Frontiers in genetics · 2026
Genetic and non-genetic determinants of vitamin D status: a polygenic score analysis in elite athletes.
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Hacker Sebastian, Reichert Lukas, Lenz Claudia, Henne Sabrina, David Friederike, Heilmann-Heimbach Stefanie, Zentgraf Karen, Krüger Karsten
Аннотация
INTRODUCTION: Vitamin D plays a critical role in bone metabolism, immune regulation, and muscle performance. Serum 25-hydroxyvitamin D [25(OH)D] levels are shaped by both genetic and non-genetic determinants. To explore their combined role, we investigated the association between a polygenic score (PGS) for vitamin D metabolism and serum 25(OH)D concentrations in elite athletes.
METHODS: Serum vitamin D status was measured in 473 German national squad athletes. PRSice-2 was used for PGS development. Linear regression models compared the predictive utility of an overall model (PGS plus covariates) versus a null model (covariates only). Covariates included age, sex, competition environment, ambient UVB dose, and supplementation.
RESULTS: Serum 25(OH)D was significantly predicted by the standardized PGS (β = 4.04), age (β = 8.59), supplementation (β = 20.86), and cumulative weighted UVB dose (cw-D-UVB, β = 7.18; all p < 0.05). The full model explained 31.7% (12.1%) of variance (adjusted R2 = 0.235), with the PGS contributing an incremental 1% (1%). Model performance yielded a mean absolute error of 18.64 nmol/L (3.18 nmol/L) and root mean squared error of 25.27 nmol/L (4.24 nmol/L). No significant interactions were found for PGS × cw- D-UVB, PGS × supplementation status and PGS × competition environment.
DISCUSSION: The findings indicate that genetic determinants account for a small proportion of the total variance in vitamin D status. Stronger predictors are non-genetic determinants such as supplementation, UVB exposure, and age, highlighting the predominant role of environmental influences. Given the limited explanatory power of the PGS, individualized management strategies should primarily focus on modifiable factors such as sun exposure and supplementation.
METHODS: Serum vitamin D status was measured in 473 German national squad athletes. PRSice-2 was used for PGS development. Linear regression models compared the predictive utility of an overall model (PGS plus covariates) versus a null model (covariates only). Covariates included age, sex, competition environment, ambient UVB dose, and supplementation.
RESULTS: Serum 25(OH)D was significantly predicted by the standardized PGS (β = 4.04), age (β = 8.59), supplementation (β = 20.86), and cumulative weighted UVB dose (cw-D-UVB, β = 7.18; all p < 0.05). The full model explained 31.7% (12.1%) of variance (adjusted R2 = 0.235), with the PGS contributing an incremental 1% (1%). Model performance yielded a mean absolute error of 18.64 nmol/L (3.18 nmol/L) and root mean squared error of 25.27 nmol/L (4.24 nmol/L). No significant interactions were found for PGS × cw- D-UVB, PGS × supplementation status and PGS × competition environment.
DISCUSSION: The findings indicate that genetic determinants account for a small proportion of the total variance in vitamin D status. Stronger predictors are non-genetic determinants such as supplementation, UVB exposure, and age, highlighting the predominant role of environmental influences. Given the limited explanatory power of the PGS, individualized management strategies should primarily focus on modifiable factors such as sun exposure and supplementation.