| 其他摘要 | Background:
Tardive dyskinesia (TD) is a common extrapyramidal symptom that substantially affects many schizophrenia (SCZ) patients who use antipsychotics (AP) for more than three months. Many studies have investigated its relationship with various environmental and biological parameters such as genetics, epigenetics, hormonal, clinical, physiological, and immunological variables. However, previous studies are inconsistent, and only a few studies aim to predict TD development risk using novel ML models, including random forest (RF), neural networks (NN), naive bayes (NB), or support vector machine (SVM) models. Furthermore, its treatment with antioxidant Ginkgo Biloba (EGB) was suggested to improve the severity of TD symptoms. In addition, it is necessary to detect relevant factors that impact treatment response to potential therapeutics such as EGB and APs.
Methods:
In the first section of the study, as a first hypothesis, we investigated the prevalence, clinical associations, and risk factors of TD development in Chinese patients with chronic SCZ. Nine hundred-one Chinese inpatients with SCZ were included. Later, in the second section of the study, within the study's second hypothesis, our goal was to predict TD in this sample. Next, as a third hypothesis, we included only male and smoker patients to predict TD and reduce the confounding factors' impact. In detail, 338 smokers and males with chronic SCZ were recruited from Hebei Rongjun Hospital in Baoding (city of China) to create a random forest (RF) algorithm. Schooler and Kane criteria were used to assess TD. One hundred sixty-five of the patients were diagnosed with TD, while 173 of the patients were not diagnosed with TD. Similar to the sample in Hebei Rongjun Hospital, 74 smokers and male patients were selected from Beijing HuiLongGuan Hospital to validate the RF algorithm. Among them, 14 patients had TD (18.9%), while 60 of the patients did not have TD (81.08%). Next, for the ML model (n=76), the method of RF was used since the ML model could predict TD successfully with over 70% accuracy. Single nucleotide polymorphism (SNP) was analyzed using polymerase chain reaction (PCR)-based methods. In addition, patients were analyzed according to Superoxide Dismutase (SOD) (fifth hypothesis) and AP type categories (sixth hypothesis) to gain insights regarding the most predictive factors using RF, NN, NB, and SVM models.
In the third section, as the first hypothesis, we have investigated EGB treatment's effects on TD occurrence and genetics' role. One hundred fifty patients were recruited from Beijing HuiLongGuan Hospital. Seventy-five patients took a placebo, while the rest received EGB treatment. In addition, we have investigated predictive genetic factors related to EGB treatment as a second hypothesis. Specifically, we have investigated its association with the language subscale of RBANS according to the minimum clinically meaningful difference in language score.
In the third section, as the first hypothesis, we have investigated EGB treatment's effects on TD occurrence and genetics' role. One hundred fifty patients were recruited from Beijing HuiLongGuan Hospital. Seventy-five patients took a placebo, while the rest received EGB treatment. In addition, we have investigated predictive genetic factors related to EGB treatment as a second hypothesis. Specifically, we have investigated its association with the language subscale of RBANS according to the minimum clinically meaningful difference in language score.
According to the third hypothesis, in the Hebei Rongjun Hospital, the RF model demonstrated an accuracy of 88% (sensitivity: 90.6% and specificity: 85.5%). The model's receiver operating characteristics (ROC) curve value was 94%. Moreover, the top 5 predictors were neutrophil, body mass index (BMI), lymphocyte, heart rate (HR), and hemoglobin (HGB), respectively. After testing the same optimized RF model with the same parameters created using the data of Hebei Rongjun Hospital, we have reached an accuracy of 71.6% and a specificity of 86.8%. However, the sensitivity was only 7.1% (Beijing HuiLongGuan Hospital). This hypothesis was tested in an independent sample. Next, the RF model was measured according to the fourth hypothesis; the accuracy was 81.25%, while sensitivity and specificity values were 88.8% and 83%, respectively. The AUC value was more than 80%. The most predictive factors were Prolactin (PRL), TNF-α, Interleukin 2 (IL-2), globin (GLO), and potassium, respectively. The most discriminative genetic feature was Superoxide Dismutase (SOD). In addition, Monoamine oxidase A (MAOA) 272, Brain-Derived Neurotrophic Factor (BDNF) 196, Tumor Necrosis Factor-alpha (TNF-α), Leptin 242, MAOA 309, Catechol-O-Methyltransferase (COMT), IL-6R, Leptin 367, MAOA 351, and BDNF 270 features have followed this trend respectively.
Moreover, in the AP-based model, the NB ML model in the SGA group predicted TD with over 80 % accuracy. However, it did not predict TD in the FGA group with the same accuracy as in the SGA group, possibly because of the lower sample size (n=25). However, in the logistic regression (LR) model, the accuracy was successfully predicted with over 80 % accuracy.
According to the first hypothesis of the third section, EGB treatment of TD results was predicted using a SVM model with genetic variables with an accuracy of 74%. SVM performed best among all ML models. Other ML models also predicted TD with an accuracy of over 70%. In addition, as the second hypothesis of the third section, EGB treatment effectiveness measured by the language subscale of RBANS using SVM and LR was 91%, whereas the sensitivity value was only 0% due to the lack of true negatives because of the unbalanced sample.
Conclusion:
Our findings indicate that TD is a common movement disorder, with specific demographic and clinical variables being risk factors for the development of TD (first section). We found that certain sociodemographic, genetic, treatment-related, hormonal, and clinical variables are essential in predicting TD development risk (over chance level accuracy). In addition, AP-based categorization may improve the accuracy of the TD prediction model (second section).
Additionally, according to the first hypothesis, the treatment response of EGB to TD can be predicted in the third section. Moreover, according to the second hypothesis in this section, cognitive function measured by RBANS language subscale score can be predicted using parameters related to various genetic variables. |
修改评论