Evidence map›Paper›PMID 38947346›Full record

ArticleFrontiers in public health2024

Efficacy of polygenic risk scores and digital technologies for INNOvative personalized cardiovascular disease PREVention in high-risk adults: protocol of a randomized controlled trial.

Roberta Pastorino, Angelo Maria Pezzullo, Antonella Agodi, Chiara de Waure, Walter Mazzucco, Luigi Russo, Martina Bianchi, Alessandra Maio, Sara Farina, Martina Porcelli and 16 more

Erratum issuedAbstract readClinical Trial Protocol
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

26 authors.

Roberta PastorinoSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Angelo Maria PezzulloSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Antonella AgodiDepartment of Medical and Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, Catania, Italy.
Chiara de WaureDepartment of Medicine and Surgery, University of Perugia, Perugia, Italy.
Walter MazzuccoAzienda Ospedaliera Universitaria Policlinico "Paolo Giaccone" di Palermo, Palermo, Italy.
Luigi RussoSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Martina BianchiSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Alessandra MaioSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Sara FarinaSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Martina PorcelliSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Diego Maria TonaSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Matteo Di PumpoSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Rosarita AmoreSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Malgorzata WachockaSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Tina PasciutoSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Martina BarchittaDepartment of Medical and Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, Catania, Italy.
Roberta Magnano San LioDepartment of Medical and Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, Catania, Italy.
Giuliana FavaraDepartment of Medical and Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, Catania, Italy.
Antonino TuttolomondoAzienda Ospedaliera Universitaria Policlinico "Paolo Giaccone" di Palermo, Palermo, Italy.
Fabio TramutoAzienda Ospedaliera Universitaria Policlinico "Paolo Giaccone" di Palermo, Palermo, Italy.
Gaia MorelloAzienda Ospedaliera Universitaria Policlinico "Paolo Giaccone" di Palermo, Palermo, Italy.
Daniele Domenico De BellaDipartimento PROMISE, Università degli Studi di Palermo, Palermo, Italy.
Santo FruscioneDipartimento PROMISE, Università degli Studi di Palermo, Palermo, Italy.
Anna SeverinoDepartment of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy.
Giovanna LiuzzoDepartment of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy.
Stefania BocciaSection of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cardiovascular diseases (CVDs) pose a significant global health challenge, necessitating innovative approaches for primary prevention. Personalized prevention, based on genetic risk scores (PRS) and digital technologies, holds promise in revolutionizing CVD preventive strategies. However, the clinical efficacy of these interventions requires further investigation. This study presents the protocol of the INNOPREV randomized controlled trial, aiming to evaluate the clinical efficacy of PRS and digital technologies in personalized cardiovascular disease prevention. Methods: The INNOPREV trial is a four-arm RCT conducted in Italy. A total of 1,020 participants, aged 40-69 with high 10-year CVD risk based on SCORE 2 charts, will be randomly assigned to traditional CVD risk assessment, genetic testing (CVD PRS), digital intervention (app and smart band), or a combination of genetic testing and digital intervention. The primary objective is to evaluate the efficacy of providing CVD PRS information, measured at baseline, either alone or in combination with the use of an app and a smart band, on two endpoints: changes in lifestyle patterns, and modification in CVD risk profiles. Participants will undergo a comprehensive assessment and cardiovascular evaluation at baseline, with follow-up visits at one, five, and 12 months. Lifestyle changes and CVD risk profiles will be assessed at different time points beyond the initial assessment, using the Life's Essential 8 and SCORE 2, respectively. Blood samples will be collected at baseline and at study completion to evaluate changes in lipid profiles. The analysis will employ adjusted mixed-effect models for repeated measures to assess significant differences in the data collected over time. Additionally, potential moderators and mediators will be examined to understand the underlying mechanisms of behavior change. Discussion: As the largest trial in this context, the INNOPREV trial will contribute to the advancement of personalized cardiovascular disease prevention, with the potential to positively impact public health and reduce the burden of CVDs on healthcare systems. By systematically examining the clinical efficacy of PRS and digital interventions, this trial aims to provide valuable evidence to guide future preventive strategies and enhance population health outcomes.

Indexed as

Cardiovascular DiseasesDigital TechnologyAdultAgedFemaleGenetic Risk ScoreGenetic TestingHumansItalyMaleMiddle AgedPrecision MedicinePrimary PreventionRandomized Controlled Trials as TopicRisk Assessmentcardiovascular diseasepersonalized preventionpolygenic risk score (PRS)smart bandstrial

Identifiers

PMID38947346
PMCPMC11211566

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.