Vitamin D (VD) is involved in a wide variety of physiological processes. The high prevalence of VD deficiency in the population requires stronger preventive measures.

Characterize the dietary and lifestyle determinants of VD levels in blood and of VD deficiency to further develop predictive models of these two outcomes.

63,759 participants from the UK Biobank study with available data on dietary intake of VD, assessed via 24-hour recalls, and with measurements of serum 25(OH)D levels. Linear and logistic regression models were applied to identify factors associated with VD levels and VD deficiency outcomes, and to evaluate the influence of covariates on the association between VD in serum and VD in the diet. Predictive models were constructed using classical regression models and machine learning methods based on penalized likelihood methods.

Approximately 10% of the participants had VD deficiency (VD < 25 nmol/L), and 38.9% were at risk of VD inadequacy (VD 25-49 nmol/L). The dietary intake of VD was significantly lower in the VD deficient group. This latter group showed lower engagement in physical activity (22.1%) compared to the non-deficient group (13.4%; p < 0.001). Also, overweight and obesity (vs normal weight) were related to a greater likelihood of VD deficiency (OR = 1.18 and 1.96, respectively). A similar odds of VD deficiency was observed for abdominal obesity (OR = 1.83). A weaker association was observed between dietary VD intake, based on participant reports, and VD levels. With regard to sunlight exposure, darker skin tones (OR dark vs fair skin = 3.11), season (OR winter vs autumn = 3.76) and less outdoor time activities (OR per 1 hour increase = 0.96) were also related to VD deficiency. Predictive models for both classical regression and machine learning, showed good accuracy (AUC = 0.8-0.9 for VD deficiency).

While a rich diet in VD boosts its levels, sun exposure plays a more significant role particularly in populations from the UK or Northern Europe. A predictive model including key determinants could effectively assess VD deficiency.