A simple questionnaire for the prediction of vitamin D deficiency in Japanese adults (Vitaimn D Deficiency questionnaire for Japanese: VDDQ-J)

Kuwabara, Akiko; Tsugawa, Naoko; Mizuno, Kei; Ogasawara, Honami; Watanabe, Yasuyoshi; Tanaka, Kiyoshi

doi:10.1007/s00774-018-0984-2

Cited by 22 publications

(29 citation statements)

References 29 publications

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“…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. Sex, as aging, has not been consistently associated with vitamin D concentration in the literature.…”

Section: Discussioncontrasting

confidence: 55%

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussioncontrasting

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…They have used the Pearson correlation coefficient to predict the correlation between the actual and predicted serum values. Akiko Kuwabara et al [11], used a list of questionnaires to predict vitamin d deficiency severity. The prediction model was developed by using logistic regression.…”

Section: Related Workmentioning

confidence: 99%

A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods

2020

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Vitamin D Deficiency (VDD) is one of the most significant global health problem and there is a strong demand for the prediction of its severity using non-invasive methods. The primary data containing serum Vitamin D levels were collected from a total of 3044 college students between 18-21 years of age. The independent parameters like age, sex, weight, height, body mass index (BMI), waist circumference, body fat, bone mass, exercise, sunlight exposure, and milk consumption were used for prediction of VDD. The study aims to compare and evaluate different machine learning models in the prediction of severity in VDD. The objectives of our approach are to apply various powerful machine learning algorithms in prediction and evaluate the results with different performance measures like Precision, Recall, F1-measure, Accuracy, and Area under the curve of receiver operating characteristic (ROC). The McNemar's test was conducted to validate the empirical results which is a statistical test. The final objective is to identify the best machine learning classifier in the prediction of the severity of VDD. The most popular and powerful machine learning classifiers like K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), AdaBoost (AB), Bagging Classifier (BC), ExtraTrees (ET), Stochastic Gradient Descent (SGD), Gradient Boosting (GB), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) were implemented to predict the severity of VDD. The final experimentation results showed that the Random Forest Classifier achieves better accuracy of 96 % and outperforms well on training and testing Vitamin D dataset. The McNemar's statistical test results support that the RF classifier outperforms than the other classifiers.

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Prediction models and questionnaires developed to predict vitamin D status in adults: a systematic review

et al. 2020

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A simple questionnaire for the prediction of vitamin D deficiency in Japanese adults (Vitaimn D Deficiency questionnaire for Japanese: VDDQ-J)

Cited by 22 publications

References 29 publications

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

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

A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods

Prediction models and questionnaires developed to predict vitamin D status in adults: a systematic review

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