Predicting vitamin D deficiency in older Australian adults. (13th April 2013)

• Record Type: Journal Article
• Authors: Tran, Bich; Armstrong, Bruce K.; McGeechan, Kevin; Ebeling, Peter R.; English, Dallas R.; Kimlin, Michael G.; Lucas, Robyn; van der, Jolieke C.; Venn, Alison; Gebski, Val; Whiteman, David C.; Webb, Penelope M.; Neale, Rachel E.
• Is Part Of: Clinical endocrinology. Volume 79:Number 5(2013:Nov.)
• Page Start: 631
• Page End: 640
• Publication Date: 2013-04-13
• Subjects: Endocrinology -- Periodicals; 616.4005
• DOI: 10.1111/cen.12203
• Languages: English
• ISSNs: 0300-0664

Abstract

<abstract abstract-type="main" xml:lang="en" id="cen12203-abs-0001"> <title>Summary</title> <sec id="cen12203-sec-0001" sec-type="section"> <title>Objective</title> <p>There has been a dramatic increase in vitamin D testing in Australia in recent years, prompting calls for targeted testing. We sought to develop a model to identify people most at risk of vitamin D deficiency.</p> </sec> <sec id="cen12203-sec-0002" sec-type="section"> <title>Design and Participants</title> <p>This is a cross‐sectional study of 644 60‐ to 84‐year‐old participants, 95% of whom were Caucasian, who took part in a pilot randomized controlled trial of vitamin D supplementation.</p> </sec> <sec id="cen12203-sec-0003" sec-type="section"> <title>Measurements</title> <p>Baseline 25(OH)D was measured using the Diasorin Liaison platform. Vitamin D insufficiency and deficiency were defined using 50 and 25 nmol/l as cut‐points, respectively. A questionnaire was used to obtain information on demographic characteristics and lifestyle factors. We used multivariate logistic regression to predict low vitamin D and calculated the net benefit of using the model compared with 'test‐all' and 'test‐none' strategies.</p> </sec> <sec id="cen12203-sec-0004" sec-type="section"> <title>Results</title> <p>The mean serum 25(OH)D was 42 (SD 14) nmol/1. Seventy‐five per cent of participants were vitamin D insufficient and 10% deficient. Serum 25(OH)D was positively correlated with time outdoors, physical activity, vitamin D intake and ambient UVR, and inversely correlated with age, BMI and poor self‐reported health status. These predictors explained approximately 21% of the variance in serum 25(OH)D. The area under the ROC curve predicting vitamin D deficiency was 0·82. Net benefit for the prediction model was higher than that for the 'test‐all' strategy at all probability thresholds and higher than the 'test‐none' strategy for probabilities up to 60%.</p> </sec> <sec id="cen12203-sec-0005" sec-type="section"> <title>Conclusion</title> <p>Our model could predict vitamin D deficiency with reasonable accuracy, but it needs to be validated in other populations before being implemented.</p> </sec> </abstract>