Background: Epidemiological evidence suggests that green leafy vegetables (GLV) may reduce the risk of cardiovascular disease (CVD). However, the relationship remains incompletely understood due to inconsistencies between findings from observational studies and randomized controlled trials (RCTs). Therefore, a comprehensive synthesis of evidence across study designs is warranted. This systematic review and meta-analysis aimed to evaluate the association between GLV consumption and CVD risk, as well as related cardiovascular risk factors, including blood pressure (BP) and endothelial function, based on evidence from observational studies and RCTs. Methods: Systematic searches of PubMed, EMBASE, APA PsycInfo, and Ovid databases were conducted through 30 October 2024. Risk of bias was assessed using the ROBINS-E tool for observational studies and the RoB 2 tool for RCTs. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated for observational studies, whereas mean differences (MDs) were used for RCT outcomes. Random-effects meta-analyses were performed, and heterogeneity was evaluated using Cochran’s Q and I2 statistics. Results: Higher GLV consumption was significantly associated with a reduced risk of CVD in observational studies (RR: 0.89; 95% CI: 0.82–0.97; p < 0.01; I2 = 80%; Q = 55.88) and lower CVD mortality (RR: 0.79; 95% CI: 0.62–0.97; p < 0.01; I2 = 80%; Q = 4.93). However, pooled analysis of seven RCTs showed no significant improvement in vascular function following GLV intake (MD: −0.37; 95% CI: −1.90 to 1.16; p = 0.64; I2 = 88%; Q = 18.59). No significant associations were observed between GLV consumption and hypertension risk in observational studies, nor were significant effects found on systolic or diastolic BP in RCTs. Conclusions: Higher GLV consumption may be associated with a lower risk of CVD in observational studies. However, evidence from RCTs remains limited and inconclusive. Further well-designed clinical trials are needed to confirm these associations, establish dose–response relationships, and elucidate the mechanisms underlying the cardioprotective effects of GLVs to support evidence-based dietary recommendations.
We present Multi-Agent Reasoning and Coordination (MARC), an open-source framework that replaces monolithic LLM prompting with deterministic multi-agent orchestration for clinical reasoning. MARC coordinates role-specialized agents for extraction, reasoning, answer generation, and evaluation, with explicit context passing and traceable intermediate outputs, enabling stage-wise failure attribution. We additionally introduce a Decomposer module that generates task-specific agent prompts from a plain-language description, eliminating manual prompt engineering. The framework supports both API-based and local CPU-compatible deployments and is entirely configurable via YAML, without code modifications. MARC is designed to be model-agnostic, interpretable, and accessible to clinical domain experts without programming expertise. The full framework is available at https://github.com/Penn-RAIL/MARC-v1.
Saisha Shetty, Satvik Tripathi, A. Lin et al.· 0 citations
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