The Moli-sani risk score, a new algorithm for measuring the global impact of modifiable cardiovascular risk factors
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 includin...
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| Vydané v: | International journal of cardiology Ročník 389; s. 131228 |
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| Hlavní autori: | , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Netherlands
Elsevier B.V
15.10.2023
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| ISSN: | 0167-5273, 1874-1754, 1874-1754 |
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| Abstract | 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.
•Vascular disease is preventable through interventions targeting modifiable factors.•A score of vascular modifiable risk factors is provided, based on European data.•The algorithm is able to measure the combined impact of modifiable risk factors.•The score can be used to quantify the effectiveness of preventive interventions. |
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| AbstractList | 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.BACKGROUNDCardiovascular (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.METHODSTo 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.RESULTSAfter 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.CONCLUSIONWe 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. 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. •Vascular disease is preventable through interventions targeting modifiable factors.•A score of vascular modifiable risk factors is provided, based on European data.•The algorithm is able to measure the combined impact of modifiable risk factors.•The score can be used to quantify the effectiveness of preventive interventions. 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. |
| ArticleNumber | 131228 |
| Author | Roncaglioni, Maria Carla Donati, Maria Benedetta Costanzo, Simona Iacoviello, Licia Persichillo, Mariarosaria Cerletti, Chiara Di Castelnuovo, Augusto De Curtis, Amalia Bracone, Francesca Baviera, Marta de Gaetano, Giovanni Baldassarre, Damiano Bonaccio, Marialaura Panzera, Teresa |
| Author_xml | – sequence: 1 givenname: Augusto surname: Di Castelnuovo fullname: Di Castelnuovo, Augusto organization: Mediterranea Cardiocentro, Napoli, Italy – sequence: 2 givenname: Marialaura surname: Bonaccio fullname: Bonaccio, Marialaura organization: Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy – sequence: 3 givenname: Simona surname: Costanzo fullname: Costanzo, Simona organization: Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy – sequence: 4 givenname: Amalia surname: De Curtis fullname: De Curtis, Amalia organization: Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy – sequence: 5 givenname: Mariarosaria surname: Persichillo fullname: Persichillo, Mariarosaria organization: Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy – sequence: 6 givenname: Teresa surname: Panzera fullname: Panzera, Teresa organization: Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy – sequence: 7 givenname: Francesca surname: Bracone fullname: Bracone, Francesca organization: Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy – sequence: 8 givenname: Damiano surname: Baldassarre fullname: Baldassarre, Damiano organization: Centro Cardiologico Monzino, IRCCS, Milan, Italy – sequence: 9 givenname: Maria Carla surname: Roncaglioni fullname: Roncaglioni, Maria Carla organization: Laboratory of Cardiovascular Prevention, Istituto di Ricerche Farmacologiche Mario Negri-IRCCS, Milan, Italy – sequence: 10 givenname: Marta surname: Baviera fullname: Baviera, Marta organization: Laboratory of Cardiovascular Prevention, Istituto di Ricerche Farmacologiche Mario Negri-IRCCS, Milan, Italy – sequence: 11 givenname: Chiara surname: Cerletti fullname: Cerletti, Chiara organization: Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy – sequence: 12 givenname: Maria Benedetta surname: Donati fullname: Donati, Maria Benedetta organization: Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy – sequence: 13 givenname: Giovanni surname: de Gaetano fullname: de Gaetano, Giovanni organization: Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy – sequence: 14 givenname: Licia surname: Iacoviello fullname: Iacoviello, Licia email: licia.iacoviello@moli-sani.org organization: Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37527754$$D View this record in MEDLINE/PubMed |
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| Keywords | Prevention Predictive algorithms Cardiovascular risk Modifiable risk factors |
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