Evidence map›Paper›PMID 35260745›Full record

ArticleScientific reports2022

Exposome-wide ranking of modifiable risk factors for cardiometabolic disease traits.

Alaitz Poveda, Hugo Pomares-Millan, Yan Chen, Azra Kurbasic, Chirag J Patel, Frida Renström, Göran Hallmans, Ingegerd Johansson, Paul W Franks

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 31% of its field
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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Understanding exposomes and its relation with cancer risk in Malaysia based on epidemiological evidence: a narrative review.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2024
    Review
  4. Article
  5. 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

9 authors at 3 institutions in 3 countries.

Alaitz PovedaDepartment of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, 214 28, Malmö, Sweden.
Hugo Pomares-Millan *Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, 214 28, Malmö, Sweden.
Yan Chen *Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, 214 28, Malmö, Sweden.
Azra KurbasicDepartment of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, 214 28, Malmö, Sweden.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Frida RenströmDepartment of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, 214 28, Malmö, Sweden.
Göran HallmansDepartment of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
Ingegerd JohanssonDepartment of Odontology, Umeå University, Umeå, Sweden.
Paul W FranksDepartment of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, 214 28, Malmö, Sweden. paul.franks@med.lu.se.
Lund University · SEHarvard University · USUmeå University · SE

Funding

Data science tools to identify robust exposure-phenotype associations for precision medicineR01ES032470 · NIEHS · HARVARD MEDICAL SCHOOL · PI MANRAI, ARJUN KUMAR, PATEL, CHIRAG J. · 2021 to 2025
$3.5M
NIEHS NIH HHS R01 ES032470
6 · The paper itself

Abstract

The present study assessed the temporal associations of ~ 300 lifestyle exposures with nine cardiometabolic traits  to identify exposures/exposure groups that might inform lifestyle interventions for the reduction of cardiometabolic disease risk. The analyses were undertaken in a longitudinal sample comprising > 31,000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test associations with 10-year change in the cardiometabolic traits. 'Physical activity' and 'General Health' were the exposure categories containing the highest number of 'tentative signals' in analyses assessing the average association of lifestyle variables, while 'Tobacco use' was the top category for the 10-year change association analyses. Eleven modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to the domains: (i) Smoking, (ii) Beverage (filtered coffee), (iii) physical activity, (iv) alcohol intake, and (v) specific variables related to Nordic lifestyle (hunting/fishing during leisure time and boiled coffee consumption). We used an agnostic, data-driven approach to assess a wide range of established and novel risk factors for cardiometabolic disease. Our findings highlight key variables, along with their respective effect estimates, that might be prioritised for subsequent prediction models and lifestyle interventions.

Indexed as

Cardiovascular DiseasesExposomeAdultCoffeeHumansPhenotypeRisk FactorsCoffee

Identifiers

PMID35260745
PMCPMC8904494
OpenAlexW4220738872

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.