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Interaction between polyphenols intake and PON1 gene variants on markers of cardiovascular disease: a nutrigenetic observational study

Overview of attention for article published in Journal of Translational Medicine, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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9 tweeters

Citations

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18 Dimensions

Readers on

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42 Mendeley
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Title
Interaction between polyphenols intake and PON1 gene variants on markers of cardiovascular disease: a nutrigenetic observational study
Published in
Journal of Translational Medicine, June 2016
DOI 10.1186/s12967-016-0941-6
Pubmed ID
Authors

Federica Rizzi, Costanza Conti, Elena Dogliotti, Annalisa Terranegra, Erika Salvi, Daniele Braga, Flavia Ricca, Sara Lupoli, Alessandra Mingione, Francesca Pivari, Caterina Brasacchio, Matteo Barcella, Martina Chittani, Francesca D’Avila, Maurizio Turiel, Monica Lazzaroni, Laura Soldati, Daniele Cusi, Cristina Barlassina

Abstract

Paraoxonase 1 (PON1) gene polymorphisms and polyphenols intake have been reported independently associated to lipid profile and susceptibility to atherosclerosis and cardiovascular disease. However, the interaction between these factors remains to be investigated. We performed an observational nutrigenetic study to examine whether the interaction between polyphenols and anthocyanins intake and PON1 genetic variants can modulate biomarkers of cardiovascular health in an Italian healthy population. We recruited 443 healthy volunteers who participated in the EC funded ATHENA project (AnThocyanin and polyphenols bioactive for Health Enhancement through Nutritional Advancement). Data collection included detailed demographic, clinical, dietary, lifestyle, biochemical and genetic data. Polyphenols and anthocyanins intake was measured by 24 h dietary recall repeated three times a year in order to get seasonal variations. We tested the interaction between 18 independent tagging SNPs in PON1 gene and polyphenols intake on HDL, LDL, cholesterol, triglycerides and atherogenic index of plasma. Without considering the genetic background, we could not observe significant differences in the lipid profile between high and low polyphenols and anthocyanins intake. Using a nutrigenetic approach, we identified protective genotypes in four independent polymorphisms that, at Bonferroni level (p ≤ 0.0028), present a significant association with increased HDL level under high polyphenols and anthocyanins intake, compared to risk genotypes (rs854549, Beta = 4.7 per C allele; rs854552, Beta = 5.6 per C allele; rs854571, Beta = 3.92 per T allele; rs854572, Beta = 3.94 per C allele). We highlight the protective role of genetic variants in PON1 towards cardiovascular risk under high polyphenols and anthocyanins consumption. PON1 variants could represent novel biomarkers to stratify individuals who might benefit from targeted dietary recommendation for health promotion and strategies of preventive medicine.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 1 2%
Unknown 41 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 19%
Student > Ph. D. Student 7 17%
Researcher 7 17%
Student > Bachelor 6 14%
Student > Postgraduate 5 12%
Other 7 17%
Unknown 2 5%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 26%
Medicine and Dentistry 8 19%
Nursing and Health Professions 5 12%
Agricultural and Biological Sciences 4 10%
Pharmacology, Toxicology and Pharmaceutical Science 3 7%
Other 3 7%
Unknown 8 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 06 November 2017.
All research outputs
#2,546,322
of 12,104,225 outputs
Outputs from Journal of Translational Medicine
#344
of 2,343 outputs
Outputs of similar age
#63,714
of 271,379 outputs
Outputs of similar age from Journal of Translational Medicine
#12
of 109 outputs
Altmetric has tracked 12,104,225 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,343 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done well, scoring higher than 85% of its peers.
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 271,379 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.