Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents

<div><h3>Background</h3><p>Dyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population.</p> <h3>Methods</h3><p>Demographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35–78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve.</p> <h3>Results</h3><p>Some risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, <em>P</em><0.001).</p> <h3>Conclusion</h3><p>The ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data.</p> </div

ROC curves of ANN and LR prediction models in the validation group.

<p>Areas under ROC curves were 0.86 and 0.68 for ANN and LR models, respectively. Area under ROC curve obtained by ANN was superior to that obtained by LR. <b>Abbreviation:</b> ANN, artificial neural network; LR, logistic regression; ROC, receiver operating characteristic.</p

Framework of artificial neural network model for predicting an individual’s risk of dyslipidemia.

<p>The input layer contained 9 neurons. In the hidden layers, the numbers of neuron were 21. The output layer had only one neuron representing the probability of dyslipidemia. <b>Abbreviations:</b> X_A, age; X_G, gender; X_EL, educational level; X_S, smoking; X_HFD, high-fat diet; X_VFI, vegetable and fruit intake; X_FH, family history of dyslipidemia; X_PA, physical activity; X_WC, waist circumference.</p

Comparison of baseline characteristics between the training and validation groups.

<p>Comparison of baseline characteristics between the training and validation groups.</p

Multivariate logistic regression analysis on risk factors of dyslipidemia in the training group.

<p><b>Abbreviations:</b> X_A, age; X_G, gender; X_EL, educational level; X_S, smoking; X_HFD, high-fat diet; X_VFI, vegetable and fruit intake; X_FH, family history of dyslipidemia; X_PA, physical activity; X_WC, waist circumference.</p

Structure and Conformation of the Medium-Sized Chlorophosphazene Rings

Medium-sized cyclic oligomeric phosphazenes [PCl₂N]_<i>m</i> (where <i>m</i> = 5–9) that were prepared from the reaction of PCl₅ and NH₄Cl in refluxing chlorobenzene have been isolated by a combination of sublimation/extraction and column chromatography from the predominant products [PCl₂N]₃ and [PCl₂N]₄. The medium-sized rings [PCl₂N]_<i>m</i> have been characterized by electrospray ionization–mass spectroscopy (ESI-MS), their ³¹P chemical shifts have been reassigned, and their T₁ relaxation times have been obtained. Crystallographic data has been recollected for [PCl₂N]₅, and the crystal structures of [PCl₂N]₆, and [PCl₂N]₈ are reported. Halogen-bonding interactions were observed in all the crystal structures of cyclic [PCl₂N]_<i>m</i> (<i>m</i> = 3–5, 6, 8). The crystal structures of [P­(OPh)₂N]₇ and [P­(OPh)₂N]₈, which are derivatives of the respective [PCl₂N]_<i>m</i>, are also reported. Comparisons of the intermolecular forces and torsion angles of [PCl₂N]₈ and [P­(OPh)₂N]₈ with those of three other octameric rings are described. The comparisons show that chlorophosphazenes should not be considered prototypical, in terms of solid-state structure, because of the strong influence of halogen bonding