Fuente: PubMed "meat" Epidemiology. 2026 Jan 5. doi: 10.1097/EDE.0000000000001932. Online ahead of print.ABSTRACTBACKGROUND: Projections for major health outcomes are crucial for decision-making to enable early interventions. Pooling results from meta-analyses with estimates based on individual participant data can reduce uncertainty and improve generalizability. However, current methods for meta-meta-analyses of nonlinear functions have remained underdeveloped.METHODS: We proposed a novel meta-analysis method to pool pointwise nonlinear function literature estimates with parameter estimates, applied here to the association of dietary change scenarios with mortality and ischemic heart disease based on Finnish individual participant data. We linked individual-level demographic and risk factor data from the Health 2000, FINRISK 2007, FINRISK 2012 and FinHealth 2017 surveys (n=20,784) with follow-up data on outcomes obtained from national health registers. We applied a Poisson multistate model and microsimulation to project state probabilities.RESULTS: Pooling reduced uncertainty in the hazard ratio estimates and in the health projections. We estimated that a two-thirds reduction in red and processed meat consumption would decrease the prevalence of IHD by 2 (95% prediction intervals PI 1, 4) percentage points (%pt) in the 2017 cohort and deaths by 2%pt (95% PI 1, 4) by 2050. We estimated that a 100% increase in whole grain consumption would reduce IHD by 2%pt (95% PI 0, 3) and deaths by 2%pt (95% PI 0, 3).CONCLUSIONS: Our flexible meta-analysis method allowed the pooling of nonlinear estimates reported in literature, without detailed technical information.Our estimates support the hypothesis that more plant-based diets reduce mortality and IHD prevalence. The most beneficial scenarios included reductions in red and processed meat, and increments in whole grain consumption.PMID:41505395 | DOI:10.1097/EDE.0000000000001932