Alcalá-Santiago Ángela, García-Villanova Belén, Ruíz-López María Dolores, Gil Ángel, Rodriguez-Barranco Miguel, Sánchez Maria José, Molina-Montes Esther
Department of Nutrition and Food Science, Faculty of Pharmacy, University of Granada, Granada, Spain; Instituto de Investigación Biosanitaria ibs. GRANADA, Granada, Spain; Institute of Nutrition and Food Technology (INYTA) 'José Mataix', Biomedical Research Centre, University of Granada, Avenida del Conocimiento s/n, Granada, Spain.
Department of Nutrition and Food Science, Faculty of Pharmacy, University of Granada, Granada, Spain.
J Nutr Biochem. 2025 Aug;142:109919. doi: 10.1016/j.jnutbio.2025.109919. Epub 2025 Apr 11.
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. The aim was to characterize the dietary and lifestyle determinants of VD levels in blood and of VD deficiency to further develop predictive models of these two outcomes. A total of 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 were included. 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 for both VD outcomes 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<.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 h 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). In conclusion, 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.
维生素D(VD)参与多种生理过程。人群中VD缺乏的高患病率需要更强有力的预防措施。目的是确定血液中VD水平和VD缺乏的饮食及生活方式决定因素,以进一步建立这两种结果的预测模型。纳入了英国生物银行研究中的63759名参与者,他们有通过24小时回忆法评估的VD饮食摄入量数据,以及血清25(OH)D水平的测量值。应用线性和逻辑回归模型来识别与VD水平和VD缺乏结果相关的因素,并评估协变量对血清VD与饮食中VD之间关联的影响。使用经典回归模型和基于惩罚似然法的机器学习方法构建了两种VD结果的预测模型。约10%的参与者存在VD缺乏(VD<25nmol/L),38.9%有VD不足风险(VD 25 - 49nmol/L)。VD缺乏组的VD饮食摄入量显著更低。与非缺乏组(13.4%;P<0.001)相比,后一组的身体活动参与度更低(22.1%)。此外,超重和肥胖(与正常体重相比)与VD缺乏的可能性更大相关(OR分别为1.18和1.96)。腹部肥胖的VD缺乏几率相似(OR = 1.83)。基于参与者报告的饮食VD摄入量与VD水平之间的关联较弱。关于阳光照射,肤色较深(OR深肤色与浅肤色 = 3.11)、季节(OR冬季与秋季 = 3.76)以及户外时间活动较少(每增加1小时OR = 0.96)也与VD缺乏有关。经典回归和机器学习的预测模型均显示出良好的准确性(VD缺乏的AUC = 0.8 - 0.9)。总之,虽然富含VD的饮食会提高其水平,但阳光照射起着更重要的作用,特别是在来自英国或北欧的人群中。包含关键决定因素的预测模型可以有效评估VD缺乏。