Those with a history of social isolation or loneliness had the highest risk of stroke [HR = 1.23, 95% CI 1.07–1.43, I2 = 74.5%, P = 0.004]. Furthermore, Asian populations exhibited a slightly higher risk of CVD compared to North American and European populations [HR = 1.46, 95% CI 1.12–1.91, I2 = 0%, P = 0.005]. Conclusions The increased risk of CVD among social isolation or loneliness individuals underscore the importance of prioritizing their care in clinical practice and nursing. However, the high heterogeneity in meta-analysis suggests the need for further studies to validate and explore this association thoroughly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.038 | 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".