Evidence map›Paper›PMID 40284242›Full record

ArticleNutrients2025

Influence of Genetic Polymorphisms and Biochemical Biomarkers on Response to Nutritional Iron Supplementation and Performance in a Professional Football Team: A Pilot Longitudinal Study.

David Varillas-Delgado

Abstract read
In one paragraph

Article in Nutrients, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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  3. Article
  4. Review
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  6. 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

1 author.

David Varillas-DelgadoExercise and Sport Science, Faculty of Health Sciences, Universidad Francisco de Vitoria, 28223 Pozuelo, Spain.ORCID 0000-0001-5026-2701

Funding

MAPFRE Foundation, Ignacio H. de Larramendi 6391
6 · The paper itself

Abstract

backgroundIron deficiency is a prevalent issue among elite athletes, particularly in endurance-based sports like football, where optimal iron status is crucial for aerobic capacity and performance. Despite the well-documented role of iron in oxygen transport and energy metabolism, the interplay between genetic polymorphisms, biochemical markers, and iron supplementation remains poorly understood. This study aimed to investigate the relationship between genetic polymorphisms and iron status in professional football players, assess the impact of iron supplementation on athletic performance, and develop a predictive model for iron supplementation based on genetic and biochemical profiles.

methodsA longitudinal study was conducted over three seasons (2021-2024) with 48 male professional football players. Participants underwent genotyping for polymorphisms in

resultsPlayers with "optimal" genotypes (

conclusionsGenetic profile combined with biochemical monitoring effectively predicts iron supplementation needs in athletes. Personalized nutrition strategies, guided by TGS, can optimize iron status and enhance performance in elite football players. This approach bridges a critical gap in sports science, offering a framework for precision nutrition in athletics.

Indexed as

Athletic PerformanceDietary SupplementsIronPolymorphism, GeneticSoccerActininAdultAMP DeaminaseAthletesBiomarkersFerritinsGenotypeHemochromatosis ProteinHemoglobinsHumansLongitudinal StudiesACE protein, humanActininACTN3 protein, humanAMPD1 protein, humanAMP DeaminaseBiomarkersFerritinsHemochromatosis ProteinHemoglobinsHFE protein, humanIronPeptidyl-Dipeptidase Afootballgenetic profileiron supplementationperformancepersonalized nutrition

Identifiers

PMID40284242
PMCPMC12030593

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LicenceCC BY
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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.