Evidence map›Paper›PMID 39525379›Full record

ReviewMethodist DeBakey cardiovascular journal2024

Big Data, Big Insights: Leveraging Data Analytics to Unravel Cardiovascular Exposome Complexities.

Ramzi Ibrahim, Hoang Nhat Pham, Khurram Nasir, Omar Hahad, Ashutosh Sabharwal, Sadeer Al-Kindi

Abstract readReview
In one paragraph

Review in Methodist DeBakey cardiovascular journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ramzi IbrahimMayo Clinic, Phoenix, Arizona, US.ORCID https://orcid.org/0000-0001-8412-0082
Hoang Nhat PhamUniversity of Arizona Tucson, Tucson, Arizona, US.ORCID https://orcid.org/0000-0001-8412-0082
Khurram NasirCenter for Cardiovascular Computational and Precision Health, Houston Methodist, Houston, Texas, US.ORCID https://orcid.org/0000-0001-5376-2269
Omar HahadUniversity Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.ORCID https://orcid.org/0000-0001-7823-7671
Ashutosh SabharwalRice University, Houston, Texas, US.ORCID https://orcid.org/0000-0003-1898-5787
Sadeer Al-KindiCenter for Cardiovascular Computational and Precision Health.ORCID https://orcid.org/0000-0002-1122-7695

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The exposome encompasses the full range of environmental exposures throughout a person's lifetime and plays an important role in cardiovascular health. Interactions with the social, natural, and built components of the exposome significantly impact cardiovascular disease prevalence and mortality. Robust data analytics, including machine learning and geospatial analysis, have advanced our understanding of how these factors converge to influence cardiovascular disease risk. The integration of multiomics platforms and advanced computational approaches enhances our ability to characterize the exposome, leading to targeted public health interventions and innovative risk reduction strategies aimed at improving cardiovascular health globally. These multiomics platforms that integrate factors such as genomics, epigenomics, clinical data, social factors, environmental factors, and wearable technology will characterize the exposome in greater detail concerning cardiovascular health. In this review, we aimed to elucidate the components of the exposome and discuss recent literature regarding their relationship to cardiovascular health.

Indexed as

Big DataCardiovascular DiseasesEnvironmental ExposureExposomeData MiningGene-Environment InteractionHeart Disease Risk FactorsHumansMachine LearningPrognosisRisk AssessmentRisk Factorsbig dataelectronic health dataexposome

Identifiers

PMID39525379
PMCPMC11546329

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.