Mediterranea Cardiocentro, Napoli, Italy.
Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy.
Int J Cardiol. 2023 Oct 15;389:131228. doi: 10.1016/j.ijcard.2023.131228. Epub 2023 Jul 30.
Cardiovascular (CV) disease is preventable through interventions targeting modifiable factors. Most algorithms based on modifiable CV risk factors (CV-rf) have been developed in US populations and do not account for the role of diet. We aimed to assess an algorithm based on modifiable CV-rf including diet, using data from an Italian population.
To derive the Moli-sani Risk Score (MRS), we used data on 16,656 men and women (age ≥ 35 y) from the population of the Moli-sani Study. The Risk-and-Prevention-Study, Italy (N = 8606) acted as external validation cohort and the Life's-Simple-7 score was used as benchmark. The MRS targeted at fatal or non-fatal CV events and included 9 common modifiable CV-rf.
After 8.1 years (median) of follow-up, 816 events occurred in the derivation cohort. The MRS was calculated as a weighted sum of its 9 components, with weights reflecting the strength of the association. In comparison with individuals in the first, those in the fourth quartile of the score had hazard ratio (HR) for CV events equal to 3.18 (95%CI: 2.54-3.97). One more point in the score was associated with 7% (6%-8%) and 4% (3%-5%) higher hazard of events in the derivation and validation cohort, respectively. The MRS performed better than the Life's Simple-7 for discrimination.
We propose the Moli-sani Risk Score, a validated, performing algorithm able to measure the combined impact that modifiable CV-rf have on CV risk. The score can be used to design preventive interventions, quantify the effectiveness of interventions, and compare different preventive strategies.
心血管疾病（CV）可以通过针对可改变因素的干预措施来预防。大多数基于可改变的 CV 危险因素（CV-rf）的算法都是在美国人群中开发的，并未考虑饮食的作用。我们旨在评估一种基于包括饮食在内的可改变 CV-rf 的算法，该算法使用了来自意大利人群的 Moli-sani 研究的数据。
为了推导出 Moli-sani 风险评分（MRS），我们使用了来自 Moli-sani 研究的 16656 名男性和女性（年龄≥35 岁）的数据。意大利风险与预防研究（N=8606）作为外部验证队列，使用 Life's-Simple-7 评分作为基准。MRS 针对致命或非致命的 CV 事件，包括 9 个常见的可改变的 CV-rf。
在 8.1 年（中位数）的随访期间，推导队列中发生了 816 例事件。MRS 是其 9 个组成部分的加权总和，权重反映了关联的强度。与评分第一四分位数的个体相比，评分第四四分位数的个体发生 CV 事件的风险比（HR）为 3.18（95%CI：2.54-3.97）。评分每增加 1 分，在推导和验证队列中分别与事件风险增加 7%（6%-8%）和 4%（3%-5%）相关。MRS 在区分度方面优于 Life's Simple-7。
我们提出了 Moli-sani 风险评分，这是一种经过验证、表现良好的算法，能够衡量可改变的 CV-rf 对 CV 风险的综合影响。该评分可用于设计预防干预措施、量化干预措施的效果，并比较不同的预防策略。
