The comparison of insulin and uric acid levels in adolescents with and without metabolic syndrome

Rashidi, Homeira; Shahbazian, Hajieh; Nokhostin, Forogh; Latifi, Seyed Mahmoud; Jafarizade, Mehrian

doi:10.1007/s11515-018-1515-1

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…UA and ALT were found to be risk factors for MetS, and related studies showed that UA increased the risk of MetS by increasing insulin resistance, and increased ALT in the blood might cause fat accumulation in the liver. Through investigation, Mandana Khalili et al found that patients with MetS had higher hepatic steatosis level, and there was a correlation between the elevation of ALT and MetS [ 30 , 31 ].Different from the general population, oil workers have been in a special occupational environment for a long time. High temperature environment causes the body’s circulatory system to be in a long-term stress state, resulting in decreased elasticity of blood vessel wall, increased blood viscosity, and increased blood pressure.…”

Section: Discussionmentioning

confidence: 99%

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Wang

et al. 2020

BMC Public Health

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Background The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people’s health. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome. Methods Design existing circumstances research. A total of 1468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models. Results The results showed that the accuracy of the three models was 82.49,95.98 and 92.03%, the sensitivity was 87.94,95.52 and 90.59%, the specificity was 74.54, 96.65 and 94.14%, the F1 score was 0.86,0.97 and 0.93, and the area under ROC curve was 0.88,0.96 and 0.92, respectively. The Brier score of the three models was 0.15, 0.08 and 0.12, Observed-expected ratio was 0.83, 0.97 and 1.13, and the Integrated Calibration Index was 0.075,0.073 and 0.074, respectively, and explained how the random forest model was used for individual disease risk score. Conclusions The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

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Section: Discussionmentioning

confidence: 99%

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Wang

et al. 2020

BMC Public Health

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“…Irregular shift work will affect the biological rhythm of human body due to irregular circadian rhythm, resulting in the disturbance of nutrients and related hormones in the body, thus resulting in glucose and lipid metabolism disorder and energy imbalance [31].On the other hand, the workers of night shift work lack of sleep time, and the incidence of unhealthy lifestyle such as smoking, drinking and irregular diet increases greatly, which are the driving forces for the occurrence of metabolic syndrome [32].In this study, UA and ALT were found to be risk factors for MetS, and related studies showed that UA increased the risk of MetS by increasing insulin resistance, and increased ALT in the blood might cause fat accumulation in the liver. Through investigation, Mandana Khalili et al found that patients with MetS had higher hepatic steatosis level, and there was a correlation between the elevation of ALT and MetS [33][34].…”

Section: Discussionmentioning

confidence: 99%

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Wang

et al. 2020

Preprint

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Abstract Background.The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Objective.To develop and internally verify three risk prediction models for the metabolic syndrome of petroleum workers, compare the prediction performance of the three models, and find the optimal model.Methods. Design existing circumstances research. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results.The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, the area under the ROC curve was 0.894, 0.987 and 0.935, and the Integrated Calibration Index was 0.074, 0.071 and 0.078, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, the area under the ROC curve was 0.797, 0.861 and 0.855, and the Integrated Calibration Index was 0.064, 0.051 and 0.096, respectively.Conclusions.The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

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“…Irregular shift work will affect the biological rhythm of human body due to irregular circadian rhythm, resulting in the disturbance of nutrients and related hormones in the body, thus resulting in glucose and lipid metabolism disorder and energy imbalance [30].On the other hand, the workers of night shift work lack of sleep time, and the incidence of unhealthy lifestyle such as smoking, drinking and irregular diet increases greatly, which are the driving forces for the occurrence of metabolic syndrome [31].In this study, UA and ALT were found to be risk factors for MetS, and related studies showed that UA increased the risk of MetS by increasing insulin resistance, and increased ALT in the blood might cause fat accumulation in the liver. Through investigation, Mandana Khalili et al found that patients with MetS had higher hepatic steatosis level, and there was a correlation between the elevation of ALT and MetS [32][33].…”

Section: Comparison Of Predictive Performance Of Metabolic Syndrome Rmentioning

confidence: 99%

Application of Data Mining Technology in Risk Prediction of Metabolic Syndrome in Oil Workers

Wang¹,

Li²,

Li³

et al. 2020

Preprint

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Background. The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health.In this paper, three kinds of risk prediction models applicable to the metabolic syndrome of oil workers were established, and the optimal models were found through comparison. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Methods. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results. The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, and the area under the ROC curve was 0.894, 0.987 and 0.935, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, and the area under the ROC curve was 0.797, 0.861 and 0.855, respectively.Conclusions. The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

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The comparison of insulin and uric acid levels in adolescents with and without metabolic syndrome

Cited by 3 publications

References 24 publications

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Development and internal validation of risk prediction model of metabolic syndrome in oil workers

Application of Data Mining Technology in Risk Prediction of Metabolic Syndrome in Oil Workers

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