ArticleJournal of bone and mineral metabolism2019
A simple questionnaire for the prediction of vitamin D deficiency in Japanese adults (Vitaimn D Deficiency questionnaire for Japanese: VDDQ-J).
Article in Journal of bone and mineral metabolism, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Prediction models and questionnaires developed to predict vitamin D status in adults: a systematic review.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2020Pooled it
- Article
- Impact of Vitamin D on Surgical Outcomes for Lumbar Spinal Canal Stenosis.International journal of spine surgery · 2026Article
- Vitamin D supplementation improves genu varum in toddlers: two-center pilot study.Journal of bone and mineral metabolism · 2025Article
- Development and validation of prediction model for vitamin D deficiency in Chinese college students (a dynamic online nomogram predicting vitamin D deficiency for Chinese college students).Journal of health, population, and nutrition · 2025Article
- Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing.PeerJ. Computer science · 2025Article
- Review
- Simple prediction model for vitamin D deficiency in women with osteoporosis or risk factors for osteoporosis in Thailand.Journal of clinical & translational endocrinology · 2024Article
- Development of a predictive scoring system for vitamin D deficiency 'Vitamin D Deficiency Predicting Scoring (ViDDPreS)' based on the vitamin D status in young Japanese women: a nationwide cross-sectional study.Public health nutrition · 2024Article
- Observational
- Forecasting levels of serum 25-hydroxyvitamin D based on dietary intake, lifestyle and personal determinants in a sample of Southern Europeans.The British journal of nutrition · 2023Article
- Machine learning approach for the detection of vitamin D level: a comparative study.BMC medical informatics and decision making · 2023Article
- Effect of vitamin D deficiency on surgical outcomes of degenerative cervical myelopathy.North American Spine Society journal · 2023Article
- Score-based prediction model for severe vitamin D deficiency in patients with critical illness: development and validation.Critical care (London, England) · 2022Article
- Development of a Short Questionnaire for the Screening for Vitamin D Deficiency in Italian Adults: The EVIDENCe-Q Project.Nutrients · 2022Article
- Development of a predictive model for vitamin D deficiency based on the vitamin D status in young Japanese women: A study protocol.PloS one · 2022Article
- Development and Validation of a Predictive Model of Hypovitaminosis D in General Adult Population: SCOPYD Study.Nutrients · 2021Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vitamin D deficiency (VDD) is associated with an increased risk of various diseases. Serum 25-hydroxyvitamin D [25(OH)D] concentration is the best marker for vitamin D status and its concentration < 20 ng/mL indicates VDD. However, its measurement is not easily applicable for the evaluation of vitamin D status in the general population because of its cost. Therefore, we aimed to develop a simple questionnaire for easily identifying the risk of VDD. From the total sample (649 healthy subjects aged 19-70 years), 434 and 215 subjects were randomly assigned to the derivation and the validation cohort, respectively. Prediction model for VDD was developed by backward logistic regression analysis. The regression β coefficients of the significant predictors were transformed into integral numbers and used for the individual score. These individual scores were summed to calculate the total risk score (VDD questionnaire for Japanese score: VDDQ-J score). VDD was present in 54.1% of the total subjects. The model for the prediction of VDD consisted of 7 predictors. Areas under the curve were 0.78 and 0.75 in the data set of internal validation and of the external validation, respectively. The cutoff value was determined to be 31 points (range 0-54) with the sensitivity/specificity and positive predictive value/negative predictive value of 61%/79%, and 81%/57%, respectively. Our VDDQ-J score is easy to answer by the wide range of subjects, and well predicts VDD. This risk score would be useful to identify subjects at risk for VDD both in clinical and epidemiological settings.
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