Development and Validation of a Predictive Model of Hypovitaminosis D in General Adult Population: SCOPYD Study

Abstract

The worldwide global increase in serum 25-hydroxyvitamin D (25(OH)D) measurements has led some countries to restrict reimbursement for certain clinical situations only. Another approach could consist in providing physicians with screening tools in order to better target blood test prescription. The objective of the SCOPYD study was to identify the best combination of predictors of serum VitD concentration among adults aged 18–70 years. Potential risk factors for VitD deficiency were collected using a comprehensive self-administered questionnaire. A multivariable linear regression was used to build a predictive model of serum 25(OH)D concentration. Among 2488 participants, 1080 (43.4%) had VitD deficiency (<50 nmol/L) and 195 (7.8%) had severe deficiency (<25 nmol/L). The final model included sunlight exposure in the preceding week and during the last holidays, month of blood sampling, age, sex, body mass index, skin phototype, employment, smoking, sport practice, latitude, and VitD supplementation in preceding year. The area under the curve was 0.82 (95% CI (0.78; 0.85)) for severe deficiency. The model predicted severe deficiency with a sensitivity of 77.9% (95% CI (69.1; 85.7)) and a specificity of 68.3% (95% CI (64.8; 71.9)). We identified a set of predictors of severe VitD deficiency that are easy to collect in routine that may help to better target patients for serum 25(OH)D concentration determination.

Prevention of Recurrent Benign Paroxysmal Positional Vertigo: The Role of Combined Supplementation with Vitamin D and Antioxidants

“…The production of vitamin D3 in the skin depends on sunshine exposure, latitude, and skin pigmentation, and 25 (OH) D is lower with higher latitudes and darker skin types, although there are some exceptions. Hypovitaminosis D is a widespread condition in the general population, and its prevalence is influenced by age and sex [ 28 , 29 , 30 ]. A large-scale study in the United Kingdom, including subjects from 18 to 85 y.o.…”

Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters

“…In a study conducted with young Italian females, Adami et al [15] found that the main determinants of vitamin D deficiency were an increase in BMI and exposure to sunlight. In a more recent study performed to identify the best combination of predictors for the serum concentration of vitamin D in adults aged between 18 and 70 years old, the multivariate linear regression model included age, sex, BMI, sunlight exposure in the previous week and during the month of blood sample collection, skin phototype, job position, smoking status, physical activity, latitude, and administration of vitamin D supplements in the previous year [63]. However, these results differed from some of our results, in that BMI was not found to be significant in females; we considered that these differences could be due to differences in age ranges and ethnicities.…”