{"publication_id":"d8f38608-04e9-4baf-a98a-b15cef29f955","screening":{"identified":56,"screened":56,"excluded":0,"included":56,"included_or_retained":56,"flow":["identified","screened","excluded_with_reasons","included"],"wording":"56 candidate receipts retained after source retrieval, deduplication, and topic filtering. This is an evidence-map screening trace, not a PRISMA full-text exclusion audit.","exclusion_reasons":["No PRISMA full-text exclusion-stage filter was applied."]},"limitations":["This is an agent-assisted evidence map, not a PRISMA-complete systematic review or clinical guideline.","It is not PROSPERO-registered and should not be read as medical advice.","Public sidecars expose citation traces and extraction status; empty fields mean not extracted, not assumed absent."],"contradictions":["Evidence-honesty note: 43/56 retained sources are indirect, review-level, adjacent, or mechanistic and are used only to bound interpretation. The conclusion therefore does not support broad causal, clinical, or policy claims. Liraglutide, a glucagon-like peptide-1 receptor agonist, is now evaluated across an unusually broad slate of cardiometabolic, renal, hepatic, neurologic, and behavioral biomarkers, with regulatory and clinical interest extending well beyond glycemic control (Teng 2024; Yeo 2025). Whether the surrogate-endpoint signals that drive most biomarker reports translate into clinically meaningful aging-related benefit remains unresolved, an issue framed by Ioannidis-style surrogate-endpoint caution (Ioannidis 2005). We applied an AI-assisted structured evidence synthesis with an explicit audit trail, restricting the analytic frame to direct human randomized or longitudinal evidence on liraglutide and separating it from mechanistic or cross-domain signals. We conclude that liraglutide produces reproducible within-class cardiometabolic biomarker gains in direct RCTs, while its purported broader aging-related biomarker benefits are not yet demonstrated and should be treated as hypothesis-generating until adequately powered direct trials report hard functional endpoints. **Evidence-abstraction note.","The corpus contains 13 direct clinical sources, 42 adjacent, review, or context sources, and 1 mechanistic or model-system source. That distribution makes the synthesis appropriate for evaluating convergence, boundary conditions, and trial-design implications, while requiring caution around any conclusion that would exceed the direct human evidence.","Null findings have a specific role in this evidence model. They do not erase mechanistic plausibility, but they do narrow the set of claims that can be made about effect consistency, target population, and endpoint selection.","The evidence base also distinguishes breadth from certainty. A broad corpus can cover many biological domains while still leaving the clinically decisive question unresolved if direct evidence is limited, heterogeneous, or endpoint-specific.","The direct evidence establishes what has been observed in human or adjacent clinical settings. The mechanistic evidence helps explain why an effect might be plausible, but it does not by itself establish the size, durability, or safety of a human healthspan effect.","The study-level structure also prevents selective emphasis. Supportive, null, mixed, and adverse findings remain visible in the same manuscript, allowing the reader to distinguish evidential breadth from evidential certainty."]}