Prediction of dyslipidemia using gene mutations, family history of diseases and anthropometric indicators in children and adolescents: The CASPIAN-III study

Marateb, Hamid Reza; Mohebian, Mohammad Reza; Javanmard, Shaghayegh Haghjooy; Tavallaei, Amir Ali; Tajadini, Mohamadhasan; Heidari‐Beni, Motahar; Mañanas, Miguel Ángel; Motlagh, Mohammad Esmaeil; Heshmat, Ramin; Mansourian, Marjan; Kelishadi, Roya

doi:10.1016/j.csbj.2018.02.009

Cited by 21 publications

(14 citation statements)

References 85 publications

(101 reference statements)

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“…The differences may well be due to the effects of geographical and ethnic dissimilarities between populations of the studies. In conformity with our finding of dyslipidemia, an Iranian study which evaluated 725 children and adolescents demonstrated the relationship of LPL Hind III [rs320] and abnormal lipid levels ( Marateb et al, 2018 ).…”

Section: Discussionsupporting

confidence: 87%

Association between HindIII (rs320) variant in the lipoprotein lipase gene and the presence of coronary artery disease and stroke among the Saudi population

Bogari

Aljohani

Dannoun

et al. 2020

Saudi Journal of Biological Sciences

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Lipoprotein Lipase (LPL) is known to be a key enzyme for lipid metabolism specifically in an enzymatic glycoprotein which provide tissues without fatty-acids and eliminates triglycerides (TG) by the circulation. Mutations in LPL were proven to cause alteration in fractions within lipoprotein, causing the development of atherosclerosis which predispose to weakening coronary artery disease (CAD) and stroke. We examined the linkage between genetic variant Hind III in LPL on lipoprotein fractions, stroke occurrences and CAD. In this case-control study, we have recruited 315 CAD cases and 205 age-matched controls. A total of 520 genomic DNA was digested with the purified PCR products for restriction fragment length polymorphism with Hind III restriction enzyme. The distribution of genotypes in a decreasing order were TT, 148 (47%), GT 135 (42.9%) and GG 32 (10.2%) in CAD groups of the study while the pattern in controls were GT 91 (44.4%), TT 86 (42%) and GG 28 (13.7%). None of all the allele or genotype frequencies were found to be significant in our study (p greater than 0.05), while the biochemical levels for both TG and LDL-c were shown to be prone in CAD patients when compare with the controls. Furthermore, the occurence of strokes were more in CAD groups vs. controls: 72 (22.9%) vs. 7 (3.4%) [p 0.000]. This could indicate the influence of Hind III variant on plasma lipid levels, and the possibility of considering it a risk factor for atherosclerosis leading to CAD and stroke occurrence.

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Section: Discussionsupporting

confidence: 87%

Association between HindIII (rs320) variant in the lipoprotein lipase gene and the presence of coronary artery disease and stroke among the Saudi population

Bogari

Aljohani

Dannoun

et al. 2020

Saudi Journal of Biological Sciences

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“…Moreover, a medical diagnosis system is clinically reliable if Se ≥ 80%, Sp ≥ 95% [ 83 ], PPV ≥ 95% [ 46 ], and DOR ≥ 100 [ 84 ]. The proposed diagnosis system fulfilled three conditions.…”

Section: Resultsmentioning

confidence: 99%

Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region

et al. 2020

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Suicide is one of the most critical public health concerns in the world and the second cause of death among young people in many countries. However, to date, no study can diagnose suicide ideation/behavior among university students in the Middle East and North Africa (MENA) region using a machine learning approach. Therefore, stability feature selection and stacked ensembled decision trees were employed in this classification problem. A total of 573 university students responded to a battery of questionnaires. Three-fold cross-validation with a variety of performance indices was sued. The proposed diagnostic system had excellent balanced diagnosis accuracy (AUC = 0.90 [CI 95%: 0.86–0.93]) with a high correlation between predicted and observed class labels, fair discriminant power, and excellent class labeling agreement rate. Results showed that 23 items out of all items could accurately diagnose suicide ideation/behavior. These items were psychological problems and how to experience trauma, from the demographic variables, nine items from Post-Traumatic Stress Disorder Checklist (PCL-5), two items from Post Traumatic Growth (PTG), two items from the Patient Health Questionnaire (PHQ), six items from the Positive Mental Health (PMH) questionnaire, and one item related to social support. Such features could be used as a screening tool to identify young adults who are at risk of suicide ideation/behavior.

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“…Several studies have identified individual risk factors of dyslipidemia and developed risk models depending on the questionnaire including dietary, lifestyle, anthropometric data and genetic markers 7–10. However, researches on dyslipidemia prediction were mainly based on cross-sectional studies for disease diagnosis and screening.…”

Section: Introductionmentioning

confidence: 99%

<p>Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model</p>

Zhang¹,

Tang²,

Ji³

et al. 2019

CLEP

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ObjectiveDyslipidemia has been recognized as a major risk factor of several diseases, and early prevention and management of dyslipidemia is effective in the primary prevention of cardiovascular events. The present study aims to develop risk models for predicting dyslipidemia using Random Survival Forest (RSF), which take the complex relationship between the variables into account.MethodsWe used data from 6328 participants aged between 19 and 90 years free of dyslipidemia at baseline with a maximum follow-up of 5 years. RSF was applied to develop gender-specific risk model for predicting dyslipidemia using variables from anthropometric and laboratory test in the cohort. Cox regression was also adopted in comparison with the RSF model, and Harrell’s concordance statistic with 10-fold cross-validation was used to validate the models.ResultsThe incidence density of dyslipidemia was 101/1000 in total and subgroup incidence densities were 121/1000 for men and 69/1000 for women. Twenty-four predictors were identified in the prediction model of males and 23 in females. The C-statistics of the prediction models for males and females were 0.731 and 0.801, respectively. The RSF model shows better discriminative performance than CPH model (0.719 for males and 0.787 for females). Moreover, some predictors were observed to have a nonlinear effect on dyslipidemia.ConclusionThe RSF model is a promising method in identifying high-risk individuals for the prevention of dyslipidemia and related diseases.

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Prediction of dyslipidemia using gene mutations, family history of diseases and anthropometric indicators in children and adolescents: The CASPIAN-III study

Cited by 21 publications

References 85 publications

Association between HindIII (rs320) variant in the lipoprotein lipase gene and the presence of coronary artery disease and stroke among the Saudi population

Association between HindIII (rs320) variant in the lipoprotein lipase gene and the presence of coronary artery disease and stroke among the Saudi population

Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region

<p>Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model</p>

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