Current pharmacological treatment of diabetes is largely algorithmic. Other than for cardiovascular disease or renal disease, where sodium–glucose cotransporter 2 inhibitors and/or glucagon-like peptide-1 receptor agonists are indicated, the choice of treatment is based upon overall risks of harm or side effect and cost, and not on probable benefit. Here we argue that a more precise approach to treatment choice is necessary to maximise benefit and minimise harm from existing diabetes therapies. We propose a roadmap to achieve precision medicine as standard of care, to discuss current progress in relation to monogenic diabetes and type 2 diabetes, and to determine what additional work is required. The first step is to identify robust and reliable genetic predictors of response, recognising that genotype is static over time and provides the skeleton upon which modifiers such as clinical phenotype and metabolic biomarkers can be overlaid. The second step is to identify these metabolic biomarkers (e.g. beta cell function, insulin sensitivity, BMI, liver fat, metabolite profile), which capture the metabolic state at the point of prescribing and may have a large impact on drug response. Third, we need to show that predictions that utilise these genetic and metabolic biomarkers improve therapeutic outcomes for patients, and fourth, that this is cost-effective. Finally, these biomarkers and prediction models need to be embedded in clinical care systems to enable effective and equitable clinical implementation. Whilst this roadmap is largely complete for monogenic diabetes, we still have considerable work to do to implement this for type 2 diabetes. Increasing collaborations, including with industry, and access to clinical trial data should enable progress to implementation of precision treatment in type 2 diabetes in the near future.

Graphical abstract

中文翻译：

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实现糖尿病药理学精准医疗的路线图

目前对糖尿病的药物治疗主要是算法。除了心血管疾病或肾脏疾病，需要使用钠-葡萄糖协同转运蛋白 2 抑制剂和/或胰高血糖素样肽-1 受体激动剂，治疗的选择是基于伤害或副作用的总体风险和成本，而不是基于可能的好处。在这里，我们认为需要一种更精确的治疗选择方法，以最大限度地提高现有糖尿病疗法的益处并最大限度地减少危害。我们提出了将精准医学作为护理标准的路线图，讨论与单基因糖尿病和 2 型糖尿病相关的当前进展，并确定需要进行哪些额外工作。第一步是确定稳健可靠的反应遗传预测因子，认识到基因型随着时间的推移是静态的，并提供了可以覆盖临床表型和代谢生物标志物等修饰物的骨架。第二步是识别这些代谢生物标志物（例如，β细胞功能、胰岛素敏感性、BMI、肝脏脂肪、代谢物谱），这些标志物可以捕捉处方时的代谢状态，并可能对药物反应产生重大影响。第三，我们需要证明利用这些遗传和代谢生物标志物的预测可以改善患者的治疗结果，第四，这是具有成本效益的。最后，这些生物标志物和预测模型需要嵌入临床护理系统，以实现有效和公平的临床实施。虽然这个路线图对于单基因糖尿病来说基本上是完整的，对于 2 型糖尿病，我们仍有大量工作要做。增加合作，包括与行业的合作，以及获得临床试验数据，应该能够在不久的将来推动实施 2 型糖尿病的精准治疗。

图形概要