Billions Spent, Few Saved: Rethinking National Institutes of Health HEAL Initiative in Light of Dopaminergic Alternatives to the Opioid Trap

数十亿美元投入,收效甚微:鉴于多巴胺替代疗法可摆脱阿片类药物陷阱,重新思考美国国立卫生研究院的HEAL计划

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Abstract

BACKGROUND: Despite >$3.2 billion invested across >1,800 projects by NIH's HEAL Initiative, scalable solutions for chronic pain and opioid use disorder remain limited. Continued reliance on opioid-replacement therapies often suppresses reward circuitry without restoring its neurobiology. OBJECTIVE: To articulate a neurobiologically grounded, whole-person strategy that reframes perioperative pain management as dopaminergic restoration, and to propose pragmatic clinical and policy steps for translation. APPROACH CONCEPTUAL MODEL: We synthesize evidence that chronic pain and addiction share hypodopaminergic mechanisms (Reward Deficiency Syndrome). We highlight precision nutraceuticals (amino-acid precursors + enkephalinase inhibitors) aimed at restoring dopamine homeostasis-especially in genetically vulnerable patients identified by the Genetic Addiction Risk Severity (GARS) test-and position them within ERAS 2.0 as adjuncts to peripheral analgesia. KEY POINTS/RECOMMENDATIONS: (1) Initiate dopaminergic repletion ~2 weeks preoperative to 4 weeks postoperative, layered with local anesthetic strategies. (2) Incorporate GARS-guided risk stratification and track MMEs, pain, function, mood/craving, LOS, and 90-day opioid persistence. (3) Prioritize multicenter pragmatic RCTs and registries with mechanistic endpoints. (4) Improve funding transparency and link HEAL-like investments to clinical outcomes dashboards. CLINICAL SIGNIFICANCE: We challenge the clinical status quo and call on spine surgeons and pain specialists to integrate dopaminergic repletion protocols within a precision-prehabilitation framework that offers a low-risk, non-opioid pathway to reduce suffering, enhance recovery, and decrease opioid dependence in spine surgery. LEVEL OF EVIDENCE: 5 (Expert Opinion). The model integrates neurobiology, early translational signals, and policy levers to guide hypothesis-generating implementation.

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