A roadmap to achieve pharmacological precision medicine in diabetes.
A roadmap to achieve pharmacological precision medicine in diabetes.
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DOI：
10.1007/s00125-022-05732-3
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发表时间：
2022-11
期刊：
影响因子：
8.2
通讯作者：
Pearson, Ewan R.
中科院分区：
文献类型：
--
作者：
Florez, Jose C.;Pearson, Ewan R.
关键词：
目前糖尿病的药物治疗主要是算法。除了适用于钠-葡萄糖协同转运蛋白2抑制剂和/或胰高血糖素样肽-1受体激动剂的心血管疾病或肾脏疾病外，治疗的选择是基于损害或副作用的总体风险和成本，而不是可能的获益。在这里，我们认为，更精确的治疗选择方法是必要的，以最大限度地提高效益，并减少现有糖尿病治疗的危害。我们提出了一个路线图，以实现精准医疗作为标准的护理，讨论目前的进展与单基因糖尿病和2型糖尿病，并确定需要做什么额外的工作。第一步是确定反应的稳健和可靠的遗传预测因子，认识到基因型随时间推移是静态的，并提供了修饰因子（如临床表型和代谢生物标志物）可以覆盖的骨架。第二步是确定这些代谢生物标志物（如β细胞功能、胰岛素敏感性、BMI、肝脏脂肪、代谢物特征），这些生物标志物可在处方时捕获代谢状态，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到临床护理系统中，以实现有效和公平的临床实施。虽然这一路线图在单基因糖尿病方面基本完成，但我们仍有大量工作要做，以实现2型糖尿病。增加合作，包括与行业的合作，以及临床试验数据的获取，应该能够在不久的将来在2型糖尿病的精确治疗方面取得进展。在线版本包含数字幻灯片，可在10.1007/s 00125 -022-05732-3下载。
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. The online version contains a slideset of the figures for download, which is available at 10.1007/s00125-022-05732-3.
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影响因子：
7.7
作者：
Chen, Zsu-Zsu;Liu, Jinxi;Gerszten, Robert E.
通讯作者：
Gerszten, Robert E.
影响因子：
16.2
作者：
Dawed, Adem Y.;Zhou, Kaixin;Pearson, Ewan R.
通讯作者：
Pearson, Ewan R.
影响因子：
120.7
作者：
Steele, Anna M.;Shields, Beverley M.;Hattersley, Andrew T.
通讯作者：
Hattersley, Andrew T.
DOI：
10.1056/nejmsa2032271
发表时间：
2021-06-10
期刊：
The New England journal of medicine
影响因子：
--
作者：
Fang M;Wang D;Coresh J;Selvin E
通讯作者：
Selvin E
影响因子：
44.5
作者：
Ahlqvist, Emma;Storm, Petter;Groop, Leif
通讯作者：
Groop, Leif