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Universal risk factors for multifactorial diseases

Overview of attention for article published in European Journal of Epidemiology, December 2007
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191 Mendeley
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2 CiteULike
Title
Universal risk factors for multifactorial diseases
Published in
European Journal of Epidemiology, December 2007
DOI 10.1007/s10654-007-9204-4
Pubmed ID
Authors

Ronald P. Stolk, Judith G. M. Rosmalen, Dirkje S. Postma, Rudolf A. de Boer, Gerjan Navis, Joris P. J. Slaets, Johan Ormel, Bruce H. R. Wolffenbuttel

Abstract

The risk for multifactorial diseases is determined by risk factors that frequently apply across disorders (universal risk factors). To investigate unresolved issues on etiology of and individual's susceptibility to multifactorial diseases, research focus should shift from single determinant-outcome relations to effect modification of universal risk factors. We present a model to investigate universal risk factors of multifactorial diseases, based on a single risk factor, a single outcome measure, and several effect modifiers. Outcome measures can be disease overriding, such as clustering of disease, frailty and quality of life. "Life course epidemiology" can be considered as a specific application of the proposed model, since risk factors and effect modifiers of multifactorial diseases typically have a chronic aspect. Risk factors are categorized into genetic, environmental, or complex factors, the latter resulting from interactions between (multiple) genetic and environmental factors (an example of a complex factor is overweight). The proposed research model of multifactorial diseases assumes that determinant-outcome relations differ between individuals because of modifiers, which can be divided into three categories. First, risk-factor modifiers that determine the effect of the determinant (such as factors that modify gene-expression in case of a genetic determinant). Second, outcome modifiers that determine the expression of the studied outcome (such as medication use). Third, generic modifiers that determine the susceptibility for multifactorial diseases (such as age). A study to assess disease risk during life requires phenotype and outcome measurements in multiple generations with a long-term follow up. Multiple generations will also enable to separate genetic and environmental factors. Traditionally, representative individuals (probands) and their first-degree relatives have been included in this type of research. We put forward that a three-generation design is the optimal approach to investigate multifactorial diseases. This design has statistical advantages (precision, multiple-informants, separation of non-genetic and genetic familial transmission, direct haplotype assessment, quantify genetic effects), enables unique possibilities to study social characteristics (socioeconomic mobility, partner preferences, between-generation similarities), and offers practical benefits (efficiency, lower non-response). LifeLines is a study based on these concepts. It will be carried out in a representative sample of 165,000 participants from the northern provinces of the Netherlands. LifeLines will contribute to the understanding of how universal risk factors are modified to influence the individual susceptibility to multifactorial diseases, not only at one stage of life but cumulatively over time: the lifeline.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 191 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 3 2%
Germany 1 <1%
Ghana 1 <1%
Kenya 1 <1%
Slovenia 1 <1%
Greece 1 <1%
Unknown 183 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 49 26%
Researcher 34 18%
Student > Master 31 16%
Student > Bachelor 14 7%
Student > Doctoral Student 10 5%
Other 26 14%
Unknown 27 14%
Readers by discipline Count As %
Medicine and Dentistry 64 34%
Agricultural and Biological Sciences 19 10%
Social Sciences 12 6%
Biochemistry, Genetics and Molecular Biology 10 5%
Psychology 7 4%
Other 40 21%
Unknown 39 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 October 2013.
All research outputs
#8,882,501
of 26,017,215 outputs
Outputs from European Journal of Epidemiology
#954
of 1,864 outputs
Outputs of similar age
#45,443
of 170,594 outputs
Outputs of similar age from European Journal of Epidemiology
#3
of 9 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,864 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.4. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 170,594 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.