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Predicting vitamin D deficiency in older Australian adults

Citation

Tran, B and Armstrong, BK and McGeechan, K and Ebeling, PR and English, DR and Kimlin, MG and Lucas, R and van der Pols, JC and Venn, A and Gebski, V and Whiteman, DC and Webb, PM and Neale, RE, Predicting vitamin D deficiency in older Australian adults, Clinical Endocrinology, 79, (5) pp. 631-640. ISSN 0300-0664 (2013) [Refereed Article]

Copyright Statement

Copyright 2013 John Wiley

DOI: doi:10.1111/cen.12203

Abstract

Objective 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. Design and Participants 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. Measurements 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. Results 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%. Conclusion Our model could predict vitamin D deficiency with reasonable accuracy, but it needs to be validated in other populations before being implemented. © 2013 John Wiley & Sons Ltd.

Item Details

Item Type:Refereed Article
Research Division:Medical and Health Sciences
Research Group:Public Health and Health Services
Research Field:Epidemiology
Objective Division:Health
Objective Group:Public Health (excl. Specific Population Health)
Objective Field:Preventive Medicine
Author:Venn, A (Professor Alison Venn)
ID Code:87382
Year Published:2013
Web of Science® Times Cited:16
Deposited By:Menzies Institute for Medical Research
Deposited On:2013-11-14
Last Modified:2017-11-02
Downloads:0

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