Prediction of insufficient serum vitamin D status in older women: a validated model

Abstract

SummaryWe developed an externally validated simple prediction model to predict serum 25(OH)D levels < 30, < 40, < 50 and 60 nmol/L in older women with risk factors for fractures. The benefit of the model reduces when a higher 25(OH)D threshold is chosen.IntroductionVitamin D deficiency is associated with increased fracture risk in older persons. General supplementation of all older women with vitamin D could cause medicalization and costs. We developed a clinical model to identify insufficient serum 25-hydroxyvitamin D (25(OH)D) status in older women at risk for fractures.MethodsIn a sample of 2689 women ≥ 65 years selected from general practices, with at least one risk factor for fractures, a questionnaire was administered and serum 25(OH)D was measured. Multivariable logistic regression models with backward selection were developed to select predictors for insufficient serum 25(OH)D status, using separate thresholds 30, 40, 50 and 60 nmol/L. Internal and external model validations were performed.ResultsPredictors in the models were as follows: age, BMI, vitamin D supplementation, multivitamin supplementation, calcium supplementation, daily use of margarine, fatty fish ≥ 2×/week, ≥ 1 hours/day outdoors in summer, season of blood sampling, the use of a walking aid and smoking. The AUC was 0.77 for the model using a 30 nmol/L threshold and decreased in the models with higher thresholds to 0.72 for 60 nmol/L. We demonstrate that the model can help to distinguish patients with or without insufficient serum 25(OH)D levels at thresholds of 30 and 40 nmol/L, but not when a threshold of 50 nmol/L is demanded.ConclusionsThis externally validated model can predict the presence of vitamin D insufficiency in women at risk for fractures. The potential clinical benefit of this tool is highly dependent of the chosen 25(OH)D threshold and decreases when a higher threshold is used.

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

“…The unexplained remaining variability can be attributed to a variety of factors such as usual memory biases and errors regarding lifetime exposure to risk factors, variability in serum 25(OH)D measurements, or other unknown or unmeasurable factors such as genetic factors [ 41 , 42 , 43 ]. Nevertheless, the performance of our model was comparable to that of previously published models regarding AUCs [ 10 , 12 , 17 , 36 , 39 ], R 2 [ 10 , 12 , 18 , 36 , 44 ], and sensitivity and specificity [ 16 , 17 , 19 ]. Furthermore, it has the advantage of relying on parameters that can be easily and rapidly obtained in routine care.…”

“…These predictors had various association strengths with the vitamin D concentration: the month of blood sampling was the strongest predictor, illustrating the seasonality of the vitamin D concentration, the lowest concentrations were observed at the end of winter in March, and the highest concentrations were observed in August, which is consistent with a 2008 study by Holick et al [ 35 ]. In most published predictive models, seasonality was taken into account using four modalities corresponding to the four seasons [ 10 , 12 , 17 , 18 , 19 ]. In our model, we accounted for the cyclic shape of the seasonality, which allowed a more accurate adjustment of the regression model for the month of blood sampling.…”

“…In addition, individual sun exposure has been measured in various ways regarding intensity, chronology, and surface exposed. With these limitations, these models have suggested that the lengths of time spent under the sunlight, outdoor, or tanning in the past 12 months were indeed significant contributors to the predictive ability [ 10 , 15 , 16 , 17 , 19 , 36 ]. To find the best measure of individual sun exposure, a section was integrated herein with detailed and various questions about sun exposure habits, actual exposure in the preceding year, and recent exposure in the preceding week.…”

“…Old and oldest individuals (aged 65 years and more) are at a higher risk of vitamin D deficiency, and studies conducted on elderly patients have found that older age was associated with a higher prevalence of hypovitaminosis D [ 10 , 12 , 36 ]. However, most studies conducted in younger individuals have either found no association [ 15 , 18 , 19 ] or even a reverse association [ 17 ], and a non-linear association was found herein.…”

“…A large number of studies have been conducted to identify the risk factors of vitamin D deficiency and try to develop clinical scores. However, most studies have addressed this question only in specific sub-groups of the population such as post-menopausal women [ 7 , 8 , 9 , 10 ], older adults [ 11 , 12 , 13 ], or pregnant women [ 14 ], in restricted geographical areas [ 15 , 16 , 17 ], or without considering critical risk factors such as sun exposure [ 18 , 19 ] or other known factors of hypovitaminosis D [ 15 , 20 , 21 ]. Furthermore, studies that have addressed individual sun exposure have measured it incompletely or without precision regarding the surface exposed.…”