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

Abstract

Background: Iron 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.

Methods: A longitudinal study was conducted over three seasons (2021-2024) with 48 male professional football players. Participants underwent genotyping for polymorphisms in ACE (rs4646994), ACTN3 (rs1815739), AMPD1 (rs17602729), CKM (rs8111989), HFE (rs1799945), and MLCK (rs2700352, rs28497577). Biochemical markers (ferritin, haemoglobin, haematocrit, serum iron) and performance metrics (GPS-derived data) were monitored. Iron supplementation (105 mg/day ferrous sulphate) was administered to players with ferritin <30 ng/mL. A Total Genotype Score (TGS) was calculated to evaluate genetic predisposition.

Results: Players with "optimal" genotypes (ACE DD, ACTN3 CC, AMPD1 CC, HFE GC) required less iron supplementation (TGS = 51.25 vs. 41.32 a.u.; p = 0.013) and exhibited better performance metrics. Iron supplementation significantly improved haemoglobin and haematocrit in deficient players (p < 0.05). The TGS predicted supplementation need (AUC = 0.711; p = 0.023), with a threshold of 46.42 a.u. (OR = 5.23, 95% CI: 1.336-14.362; p = 0.017 for non-supplemented players). Furthermore, performance data revealed that iron-supplemented players had significantly lower competition time (1128.40 vs. 1972.84 min; p = 0.003), total distance covered (128,129.42 vs. 218,556.64 m; p = 0.005), and high-speed running in the 18-21 km/h (7.58 vs. 10.36 m/min; p = 0.007) and 21-24 km/h (4.43 vs. 6.13 m/min; p = 0.010) speed zones. They also started fewer matches (11.50 vs. 21.59; p < 0.001).

Conclusions: Genetic 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.

Keywords: football; genetic profile; iron supplementation; performance; personalized nutrition.

Figure 1

Receiver Operating Characteristic (ROC) curve summarizing the ability of the Total Genotype Score (TGS) to differentiate potential iron supplementation from non-supplemented players in the profile of selected genetic markers. AUC, area under the curve.

Figure 2

(a) Haemoglobin, (b) haematocrit, (c) serum ferritin, and (d) serum iron during whole season bio-chemistries in participants with and without iron supplementation with different ACE I/D (rs4646994) genotypes. * DD genotype is different from ID genotype for the same biochemistry at p < 0.050. † DD genotype is different from II genotype for the same biochemistry at p < 0.050.

Figure 3

(a) Haemoglobin, (b) haematocrit, (c) serum ferritin, and (d) serum iron during whole season biochemistries in participants with and without iron supplementation with different ACTN3 c.1729C>T (rs1815739) genotypes. * CC genotype is different from CT genotype for the same biochemistry at p < 0.050. † CC genotype is different from TT genotype for the same biochemistry at p < 0.050.

Figure 4

(a) Haemoglobin, (b) haematocrit, (c) serum ferritin, and (d) serum iron during whole season biochemistries in participants with and without iron supplementation with different AMPD1 c.34C>T (rs17602729) genotypes. * CC genotype is different from CT genotype for the same biochemistry at p < 0.050.

Figure 5

(a) Haemoglobin, (b) haematocrit, (c) serum ferritin, and (d) serum iron during whole season biochemistries in participants with and without iron supplementation with different HFE c.187C>G (rs1799945) genotypes. * GC genotype is different from CC genotype for the same biochemistry at p < 0.050.